#################################################################################
# NIH Biowulf Node Launch
#################################################################################
sinteractive --mem 500g --time 36:00:00 --gres=lscratch:500
#################################################################################
# Load and Launch R
#################################################################################
module load R/3.6.1 Rstudio
rstudio &
#################################################################################
# Initiate Envrinment & Install Dependencies
#################################################################################
library(gplots)
library(stringr)
library(data.table)
require(data.table)
library(Seurat)
library(ggplot2)
library(sctransform)
library(scales)
require(scales)
library(imager)
#################################################################################
# Identify number bins per bulb
#################################################################################
setwd("/data/johnsonko/SideProject/Helen/20200811/Bulb_Images")
# AZ_109_146
working.im <- load.image("AZ109.jpg")
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,"Total_Bulb_Bins_AZ109.txt",row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,"Total_Bulb_Bins_AZ109.txt",row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
# AZ_84_145
working.im <- load.image("/data/johnsonko/SideProject/Helen/Bulb_Images/AZ84.jpg")
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,"Total_Bulb_Bins_AZ84.txt",row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,"Total_Bulb_Bins_AZ84.txt",row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
# AZ_90_242
working.im <- load.image("/data/johnsonko/SideProject/Helen/Bulb_Images/AZ90.jpg")
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,"Total_Bulb_Bins_AZ90.txt",row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,"Total_Bulb_Bins_AZ90.txt",row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
# AZ_99_140
working.im <- load.image("/data/johnsonko/SideProject/Helen/Bulb_Images/AZ99.jpg")
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,"Total_Bulb_Bins_AZ99.txt",row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,"Total_Bulb_Bins_AZ99.txt",row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
# Control_H190_260
working.im <- load.image("/data/johnsonko/SideProject/Helen/Bulb_Images/H190.jpg")
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,"Total_Bulb_Bins_H190.txt",row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,"Total_Bulb_Bins_H190.txt",row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
# NewH250
working.im <- load.image("/data/johnsonko/SideProject/Helen/Bulb_Images/H250.jpg")
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,"Total_Bulb_Bins_H250.txt",row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,"Total_Bulb_Bins_H250.txt",row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
# AZ_251_146
working.im <- load.image("/data/johnsonko/SideProject/Helen/Bulb_Images/H251.jpg")
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,"Total_Bulb_Bins_H251.txt",row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,"Total_Bulb_Bins_H251.txt",row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
# Control_OFB57_194
working.im <- load.image("/data/johnsonko/SideProject/Helen/Bulb_Images/OFB57.jpg")
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,"Total_Bulb_Bins_OFB57.txt",row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,"Total_Bulb_Bins_OFB57.txt",row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
# Control_OFB6A_107
working.im <- load.image("/data/johnsonko/SideProject/Helen/Bulb_Images/OFB6a.jpg")
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,"Total_Bulb_Bins_OFB6a.txt",row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,"Total_Bulb_Bins_OFB6a.txt",row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
# PD_52_247
working.im <- load.image("/data/johnsonko/SideProject/Helen/Bulb_Images/PD52.jpg")
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,"Total_Bulb_Bins_PD52.txt",row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,"Total_Bulb_Bins_PD52.txt",row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
# PD_56_236
working.im <- load.image("/data/johnsonko/SideProject/Helen/Bulb_Images/PD56.jpg")
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,"Total_Bulb_Bins_PD56.txt",row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,"Total_Bulb_Bins_PD56.txt",row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
# PD_58_229
working.im <- load.image("/data/johnsonko/SideProject/Helen/Bulb_Images/PD58.jpg")
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,"Total_Bulb_Bins_PD58.txt",row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,"Total_Bulb_Bins_PD58.txt",row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
# PD_77_197
working.im <- load.image("/data/johnsonko/SideProject/Helen/Bulb_Images/PD77.jpg")
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,"Total_Bulb_Bins_PD77.txt",row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,"Total_Bulb_Bins_PD77.txt",row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
# PD_79_201
working.im <- load.image("/data/johnsonko/SideProject/Helen/Bulb_Images/PD79.jpg")
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,"Total_Bulb_Bins_PD79.txt",row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,"Total_Bulb_Bins_PD79.txt",row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
setwd("/data/johnsonko/SideProject/Helen/20200811/Bulb_Bins")
Total.Bulb.Bins.AZ109 <- read.table("/data/johnsonko/SideProject/Helen/20200716/Total_Bulb_Bin_Counts/Total_Bulb_Bins_AZ109.txt",skip=1)
Total.Bulb.Bins.AZ84 <- read.table("/data/johnsonko/SideProject/Helen/20200716/Total_Bulb_Bin_Counts/Total_Bulb_Bins_AZ84.txt",skip=1)
Total.Bulb.Bins.AZ90 <- read.table("/data/johnsonko/SideProject/Helen/20200716/Total_Bulb_Bin_Counts/Total_Bulb_Bins_AZ90.txt",skip=1)
Total.Bulb.Bins.AZ99 <- read.table("/data/johnsonko/SideProject/Helen/20200716/Total_Bulb_Bin_Counts/Total_Bulb_Bins_AZ99.txt",skip=1)
Total.Bulb.Bins.H190 <- read.table("/data/johnsonko/SideProject/Helen/20200716/Total_Bulb_Bin_Counts/Total_Bulb_Bins_H190.txt",skip=1)
Total.Bulb.Bins.H250 <- read.table("/data/johnsonko/SideProject/Helen/20200716/Total_Bulb_Bin_Counts/Total_Bulb_Bins_H250.txt",skip=1)
Total.Bulb.Bins.H251 <- read.table("/data/johnsonko/SideProject/Helen/20200716/Total_Bulb_Bin_Counts/Total_Bulb_Bins_H251.txt",skip=1)
Total.Bulb.Bins.OFB57 <- read.table("/data/johnsonko/SideProject/Helen/20200716/Total_Bulb_Bin_Counts/Total_Bulb_Bins_OFB57.txt",skip=1)
Total.Bulb.Bins.OFB6a <- read.table("/data/johnsonko/SideProject/Helen/20200716/Total_Bulb_Bin_Counts/Total_Bulb_Bins_OFB6a.txt",skip=1)
Total.Bulb.Bins.PD52 <- read.table("/data/johnsonko/SideProject/Helen/20200716/Total_Bulb_Bin_Counts/Total_Bulb_Bins_PD52.txt",skip=1)
Total.Bulb.Bins.PD56 <- read.table("/data/johnsonko/SideProject/Helen/20200716/Total_Bulb_Bin_Counts/Total_Bulb_Bins_PD56.txt",skip=1)
Total.Bulb.Bins.PD58 <- read.table("/data/johnsonko/SideProject/Helen/20200716/Total_Bulb_Bin_Counts/Total_Bulb_Bins_PD58.txt",skip=1)
Total.Bulb.Bins.PD77 <- read.table("/data/johnsonko/SideProject/Helen/20200716/Total_Bulb_Bin_Counts/Total_Bulb_Bins_PD77.txt",skip=1)
Total.Bulb.Bins.PD79 <- read.table("/data/johnsonko/SideProject/Helen/20200716/Total_Bulb_Bin_Counts/Total_Bulb_Bins_PD79.txt",skip=1)
Total.Bulb.Bins.AZ109 <- Total.Bulb.Bins.AZ109[Total.Bulb.Bins.AZ109[,5]>0,]
Total.Bulb.Bins.AZ84 <- Total.Bulb.Bins.AZ84[Total.Bulb.Bins.AZ84[,5]>0,]
Total.Bulb.Bins.AZ90 <- Total.Bulb.Bins.AZ90[Total.Bulb.Bins.AZ90[,5]>0,]
Total.Bulb.Bins.AZ99 <- Total.Bulb.Bins.AZ99[Total.Bulb.Bins.AZ99[,5]>0,]
Total.Bulb.Bins.H190 <- Total.Bulb.Bins.H190[Total.Bulb.Bins.H190[,5]>0,]
Total.Bulb.Bins.H250 <- Total.Bulb.Bins.H250[Total.Bulb.Bins.H250[,5]>0,]
Total.Bulb.Bins.H251 <- Total.Bulb.Bins.H251[Total.Bulb.Bins.H251[,5]>0,]
Total.Bulb.Bins.OFB57 <- Total.Bulb.Bins.OFB57[Total.Bulb.Bins.OFB57[,5]>0,]
Total.Bulb.Bins.OFB6a <- Total.Bulb.Bins.OFB6a[Total.Bulb.Bins.OFB6a[,5]>0,]
Total.Bulb.Bins.PD52 <- Total.Bulb.Bins.PD52[Total.Bulb.Bins.PD52[,5]>0,]
Total.Bulb.Bins.PD56 <- Total.Bulb.Bins.PD56[Total.Bulb.Bins.PD56[,5]>0,]
Total.Bulb.Bins.PD58 <- Total.Bulb.Bins.PD58[Total.Bulb.Bins.PD58[,5]>0,]
Total.Bulb.Bins.PD77 <- Total.Bulb.Bins.PD77[Total.Bulb.Bins.PD77[,5]>0,]
Total.Bulb.Bins.PD79 <- Total.Bulb.Bins.PD79[Total.Bulb.Bins.PD79[,5]>0,]
dim(Total.Bulb.Bins.AZ109)[1]
dim(Total.Bulb.Bins.AZ84)[1]
dim(Total.Bulb.Bins.AZ90)[1]
dim(Total.Bulb.Bins.AZ99)[1]
dim(Total.Bulb.Bins.H190)[1]
dim(Total.Bulb.Bins.H250)[1]
dim(Total.Bulb.Bins.H251)[1]
dim(Total.Bulb.Bins.OFB57)[1]
dim(Total.Bulb.Bins.OFB6a)[1]
dim(Total.Bulb.Bins.PD52)[1]
dim(Total.Bulb.Bins.PD56)[1]
dim(Total.Bulb.Bins.PD58)[1]
dim(Total.Bulb.Bins.PD77)[1]
dim(Total.Bulb.Bins.PD79)[1]
temp <- Total.Bulb.Bins.AZ109
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("Total.Bulb.Bins.AZ109",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(temp)[[1]] <- new.pos
write.table(temp,"Bulb_Locations_AZ109.txt",sep="\t")
final.Bulb.Locations.AZ109 <- temp
temp <- Total.Bulb.Bins.AZ84
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("Total.Bulb.Bins.AZ84",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(temp)[[1]] <- new.pos
write.table(temp,"Bulb_Locations_AZ84.txt",sep="\t")
final.Bulb.Locations.AZ84 <- temp
temp <- Total.Bulb.Bins.AZ90
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("Total.Bulb.Bins.AZ90",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(temp)[[1]] <- new.pos
write.table(temp,"Bulb_Locations_AZ90.txt",sep="\t")
final.Bulb.Locations.AZ90 <- temp
temp <- Total.Bulb.Bins.AZ99
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("Total.Bulb.Bins.AZ99",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(temp)[[1]] <- new.pos
write.table(temp,"Bulb_Locations_AZ99.txt",sep="\t")
final.Bulb.Locations.AZ99 <- temp
temp <- Total.Bulb.Bins.H190
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("Total.Bulb.Bins.H190",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(temp)[[1]] <- new.pos
write.table(temp,"Bulb_Locations_H190.txt",sep="\t")
final.Bulb.Locations.H190 <- temp
temp <- Total.Bulb.Bins.H250
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("Total.Bulb.Bins.H250",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(temp)[[1]] <- new.pos
write.table(temp,"Bulb_Locations_H250.txt",sep="\t")
final.Bulb.Locations.H250 <- temp
temp <- Total.Bulb.Bins.H251
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("Total.Bulb.Bins.H251",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(temp)[[1]] <- new.pos
write.table(temp,"Bulb_Locations_H251.txt",sep="\t")
final.Bulb.Locations.H251 <- temp
temp <- Total.Bulb.Bins.OFB57
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("Total.Bulb.Bins.OFB57",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(temp)[[1]] <- new.pos
write.table(temp,"Bulb_Locations_OFB57.txt",sep="\t")
final.Bulb.Locations.OFB57 <- temp
temp <- Total.Bulb.Bins.OFB6a
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("Total.Bulb.Bins.OFB6a",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(temp)[[1]] <- new.pos
write.table(temp,"Bulb_Locations_OFB6a.txt",sep="\t")
final.Bulb.Locations.OFB6a <- temp
temp <- Total.Bulb.Bins.PD52
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("Total.Bulb.Bins.PD52",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(temp)[[1]] <- new.pos
write.table(temp,"Bulb_Locations_PD52.txt",sep="\t")
final.Bulb.Locations.PD52 <- temp
temp <- Total.Bulb.Bins.PD56
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("Total.Bulb.Bins.PD56",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(temp)[[1]] <- new.pos
write.table(temp,"Bulb_Locations_PD56.txt",sep="\t")
final.Bulb.Locations.PD56 <- temp
temp <- Total.Bulb.Bins.PD58
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("Total.Bulb.Bins.PD58",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(temp)[[1]] <- new.pos
write.table(temp,"Bulb_Locations_PD58.txt",sep="\t")
final.Bulb.Locations.PD58 <- temp
temp <- Total.Bulb.Bins.PD77
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("Total.Bulb.Bins.PD77",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(temp)[[1]] <- new.pos
write.table(temp,"Bulb_Locations_PD77.txt",sep="\t")
final.Bulb.Locations.PD77 <- temp
temp <- Total.Bulb.Bins.PD79
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("Total.Bulb.Bins.PD79",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(temp)[[1]] <- new.pos
write.table(temp,"Bulb_Locations_PD79.txt",sep="\t")
final.Bulb.Locations.PD79 <- temp
#################################################################################
# Standardize Image File Names
#################################################################################
# AZ_109_146
setwd("/data/johnsonko/SideProject/Helen/20200811/Starting_Images/AZ_109_146")
system("mv *SYNUCLEIN.jpg ALPHA_SYNUCLEIN.jpg")
system("mv *_BETA*.jpg BETA_AMYLOID.jpg")
system("mv *CALBINDIN.jpg CALBINDIN.jpg")
system("mv *CD31.jpg CD31.jpg")
system("mv *CH*NEUN.jpg CH_NEUN.jpg")
system("mv *_CNPASE.jpg CNPASE.jpg")
system("mv *CNPase.jpg CNPASE.jpg")
system("mv *_COLLAGEN*.jpg COLLAGEN_IV.jpg")
system("mv *_DAPI.jpg DAPI.jpg")
system("mv *GFAP.jpg GFAP.jpg")
system("mv *_HISTONES.jpg HISTONES.jpg")
system("mv *_HLADR.jpg HLADR.jpg")
system("mv *_IBA1.jpg IBA1.jpg")
system("mv *_MAP2.jpg MAP2.jpg")
system("mv *MYELIN*.jpg MBP.jpg")
system("mv *MBP*.jpg MBP.jpg")
system("mv *NEUROFILAMENT*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEUROFILAMENT*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *NFL*.jpg NEURO_LIGHT.jpg")
system("mv *NFH*.jpg NEURO_HEAVY.jpg")
system("mv *NEURO*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEURO*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *_OMP.jpg OMP.jpg")
system("mv *_PGP9*.jpg PGP9_5.jpg")
system("mv *CALRETININ.jpg RB_CALRETININ.jpg")
system("mv *_S100.jpg S100.jpg")
system("mv *_SYNAPTOPHYSIN.jpg SYNAPTOPHYSIN.jpg")
system("mv *TAU*.jpg TAU.jpg")
system("mv *_TOMATO*.jpg TOMATO_LECTIN.jpg")
system("mv *HYDROXYLASE.jpg TYR_HYDROXYLASE.jpg")
system("mv *_UEA*.jpg UEA_LECTIN.jpg")
# AZ_251_146
setwd("/data/johnsonko/SideProject/Helen/20200811/Starting_Images/AZ_251_146")
system("mv *SYNUCLEIN.jpg ALPHA_SYNUCLEIN.jpg")
system("mv *_BETA*.jpg BETA_AMYLOID.jpg")
system("mv *CALBINDIN.jpg CALBINDIN.jpg")
system("mv *CD31.jpg CD31.jpg")
system("mv *CH*NEUN.jpg CH_NEUN.jpg")
system("mv *_CNPASE.jpg CNPASE.jpg")
system("mv *CNPase.jpg CNPASE.jpg")
system("mv *_COLLAGEN*.jpg COLLAGEN_IV.jpg")
system("mv *_DAPI.jpg DAPI.jpg")
system("mv *GFAP.jpg GFAP.jpg")
system("mv *_HISTONES.jpg HISTONES.jpg")
system("mv *_HLADR.jpg HLADR.jpg")
system("mv *_IBA1.jpg IBA1.jpg")
system("mv *_MAP2.jpg MAP2.jpg")
system("mv *MYELIN*.jpg MBP.jpg")
system("mv *MBP*.jpg MBP.jpg")
system("mv *NEUROFILAMENT*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEUROFILAMENT*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *NFL*.jpg NEURO_LIGHT.jpg")
system("mv *NFH*.jpg NEURO_HEAVY.jpg")
system("mv *NEURO*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEURO*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *_OMP.jpg OMP.jpg")
system("mv *_PGP9*.jpg PGP9_5.jpg")
system("mv *CALRETININ.jpg RB_CALRETININ.jpg")
system("mv *_S100.jpg S100.jpg")
system("mv *_SYNAPTOPHYSIN.jpg SYNAPTOPHYSIN.jpg")
system("mv *TAU*.jpg TAU.jpg")
system("mv *_TOMATO*.jpg TOMATO_LECTIN.jpg")
system("mv *HYDROXYLASE.jpg TYR_HYDROXYLASE.jpg")
system("mv *_UEA*.jpg UEA_LECTIN.jpg")
# AZ_84_145
setwd("/data/johnsonko/SideProject/Helen/20200811/Starting_Images/AZ_84_145")
system("mv *SYNUCLEIN.jpg ALPHA_SYNUCLEIN.jpg")
system("mv *_BETA*.jpg BETA_AMYLOID.jpg")
system("mv *CALBINDIN.jpg CALBINDIN.jpg")
system("mv *CD31.jpg CD31.jpg")
system("mv *CH*NEUN.jpg CH_NEUN.jpg")
system("mv *_CNPASE.jpg CNPASE.jpg")
system("mv *CNPase.jpg CNPASE.jpg")
system("mv *_COLLAGEN*.jpg COLLAGEN_IV.jpg")
system("mv *_DAPI.jpg DAPI.jpg")
system("mv *GFAP.jpg GFAP.jpg")
system("mv *_HISTONES.jpg HISTONES.jpg")
system("mv *_HLADR.jpg HLADR.jpg")
system("mv *_IBA1.jpg IBA1.jpg")
system("mv *_MAP2.jpg MAP2.jpg")
system("mv *MYELIN*.jpg MBP.jpg")
system("mv *MBP*.jpg MBP.jpg")
system("mv *NEUROFILAMENT*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEUROFILAMENT*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *NFL*.jpg NEURO_LIGHT.jpg")
system("mv *NFH*.jpg NEURO_HEAVY.jpg")
system("mv *NEURO*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEURO*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *_OMP.jpg OMP.jpg")
system("mv *_PGP9*.jpg PGP9_5.jpg")
system("mv *CALRETININ.jpg RB_CALRETININ.jpg")
system("mv *_S100.jpg S100.jpg")
system("mv *_SYNAPTOPHYSIN.jpg SYNAPTOPHYSIN.jpg")
system("mv *TAU*.jpg TAU.jpg")
system("mv *_TOMATO*.jpg TOMATO_LECTIN.jpg")
system("mv *HYDROXYLASE.jpg TYR_HYDROXYLASE.jpg")
system("mv *_UEA*.jpg UEA_LECTIN.jpg")
# AZ_90_242
setwd("/data/johnsonko/SideProject/Helen/20200811/Starting_Images/AZ_90_242")
system("mv *SYNUCLEIN.jpg ALPHA_SYNUCLEIN.jpg")
system("mv *_BETA*.jpg BETA_AMYLOID.jpg")
system("mv *CALBINDIN.jpg CALBINDIN.jpg")
system("mv *CD31.jpg CD31.jpg")
system("mv *CH*NEUN.jpg CH_NEUN.jpg")
system("mv *_CNPASE.jpg CNPASE.jpg")
system("mv *CNPase.jpg CNPASE.jpg")
system("mv *_COLLAGEN*.jpg COLLAGEN_IV.jpg")
system("mv *_DAPI.jpg DAPI.jpg")
system("mv *GFAP.jpg GFAP.jpg")
system("mv *_HISTONES.jpg HISTONES.jpg")
system("mv *_HLADR.jpg HLADR.jpg")
system("mv *_IBA1.jpg IBA1.jpg")
system("mv *_MAP2.jpg MAP2.jpg")
system("mv *MYELIN*.jpg MBP.jpg")
system("mv *MBP*.jpg MBP.jpg")
system("mv *NEUROFILAMENT*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEUROFILAMENT*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *NFL*.jpg NEURO_LIGHT.jpg")
system("mv *NFH*.jpg NEURO_HEAVY.jpg")
system("mv *NEURO*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEURO*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *_OMP.jpg OMP.jpg")
system("mv *_PGP9*.jpg PGP9_5.jpg")
system("mv *CALRETININ.jpg RB_CALRETININ.jpg")
system("mv *_S100.jpg S100.jpg")
system("mv *_SYNAPTOPHYSIN.jpg SYNAPTOPHYSIN.jpg")
system("mv *TAU*.jpg TAU.jpg")
system("mv *_TOMATO*.jpg TOMATO_LECTIN.jpg")
system("mv *HYDROXYLASE.jpg TYR_HYDROXYLASE.jpg")
system("mv *_UEA*.jpg UEA_LECTIN.jpg")
# AZ_99_140
setwd("/data/johnsonko/SideProject/Helen/20200811/Starting_Images/AZ_99_140")
system("mv *SYNUCLEIN.jpg ALPHA_SYNUCLEIN.jpg")
system("mv *_BETA*.jpg BETA_AMYLOID.jpg")
system("mv *CALBINDIN.jpg CALBINDIN.jpg")
system("mv *CD31.jpg CD31.jpg")
system("mv *CH*NEUN.jpg CH_NEUN.jpg")
system("mv *_CNPASE.jpg CNPASE.jpg")
system("mv *CNPase.jpg CNPASE.jpg")
system("mv *_COLLAGEN*.jpg COLLAGEN_IV.jpg")
system("mv *_DAPI.jpg DAPI.jpg")
system("mv *GFAP.jpg GFAP.jpg")
system("mv *_HISTONES.jpg HISTONES.jpg")
system("mv *_HLADR.jpg HLADR.jpg")
system("mv *_IBA1.jpg IBA1.jpg")
system("mv *_MAP2.jpg MAP2.jpg")
system("mv *MYELIN*.jpg MBP.jpg")
system("mv *MBP*.jpg MBP.jpg")
system("mv *NEUROFILAMENT*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEUROFILAMENT*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *NFL*.jpg NEURO_LIGHT.jpg")
system("mv *NFH*.jpg NEURO_HEAVY.jpg")
system("mv *NEURO*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEURO*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *_OMP.jpg OMP.jpg")
system("mv *_PGP9*.jpg PGP9_5.jpg")
system("mv *CALRETININ.jpg RB_CALRETININ.jpg")
system("mv *_S100.jpg S100.jpg")
system("mv *_SYNAPTOPHYSIN.jpg SYNAPTOPHYSIN.jpg")
system("mv *TAU*.jpg TAU.jpg")
system("mv *_TOMATO*.jpg TOMATO_LECTIN.jpg")
system("mv *HYDROXYLASE.jpg TYR_HYDROXYLASE.jpg")
system("mv *_UEA*.jpg UEA_LECTIN.jpg")
# Control_H190_260
setwd("/data/johnsonko/SideProject/Helen/20200811/Starting_Images/Control_H190_260")
system("mv *SYNUCLEIN.jpg ALPHA_SYNUCLEIN.jpg")
system("mv *_BETA*.jpg BETA_AMYLOID.jpg")
system("mv *CALBINDIN.jpg CALBINDIN.jpg")
system("mv *CD31.jpg CD31.jpg")
system("mv *CH*NEUN.jpg CH_NEUN.jpg")
system("mv *_CNPASE.jpg CNPASE.jpg")
system("mv *CNPase.jpg CNPASE.jpg")
system("mv *_COLLAGEN*.jpg COLLAGEN_IV.jpg")
system("mv *_DAPI.jpg DAPI.jpg")
system("mv *GFAP.jpg GFAP.jpg")
system("mv *_HISTONES.jpg HISTONES.jpg")
system("mv *_HLADR.jpg HLADR.jpg")
system("mv *_IBA1.jpg IBA1.jpg")
system("mv *_MAP2.jpg MAP2.jpg")
system("mv *MYELIN*.jpg MBP.jpg")
system("mv *MBP*.jpg MBP.jpg")
system("mv *NEUROFILAMENT*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEUROFILAMENT*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *NFL*.jpg NEURO_LIGHT.jpg")
system("mv *NFH*.jpg NEURO_HEAVY.jpg")
system("mv *NEURO*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEURO*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *_OMP.jpg OMP.jpg")
system("mv *_PGP9*.jpg PGP9_5.jpg")
system("mv *CALRETININ.jpg RB_CALRETININ.jpg")
system("mv *_S100.jpg S100.jpg")
system("mv *_SYNAPTOPHYSIN.jpg SYNAPTOPHYSIN.jpg")
system("mv *TAU*.jpg TAU.jpg")
system("mv *_TOMATO*.jpg TOMATO_LECTIN.jpg")
system("mv *HYDROXYLASE.jpg TYR_HYDROXYLASE.jpg")
system("mv *_UEA*.jpg UEA_LECTIN.jpg")
# Control_H250_185
setwd("/data/johnsonko/SideProject/Helen/20200811/Starting_Images/NewH250")
system("mv *SYNUCLEIN.jpg ALPHA_SYNUCLEIN.jpg")
system("mv *_BETA*.jpg BETA_AMYLOID.jpg")
system("mv *CALBINDIN.jpg CALBINDIN.jpg")
system("mv *CD31.jpg CD31.jpg")
system("mv *CH*NEUN.jpg CH_NEUN.jpg")
system("mv *_CNPASE.jpg CNPASE.jpg")
system("mv *CNPase.jpg CNPASE.jpg")
system("mv *_COLLAGEN*.jpg COLLAGEN_IV.jpg")
system("mv *_DAPI.jpg DAPI.jpg")
system("mv *GFAP.jpg GFAP.jpg")
system("mv *_HISTONES.jpg HISTONES.jpg")
system("mv *_HLADR.jpg HLADR.jpg")
system("mv *_IBA1.jpg IBA1.jpg")
system("mv *_MAP2.jpg MAP2.jpg")
system("mv *MAP2*.jpg MAP2.jpg")
system("mv *MYELIN*.jpg MBP.jpg")
system("mv *MBP*.jpg MBP.jpg")
system("mv *NEUROFILAMENT*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEUROFILAMENT*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *HEAVY*.jpg NEURO_HEAVY.jpg")
system("mv *NFL*.jpg NEURO_LIGHT.jpg")
system("mv *NFH*.jpg NEURO_HEAVY.jpg")
system("mv *NEURO*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEURO*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *_OMP.jpg OMP.jpg")
system("mv *_PGP9*.jpg PGP9_5.jpg")
system("mv *CALRETININ.jpg RB_CALRETININ.jpg")
system("mv *_S100.jpg S100.jpg")
system("mv *S100*.jpg S100.jpg")
system("mv *_SYNAPTOPHYSIN.jpg SYNAPTOPHYSIN.jpg")
system("mv *TAU*.jpg TAU.jpg")
system("mv *_TOMATO*.jpg TOMATO_LECTIN.jpg")
system("mv *HYDROXYLASE.jpg TYR_HYDROXYLASE.jpg")
system("mv *_UEA*.jpg UEA_LECTIN.jpg")
# Control_OFB57_194
setwd("/data/johnsonko/SideProject/Helen/20200811/Starting_Images/Control_OFB57_194")
system("mv *SYNUCLEIN.jpg ALPHA_SYNUCLEIN.jpg")
system("mv *_BETA*.jpg BETA_AMYLOID.jpg")
system("mv *CALBINDIN.jpg CALBINDIN.jpg")
system("mv *CD31.jpg CD31.jpg")
system("mv *CH*NEUN.jpg CH_NEUN.jpg")
system("mv *_CNPASE.jpg CNPASE.jpg")
system("mv *CNPase.jpg CNPASE.jpg")
system("mv *_COLLAGEN*.jpg COLLAGEN_IV.jpg")
system("mv *_DAPI.jpg DAPI.jpg")
system("mv *GFAP.jpg GFAP.jpg")
system("mv *_HISTONES.jpg HISTONES.jpg")
system("mv *_HLADR.jpg HLADR.jpg")
system("mv *_IBA1.jpg IBA1.jpg")
system("mv *_MAP2.jpg MAP2.jpg")
system("mv *MYELIN*.jpg MBP.jpg")
system("mv *MBP*.jpg MBP.jpg")
system("mv *NEUROFILAMENT*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEUROFILAMENT*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *NFL*.jpg NEURO_LIGHT.jpg")
system("mv *NFH*.jpg NEURO_HEAVY.jpg")
system("mv *NEURO*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEURO*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *_OMP.jpg OMP.jpg")
system("mv *_PGP9*.jpg PGP9_5.jpg")
system("mv *CALRETININ.jpg RB_CALRETININ.jpg")
system("mv *_S100.jpg S100.jpg")
system("mv *_SYNAPTOPHYSIN.jpg SYNAPTOPHYSIN.jpg")
system("mv *TAU*.jpg TAU.jpg")
system("mv *_TOMATO*.jpg TOMATO_LECTIN.jpg")
system("mv *HYDROXYLASE.jpg TYR_HYDROXYLASE.jpg")
system("mv *_UEA*.jpg UEA_LECTIN.jpg")
# Control_OFB6A_107
setwd("/data/johnsonko/SideProject/Helen/20200811/Starting_Images/Control_OFB6A_107")
system("mv *SYNUCLEIN.jpg ALPHA_SYNUCLEIN.jpg")
system("mv *_BETA*.jpg BETA_AMYLOID.jpg")
system("mv *CALBINDIN.jpg CALBINDIN.jpg")
system("mv *CD31.jpg CD31.jpg")
system("mv *CH*NEUN.jpg CH_NEUN.jpg")
system("mv *_CNPASE.jpg CNPASE.jpg")
system("mv *CNPase.jpg CNPASE.jpg")
system("mv *_COLLAGEN*.jpg COLLAGEN_IV.jpg")
system("mv *_DAPI.jpg DAPI.jpg")
system("mv *GFAP.jpg GFAP.jpg")
system("mv *_HISTONES.jpg HISTONES.jpg")
system("mv *_HLADR.jpg HLADR.jpg")
system("mv *_IBA1.jpg IBA1.jpg")
system("mv *_MAP2.jpg MAP2.jpg")
system("mv *MYELIN*.jpg MBP.jpg")
system("mv *MBP*.jpg MBP.jpg")
system("mv *NEUROFILAMENT*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEUROFILAMENT*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *NFL*.jpg NEURO_LIGHT.jpg")
system("mv *NFH*.jpg NEURO_HEAVY.jpg")
system("mv *NEURO*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEURO*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *_OMP.jpg OMP.jpg")
system("mv *_PGP9*.jpg PGP9_5.jpg")
system("mv *CALRETININ.jpg RB_CALRETININ.jpg")
system("mv *_S100.jpg S100.jpg")
system("mv *_SYNAPTOPHYSIN.jpg SYNAPTOPHYSIN.jpg")
system("mv *TAU*.jpg TAU.jpg")
system("mv *_TOMATO*.jpg TOMATO_LECTIN.jpg")
system("mv *HYDROXYLASE.jpg TYR_HYDROXYLASE.jpg")
system("mv *_UEA*.jpg UEA_LECTIN.jpg")
# PD_52_247
setwd("/data/johnsonko/SideProject/Helen/20200811/Starting_Images/PD_52_247")
system("mv *SYNUCLEIN.jpg ALPHA_SYNUCLEIN.jpg")
system("mv *_BETA*.jpg BETA_AMYLOID.jpg")
system("mv *CALBINDIN.jpg CALBINDIN.jpg")
system("mv *CD31.jpg CD31.jpg")
system("mv *CH*NEUN.jpg CH_NEUN.jpg")
system("mv *_CNPASE.jpg CNPASE.jpg")
system("mv *CNPase.jpg CNPASE.jpg")
system("mv *_COLLAGEN*.jpg COLLAGEN_IV.jpg")
system("mv *_DAPI.jpg DAPI.jpg")
system("mv *GFAP.jpg GFAP.jpg")
system("mv *_HISTONES.jpg HISTONES.jpg")
system("mv *_HLADR.jpg HLADR.jpg")
system("mv *_IBA1.jpg IBA1.jpg")
system("mv *_MAP2.jpg MAP2.jpg")
system("mv *MYELIN*.jpg MBP.jpg")
system("mv *MBP*.jpg MBP.jpg")
system("mv *NEUROFILAMENT*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEUROFILAMENT*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *NFL*.jpg NEURO_LIGHT.jpg")
system("mv *NFH*.jpg NEURO_HEAVY.jpg")
system("mv *NEURO*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEURO*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *_OMP.jpg OMP.jpg")
system("mv *_PGP9*.jpg PGP9_5.jpg")
system("mv *CALRETININ.jpg RB_CALRETININ.jpg")
system("mv *_S100.jpg S100.jpg")
system("mv *_SYNAPTOPHYSIN.jpg SYNAPTOPHYSIN.jpg")
system("mv *TAU*.jpg TAU.jpg")
system("mv *_TOMATO*.jpg TOMATO_LECTIN.jpg")
system("mv *HYDROXYLASE.jpg TYR_HYDROXYLASE.jpg")
system("mv *_UEA*.jpg UEA_LECTIN.jpg")
# PD_56_236
setwd("/data/johnsonko/SideProject/Helen/20200811/Starting_Images/PD_56_236")
system("mv *SYNUCLEIN.jpg ALPHA_SYNUCLEIN.jpg")
system("mv *_BETA*.jpg BETA_AMYLOID.jpg")
system("mv *CALBINDIN.jpg CALBINDIN.jpg")
system("mv *CD31.jpg CD31.jpg")
system("mv *CH*NEUN.jpg CH_NEUN.jpg")
system("mv *_CNPASE.jpg CNPASE.jpg")
system("mv *CNPase.jpg CNPASE.jpg")
system("mv *_COLLAGEN*.jpg COLLAGEN_IV.jpg")
system("mv *_DAPI.jpg DAPI.jpg")
system("mv *GFAP.jpg GFAP.jpg")
system("mv *_HISTONES.jpg HISTONES.jpg")
system("mv *_HLADR.jpg HLADR.jpg")
system("mv *_IBA1.jpg IBA1.jpg")
system("mv *_MAP2.jpg MAP2.jpg")
system("mv *MYELIN*.jpg MBP.jpg")
system("mv *MBP*.jpg MBP.jpg")
system("mv *NEUROFILAMENT*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEUROFILAMENT*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *NFL*.jpg NEURO_LIGHT.jpg")
system("mv *NFH*.jpg NEURO_HEAVY.jpg")
system("mv *NEURO*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEURO*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *_OMP.jpg OMP.jpg")
system("mv *_PGP9*.jpg PGP9_5.jpg")
system("mv *CALRETININ.jpg RB_CALRETININ.jpg")
system("mv *_S100.jpg S100.jpg")
system("mv *_SYNAPTOPHYSIN.jpg SYNAPTOPHYSIN.jpg")
system("mv *TAU*.jpg TAU.jpg")
system("mv *_TOMATO*.jpg TOMATO_LECTIN.jpg")
system("mv *HYDROXYLASE.jpg TYR_HYDROXYLASE.jpg")
system("mv *_UEA*.jpg UEA_LECTIN.jpg")
# PD_58_229
setwd("/data/johnsonko/SideProject/Helen/20200811/Starting_Images/PD_58_229")
system("mv *SYNUCLEIN.jpg ALPHA_SYNUCLEIN.jpg")
system("mv *_BETA*.jpg BETA_AMYLOID.jpg")
system("mv *CALBINDIN.jpg CALBINDIN.jpg")
system("mv *CD31.jpg CD31.jpg")
system("mv *CH*NEUN.jpg CH_NEUN.jpg")
system("mv *_CNPASE.jpg CNPASE.jpg")
system("mv *CNPase.jpg CNPASE.jpg")
system("mv *_COLLAGEN*.jpg COLLAGEN_IV.jpg")
system("mv *_DAPI.jpg DAPI.jpg")
system("mv *GFAP.jpg GFAP.jpg")
system("mv *_HISTONES.jpg HISTONES.jpg")
system("mv *_HLADR.jpg HLADR.jpg")
system("mv *_IBA1.jpg IBA1.jpg")
system("mv *_MAP2.jpg MAP2.jpg")
system("mv *MYELIN*.jpg MBP.jpg")
system("mv *MBP*.jpg MBP.jpg")
system("mv *NEUROFILAMENT*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEUROFILAMENT*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *NFL*.jpg NEURO_LIGHT.jpg")
system("mv *NFH*.jpg NEURO_HEAVY.jpg")
system("mv *NEURO*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEURO*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *_OMP.jpg OMP.jpg")
system("mv *_PGP9*.jpg PGP9_5.jpg")
system("mv *CALRETININ.jpg RB_CALRETININ.jpg")
system("mv *_S100.jpg S100.jpg")
system("mv *_SYNAPTOPHYSIN.jpg SYNAPTOPHYSIN.jpg")
system("mv *TAU*.jpg TAU.jpg")
system("mv *_TOMATO*.jpg TOMATO_LECTIN.jpg")
system("mv *HYDROXYLASE.jpg TYR_HYDROXYLASE.jpg")
system("mv *_UEA*.jpg UEA_LECTIN.jpg")
# PD_77_197
setwd("/data/johnsonko/SideProject/Helen/20200811/Starting_Images/PD_77_197")
system("mv *SYNUCLEIN.jpg ALPHA_SYNUCLEIN.jpg")
system("mv *_BETA*.jpg BETA_AMYLOID.jpg")
system("mv *CALBINDIN.jpg CALBINDIN.jpg")
system("mv *CD31.jpg CD31.jpg")
system("mv *CH*NEUN.jpg CH_NEUN.jpg")
system("mv *_CNPASE.jpg CNPASE.jpg")
system("mv *CNPase.jpg CNPASE.jpg")
system("mv *_COLLAGEN*.jpg COLLAGEN_IV.jpg")
system("mv *_DAPI.jpg DAPI.jpg")
system("mv *GFAP.jpg GFAP.jpg")
system("mv *_HISTONES.jpg HISTONES.jpg")
system("mv *_HLADR.jpg HLADR.jpg")
system("mv *_IBA1.jpg IBA1.jpg")
system("mv *_MAP2.jpg MAP2.jpg")
system("mv *MYELIN*.jpg MBP.jpg")
system("mv *MBP*.jpg MBP.jpg")
system("mv *NEUROFILAMENT*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEUROFILAMENT*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *NFL*.jpg NEURO_LIGHT.jpg")
system("mv *NFH*.jpg NEURO_HEAVY.jpg")
system("mv *NEURO*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEURO*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *_OMP.jpg OMP.jpg")
system("mv *_PGP9*.jpg PGP9_5.jpg")
system("mv *CALRETININ.jpg RB_CALRETININ.jpg")
system("mv *_S100.jpg S100.jpg")
system("mv *_SYNAPTOPHYSIN.jpg SYNAPTOPHYSIN.jpg")
system("mv *TAU*.jpg TAU.jpg")
system("mv *_TOMATO*.jpg TOMATO_LECTIN.jpg")
system("mv *HYDROXYLASE.jpg TYR_HYDROXYLASE.jpg")
system("mv *_UEA*.jpg UEA_LECTIN.jpg")
# PD_79_201
setwd("/data/johnsonko/SideProject/Helen/20200811/Starting_Images/PD_79_201")
system("mv *SYNUCLEIN.jpg ALPHA_SYNUCLEIN.jpg")
system("mv *_BETA*.jpg BETA_AMYLOID.jpg")
system("mv *CALBINDIN.jpg CALBINDIN.jpg")
system("mv *CD31.jpg CD31.jpg")
system("mv *CH*NEUN.jpg CH_NEUN.jpg")
system("mv *_CNPASE.jpg CNPASE.jpg")
system("mv *CNPase.jpg CNPASE.jpg")
system("mv *_COLLAGEN*.jpg COLLAGEN_IV.jpg")
system("mv *_DAPI.jpg DAPI.jpg")
system("mv *GFAP.jpg GFAP.jpg")
system("mv *_HISTONES.jpg HISTONES.jpg")
system("mv *_HLADR.jpg HLADR.jpg")
system("mv *_IBA1.jpg IBA1.jpg")
system("mv *_MAP2.jpg MAP2.jpg")
system("mv *MYELIN*.jpg MBP.jpg")
system("mv *MBP*.jpg MBP.jpg")
system("mv *NEUROFILAMENT*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEUROFILAMENT*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *NFL*.jpg NEURO_LIGHT.jpg")
system("mv *NFH*.jpg NEURO_HEAVY.jpg")
system("mv *NEURO*LIGHT.jpg NEURO_LIGHT.jpg")
system("mv *NEURO*HEAVY.jpg NEURO_HEAVY.jpg")
system("mv *_OMP.jpg OMP.jpg")
system("mv *_PGP9*.jpg PGP9_5.jpg")
system("mv *CALRETININ.jpg RB_CALRETININ.jpg")
system("mv *_S100.jpg S100.jpg")
system("mv *_SYNAPTOPHYSIN.jpg SYNAPTOPHYSIN.jpg")
system("mv *TAU*.jpg TAU.jpg")
system("mv *_TOMATO*.jpg TOMATO_LECTIN.jpg")
system("mv *HYDROXYLASE.jpg TYR_HYDROXYLASE.jpg")
system("mv *_UEA*.jpg UEA_LECTIN.jpg")
#################################################################################
# Generate Label Counts
#################################################################################
# AZ_109_146
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/AZ_109_146")
temp.dir <- getwd()
fff.in <- system2("ls",stdout=TRUE)
fff.out <- str_replace(fff.in,".jpg",".txt")
for (i in 1:length(fff.out)) {
fff.out[i] <- paste(c("counts_",fff.out[i]),collapse="",sep="")
}
for (i in 1:length(fff.in)) {
print(i)
ipath <- paste(c(temp.dir,"/",fff.in[i]),collapse="",sep="")
working.im <- load.image(ipath)
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,fff.in[i],row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,fff.out[i],row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
}
# AZ_251_146
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/AZ_251_146")
temp.dir <- getwd()
fff.in <- system2("ls",stdout=TRUE)
fff.out <- str_replace(fff.in,".jpg",".txt")
for (i in 1:length(fff.out)) {
fff.out[i] <- paste(c("counts_",fff.out[i]),collapse="",sep="")
}
for (i in 1:length(fff.in)) {
print(i)
ipath <- paste(c(getwd(),"/",fff.in[i]),collapse="",sep="")
working.im <- load.image(ipath)
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,fff.in[i],row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,fff.out[i],row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
}
# AZ_84_145
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/AZ_84_145")
temp.dir <- getwd()
fff.in <- system2("ls",stdout=TRUE)
fff.out <- str_replace(fff.in,".jpg",".txt")
for (i in 1:length(fff.out)) {
fff.out[i] <- paste(c("counts_",fff.out[i]),collapse="",sep="")
}
for (i in 1:length(fff.in)) {
print(i)
ipath <- paste(c(getwd(),"/",fff.in[i]),collapse="",sep="")
working.im <- load.image(ipath)
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,fff.in[i],row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,fff.out[i],row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
}
# AZ_90_242
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/AZ_90_242")
temp.dir <- getwd()
fff.in <- system2("ls",stdout=TRUE)
fff.out <- str_replace(fff.in,".jpg",".txt")
for (i in 1:length(fff.out)) {
fff.out[i] <- paste(c("counts_",fff.out[i]),collapse="",sep="")
}
for (i in 1:length(fff.in)) {
print(i)
ipath <- paste(c(getwd(),"/",fff.in[i]),collapse="",sep="")
working.im <- load.image(ipath)
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,fff.in[i],row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,fff.out[i],row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
}
# AZ_99_140
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/AZ_99_140")
temp.dir <- getwd()
fff.in <- system2("ls",stdout=TRUE)
fff.out <- str_replace(fff.in,".jpg",".txt")
for (i in 1:length(fff.out)) {
fff.out[i] <- paste(c("counts_",fff.out[i]),collapse="",sep="")
}
for (i in 1:length(fff.in)) {
print(i)
ipath <- paste(c(getwd(),"/",fff.in[i]),collapse="",sep="")
working.im <- load.image(ipath)
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,fff.in[i],row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,fff.out[i],row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
}
# Control_H190_260
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/Control_H190_260")
temp.dir <- getwd()
fff.in <- system2("ls",stdout=TRUE)
fff.out <- str_replace(fff.in,".jpg",".txt")
for (i in 1:length(fff.out)) {
fff.out[i] <- paste(c("counts_",fff.out[i]),collapse="",sep="")
}
for (i in 1:length(fff.in)) {
print(i)
ipath <- paste(c(getwd(),"/",fff.in[i]),collapse="",sep="")
working.im <- load.image(ipath)
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,fff.in[i],row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,fff.out[i],row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
}
# Control_OFB57_194
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/Control_OFB57_194")
temp.dir <- getwd()
fff.in <- system2("ls",stdout=TRUE)
fff.out <- str_replace(fff.in,".jpg",".txt")
for (i in 1:length(fff.out)) {
fff.out[i] <- paste(c("counts_",fff.out[i]),collapse="",sep="")
}
for (i in 1:length(fff.in)) {
print(i)
ipath <- paste(c(getwd(),"/",fff.in[i]),collapse="",sep="")
working.im <- load.image(ipath)
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,fff.in[i],row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,fff.out[i],row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
}
# PD_52_247
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/PD_52_247")
temp.dir <- getwd()
fff.in <- system2("ls",stdout=TRUE)
fff.out <- str_replace(fff.in,".jpg",".txt")
for (i in 1:length(fff.out)) {
fff.out[i] <- paste(c("counts_",fff.out[i]),collapse="",sep="")
}
for (i in 1:length(fff.in)) {
print(i)
ipath <- paste(c(getwd(),"/",fff.in[i]),collapse="",sep="")
working.im <- load.image(ipath)
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,fff.in[i],row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,fff.out[i],row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
}
# PD_56_236
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/PD_56_236")
temp.dir <- getwd()
fff.in <- system2("ls",stdout=TRUE)
fff.out <- str_replace(fff.in,".jpg",".txt")
for (i in 1:length(fff.out)) {
fff.out[i] <- paste(c("counts_",fff.out[i]),collapse="",sep="")
}
for (i in 1:length(fff.in)) {
print(i)
ipath <- paste(c(getwd(),"/",fff.in[i]),collapse="",sep="")
working.im <- load.image(ipath)
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,fff.in[i],row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,fff.out[i],row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
}
# PD_58_229
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/PD_58_229")
temp.dir <- getwd()
fff.in <- system2("ls",stdout=TRUE)
fff.out <- str_replace(fff.in,".jpg",".txt")
for (i in 1:length(fff.out)) {
fff.out[i] <- paste(c("counts_",fff.out[i]),collapse="",sep="")
}
for (i in 1:length(fff.in)) {
print(i)
ipath <- paste(c(getwd(),"/",fff.in[i]),collapse="",sep="")
working.im <- load.image(ipath)
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,fff.in[i],row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,fff.out[i],row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
}
# PD_77_197
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/PD_77_197")
temp.dir <- getwd()
fff.in <- system2("ls",stdout=TRUE)
fff.out <- str_replace(fff.in,".jpg",".txt")
for (i in 1:length(fff.out)) {
fff.out[i] <- paste(c("counts_",fff.out[i]),collapse="",sep="")
}
for (i in 1:length(fff.in)) {
print(i)
ipath <- paste(c(getwd(),"/",fff.in[i]),collapse="",sep="")
working.im <- load.image(ipath)
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,fff.in[i],row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,fff.out[i],row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
}
# PD_79_201
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/PD_79_201")
temp.dir <- getwd()
fff.in <- system2("ls",stdout=TRUE)
fff.out <- str_replace(fff.in,".jpg",".txt")
for (i in 1:length(fff.out)) {
fff.out[i] <- paste(c("counts_",fff.out[i]),collapse="",sep="")
}
for (i in 1:length(fff.in)) {
print(i)
ipath <- paste(c(getwd(),"/",fff.in[i]),collapse="",sep="")
working.im <- load.image(ipath)
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,fff.in[i],row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,fff.out[i],row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
}
# Control_H250_185
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/NewH250")
temp.dir <- getwd()
fff.in <- system2("ls",stdout=TRUE)
fff.out <- str_replace(fff.in,".jpg",".txt")
for (i in 1:length(fff.out)) {
fff.out[i] <- paste(c("counts_",fff.out[i]),collapse="",sep="")
}
for (i in 1:length(fff.in)) {
print(i)
ipath <- paste(c(getwd(),"/",fff.in[i]),collapse="",sep="")
working.im <- load.image(ipath)
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,fff.in[i],row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,fff.out[i],row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
}
# Control_OFB6A_107
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/Control_OFB6A_107")
temp.dir <- getwd()
fff.in <- system2("ls",stdout=TRUE)
fff.out <- str_replace(fff.in,".jpg",".txt")
for (i in 1:length(fff.out)) {
fff.out[i] <- paste(c("counts_",fff.out[i]),collapse="",sep="")
}
for (i in 1:length(fff.in)) {
print(i)
ipath <- paste(c(getwd(),"/",fff.in[i]),collapse="",sep="")
working.im <- load.image(ipath)
working.im <- grayscale(working.im)
working.im.min.x <- 0
working.im.max.x <- dim(working.im)[1]
working.im.min.y <- 0
working.im.max.y <- dim(working.im)[2]
bin.size <- 31
x.pos <- working.im.min.x
finito.x <- 0
temp.out <- t(data.table(c(rep(" ",5))))
write.table(temp.out,fff.in[i],row.names=FALSE,col.names=FALSE,append=FALSE,sep="\t",eol="\n")
while (finito.x==0) {
tempx1 <- x.pos
tempx2 <- x.pos+bin.size
if(tempx2>working.im.max.x) {
tempx2 <- working.im.max.x
finito.x <- 1
}
y.pos <- working.im.min.y
finito.y <- 0
while (finito.y==0) {
tempy1 <- y.pos
tempy2 <- y.pos+bin.size
if(tempy2>working.im.max.y) {
tempy2 <- working.im.max.y
finito.y <- 1
}
temp.grab <- working.im[c(tempx1:tempx2),c(tempy1:tempy2),1,1]
temp.grab <- as.numeric(unlist(temp.grab))
temp.grab <- ifelse(is.na(temp.grab),0,temp.grab)
temp.grab.median <- median(temp.grab)
temp.out <- t(data.table(c(tempx1,tempx2,tempy1,tempy2,temp.grab.median)))
write.table(temp.out,fff.out[i],row.names=FALSE,col.names=FALSE,append=TRUE,sep="\t",eol="\n")
y.pos <- tempy2
}
x.pos <- tempx2
}
}
#############################################################
# Collapse Label Counts into Label Matrix
#############################################################
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/AZ_109_146")
fff.in <- dir()
working.in <- NULL
for(i in 1:length(fff.in)) {
print(i)
temp <- read.table(fff.in[i])
temp0 <- temp[,5]
working.in <- cbind(working.in,temp0)
}
fff.names <- str_replace(fff.in,".txt","")
fff.names <- str_replace(fff.names,"counts_","")
dimnames(working.in)[[2]] <- fff.names
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("AZ_109_146",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(working.in)[[1]] <- new.pos
write.table(working.in,"Collapsed_Counts_AZ_109_146.txt",sep="\t")
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/AZ_251_146")
fff.in <- dir()
working.in <- NULL
for(i in 1:length(fff.in)) {
print(i)
temp <- read.table(fff.in[i])
temp0 <- temp[,5]
working.in <- cbind(working.in,temp0)
}
fff.names <- str_replace(fff.in,".txt","")
fff.names <- str_replace(fff.names,"counts_","")
dimnames(working.in)[[2]] <- fff.names
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("AZ_251_146",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(working.in)[[1]] <- new.pos
write.table(working.in,"Collapsed_Counts_AZ_251_146.txt",sep="\t")
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/AZ_84_145")
fff.in <- dir()
working.in <- NULL
for(i in 1:length(fff.in)) {
print(i)
temp <- read.table(fff.in[i])
temp0 <- temp[,5]
working.in <- cbind(working.in,temp0)
}
fff.names <- str_replace(fff.in,".txt","")
fff.names <- str_replace(fff.names,"counts_","")
dimnames(working.in)[[2]] <- fff.names
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("AZ_84_145",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(working.in)[[1]] <- new.pos
write.table(working.in,"Collapsed_Counts_AZ_84_145.txt",sep="\t")
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/AZ_90_242")
fff.in <- dir()
working.in <- NULL
for(i in 1:length(fff.in)) {
print(i)
temp <- read.table(fff.in[i])
temp0 <- temp[,5]
working.in <- cbind(working.in,temp0)
}
fff.names <- str_replace(fff.in,".txt","")
fff.names <- str_replace(fff.names,"counts_","")
dimnames(working.in)[[2]] <- fff.names
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("AZ_90_242",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(working.in)[[1]] <- new.pos
write.table(working.in,"Collapsed_Counts_AZ_90_242.txt",sep="\t")
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/AZ_99_140")
fff.in <- dir()
working.in <- NULL
for(i in 1:length(fff.in)) {
print(i)
temp <- read.table(fff.in[i])
temp0 <- temp[,5]
working.in <- cbind(working.in,temp0)
}
fff.names <- str_replace(fff.in,".txt","")
fff.names <- str_replace(fff.names,"counts_","")
dimnames(working.in)[[2]] <- fff.names
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("AZ_99_140",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(working.in)[[1]] <- new.pos
write.table(working.in,"Collapsed_Counts_AZ_99_140.txt",sep="\t")
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/Control_H190_260")
fff.in <- dir()
working.in <- NULL
for(i in 1:length(fff.in)) {
print(i)
temp <- read.table(fff.in[i])
temp0 <- temp[,5]
working.in <- cbind(working.in,temp0)
}
fff.names <- str_replace(fff.in,".txt","")
fff.names <- str_replace(fff.names,"counts_","")
dimnames(working.in)[[2]] <- fff.names
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("Control_H190_260",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(working.in)[[1]] <- new.pos
write.table(working.in,"Collapsed_Counts_Control_H190_260.txt",sep="\t")
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/NewH250")
fff.in <- dir()
working.in <- NULL
for(i in 1:length(fff.in)) {
print(i)
temp <- read.table(fff.in[i])
temp0 <- temp[,5]
working.in <- cbind(working.in,temp0)
}
fff.names <- str_replace(fff.in,".txt","")
fff.names <- str_replace(fff.names,"counts_","")
dimnames(working.in)[[2]] <- fff.names
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("Control_H250_185",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(working.in)[[1]] <- new.pos
write.table(working.in,"Collapsed_Counts_Control_H250_185.txt",sep="\t")
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/Control_OFB57_194")
fff.in <- dir()
working.in <- NULL
for(i in 1:length(fff.in)) {
print(i)
temp <- read.table(fff.in[i])
temp0 <- temp[,5]
working.in <- cbind(working.in,temp0)
}
fff.names <- str_replace(fff.in,".txt","")
fff.names <- str_replace(fff.names,"counts_","")
dimnames(working.in)[[2]] <- fff.names
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("Control_OFB57_194",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(working.in)[[1]] <- new.pos
write.table(working.in,"Collapsed_Counts_Control_OFB57_194.txt",sep="\t")
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/Control_OFB6A_107")
fff.in <- dir()
working.in <- NULL
for(i in 1:length(fff.in)) {
print(i)
temp <- read.table(fff.in[i])
temp0 <- temp[,5]
working.in <- cbind(working.in,temp0)
}
fff.names <- str_replace(fff.in,".txt","")
fff.names <- str_replace(fff.names,"counts_","")
dimnames(working.in)[[2]] <- fff.names
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("Control_OFB6A_107",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(working.in)[[1]] <- new.pos
write.table(working.in,"Collapsed_Counts_Control_OFB6A_107.txt",sep="\t")
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/PD_52_247")
fff.in <- dir()
working.in <- NULL
for(i in 1:length(fff.in)) {
print(i)
temp <- read.table(fff.in[i])
temp0 <- temp[,5]
working.in <- cbind(working.in,temp0)
}
fff.names <- str_replace(fff.in,".txt","")
fff.names <- str_replace(fff.names,"counts_","")
dimnames(working.in)[[2]] <- fff.names
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("PD_52_247",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(working.in)[[1]] <- new.pos
write.table(working.in,"Collapsed_Counts_PD_52_247.txt",sep="\t")
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/PD_56_236")
fff.in <- dir()
working.in <- NULL
for(i in 1:length(fff.in)) {
print(i)
temp <- read.table(fff.in[i])
temp0 <- temp[,5]
working.in <- cbind(working.in,temp0)
}
fff.names <- str_replace(fff.in,".txt","")
fff.names <- str_replace(fff.names,"counts_","")
dimnames(working.in)[[2]] <- fff.names
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("PD_56_236",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(working.in)[[1]] <- new.pos
write.table(working.in,"Collapsed_Counts_PD_56_236.txt",sep="\t")
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/PD_58_229")
fff.in <- dir()
working.in <- NULL
for(i in 1:length(fff.in)) {
print(i)
temp <- read.table(fff.in[i])
temp0 <- temp[,5]
working.in <- cbind(working.in,temp0)
}
fff.names <- str_replace(fff.in,".txt","")
fff.names <- str_replace(fff.names,"counts_","")
dimnames(working.in)[[2]] <- fff.names
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("PD_58_229",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(working.in)[[1]] <- new.pos
write.table(working.in,"Collapsed_Counts_PD_58_229.txt",sep="\t")
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/PD_77_197")
fff.in <- dir()
working.in <- NULL
for(i in 1:length(fff.in)) {
print(i)
temp <- read.table(fff.in[i])
temp0 <- temp[,5]
working.in <- cbind(working.in,temp0)
}
fff.names <- str_replace(fff.in,".txt","")
fff.names <- str_replace(fff.names,"counts_","")
dimnames(working.in)[[2]] <- fff.names
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("PD_77_197",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(working.in)[[1]] <- new.pos
write.table(working.in,"Collapsed_Counts_PD_77_197.txt",sep="\t")
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Counts/PD_79_201")
fff.in <- dir()
working.in <- NULL
for(i in 1:length(fff.in)) {
print(i)
temp <- read.table(fff.in[i])
temp0 <- temp[,5]
working.in <- cbind(working.in,temp0)
}
fff.names <- str_replace(fff.in,".txt","")
fff.names <- str_replace(fff.names,"counts_","")
dimnames(working.in)[[2]] <- fff.names
new.x <- apply(temp[,c(1:2)],1,median)
new.y <- apply(temp[,c(3:4)],1,median)
new.pos <- apply(cbind(new.x,new.y),1,paste,collapse="X",sep="")
new.pos <- apply(cbind(rep("PD_79_201",length(new.pos)),new.pos),1,paste,collapse="@",sep="")
dimnames(working.in)[[1]] <- new.pos
write.table(working.in,"Collapsed_Counts_PD_79_201.txt",sep="\t")
#############################################################
# Intersect Label Matrix with Bulb Locations
#############################################################
setwd("/data/johnsonko/SideProject/Helen/20200811/Bulb_Locations")
final.Bulb.Locations.AZ109 <- read.table("Bulb_Locations_AZ109.txt") # AZ_109_146
final.Bulb.Locations.AZ84 <- read.table("Bulb_Locations_AZ84.txt") # AZ_84_145
final.Bulb.Locations.AZ90 <- read.table("Bulb_Locations_AZ90.txt") # AZ_90_242
final.Bulb.Locations.AZ99 <- read.table("Bulb_Locations_AZ99.txt") # AZ_99_140
final.Bulb.Locations.H190 <- read.table("Bulb_Locations_H190.txt") # Control_H190_260
final.Bulb.Locations.H250 <- read.table("Bulb_Locations_H250.txt") # Control_H250_185
final.Bulb.Locations.H251 <- read.table("Bulb_Locations_H251.txt") # AZ_251_146
final.Bulb.Locations.OFB57 <- read.table("Bulb_Locations_OFB57.txt") # Control_OFB57_194
final.Bulb.Locations.OFB6a <- read.table("Bulb_Locations_OFB6a.txt") # Control_OFB6A_107
final.Bulb.Locations.PD52 <- read.table("Bulb_Locations_PD52.txt") # PD_52_247
final.Bulb.Locations.PD56 <- read.table("Bulb_Locations_PD56.txt") # PD_56_236
final.Bulb.Locations.PD58 <- read.table("Bulb_Locations_PD58.txt") # PD_58_22
final.Bulb.Locations.PD77 <- read.table("Bulb_Locations_PD77.txt") # PD_77_197
final.Bulb.Locations.PD79 <- read.table("Bulb_Locations_PD79.txt") # PD_79_201
setwd("/data/johnsonko/SideProject/Helen/20200811/Label_Collapsed")
Collapsed_Counts_AZ_109_146 <- read.table("Collapsed_Counts_AZ_109_146.txt")
Collapsed_Counts_AZ_251_146 <- read.table("Collapsed_Counts_AZ_251_146.txt")
Collapsed_Counts_AZ_84_145 <- read.table("Collapsed_Counts_AZ_84_145.txt")
Collapsed_Counts_AZ_90_242 <- read.table("Collapsed_Counts_AZ_90_242.txt")
Collapsed_Counts_AZ_99_140 <- read.table("Collapsed_Counts_AZ_99_140.txt")
Collapsed_Counts_Control_H190_260 <- read.table("Collapsed_Counts_Control_H190_260.txt")
Collapsed_Counts_Control_H250_185 <- read.table("Collapsed_Counts_Control_H250_185.txt")
Collapsed_Counts_Control_OFB57_194 <- read.table("Collapsed_Counts_Control_OFB57_194.txt")
Collapsed_Counts_Control_OFB6A_107 <- read.table("Collapsed_Counts_Control_OFB6A_107.txt")
Collapsed_Counts_PD_52_247 <- read.table("Collapsed_Counts_PD_52_247.txt")
Collapsed_Counts_PD_56_236 <- read.table("Collapsed_Counts_PD_56_236.txt")
Collapsed_Counts_PD_58_229 <- read.table("Collapsed_Counts_PD_58_229.txt")
Collapsed_Counts_PD_77_197 <- read.table("Collapsed_Counts_PD_77_197.txt")
Collapsed_Counts_PD_79_201 <- read.table("Collapsed_Counts_PD_79_201.txt")
setwd("/data/johnsonko/SideProject/Helen/20200811/Analysis_Ready")
# AZ_109_146
tempa <- Collapsed_Counts_AZ_109_146
tempb <- final.Bulb.Locations.AZ109
tempaa <- dimnames(tempa)[[1]]
tempaaa <- str_replace_all(tempaa,"^.+@","")
dimnames(tempa)[[1]] <- tempaaa
length(tempaaa)
tempbb <- dimnames(tempb)[[1]]
tempbbb <- str_replace_all(tempbb,"^.+@","")
length(tempbbb)
temp1 <- intersect(tempaaa,tempbbb)
length(temp1)
Final_Analysis_Ready_Counts_AZ_109_146 <- tempa[temp1,]
write.table(Final_Analysis_Ready_Counts_AZ_109_146,"Final_Analysis_Ready_Counts_AZ_109_146.txt",sep="\t")
# AZ_251_146
tempa <- Collapsed_Counts_AZ_251_146
tempb <- final.Bulb.Locations.H251
tempaa <- dimnames(tempa)[[1]]
tempaaa <- str_replace_all(tempaa,"^.+@","")
dimnames(tempa)[[1]] <- tempaaa
length(tempaaa)
tempbb <- dimnames(tempb)[[1]]
tempbbb <- str_replace_all(tempbb,"^.+@","")
length(tempbbb)
temp1 <- intersect(tempaaa,tempbbb)
length(temp1)
Final_Analysis_Ready_Counts_AZ_251_146 <- tempa[temp1,]
write.table(Final_Analysis_Ready_Counts_AZ_251_146,"Final_Analysis_Ready_Counts_AZ_251_146.txt",sep="\t")
# AZ_84_145
tempa <- Collapsed_Counts_AZ_84_145
tempb <- final.Bulb.Locations.AZ84
tempaa <- dimnames(tempa)[[1]]
tempaaa <- str_replace_all(tempaa,"^.+@","")
dimnames(tempa)[[1]] <- tempaaa
length(tempaaa)
tempbb <- dimnames(tempb)[[1]]
tempbbb <- str_replace_all(tempbb,"^.+@","")
length(tempbbb)
temp1 <- intersect(tempaaa,tempbbb)
length(temp1)
Final_Analysis_Ready_Counts_AZ_84_145 <- tempa[temp1,]
write.table(Final_Analysis_Ready_Counts_AZ_84_145,"Final_Analysis_Ready_Counts_AZ_84_145.txt",sep="\t")
# AZ_90_242
tempa <- Collapsed_Counts_AZ_90_242
tempb <- final.Bulb.Locations.AZ90
tempaa <- dimnames(tempa)[[1]]
tempaaa <- str_replace_all(tempaa,"^.+@","")
dimnames(tempa)[[1]] <- tempaaa
length(tempaaa)
tempbb <- dimnames(tempb)[[1]]
tempbbb <- str_replace_all(tempbb,"^.+@","")
length(tempbbb)
temp1 <- intersect(tempaaa,tempbbb)
length(temp1)
Final_Analysis_Ready_Counts_AZ_90_242 <- tempa[temp1,]
write.table(Final_Analysis_Ready_Counts_AZ_90_242,"Final_Analysis_Ready_Counts_AZ_90_242.txt",sep="\t")
# AZ_99_140
tempa <- Collapsed_Counts_AZ_99_140
tempb <- final.Bulb.Locations.AZ99
tempaa <- dimnames(tempa)[[1]]
tempaaa <- str_replace_all(tempaa,"^.+@","")
dimnames(tempa)[[1]] <- tempaaa
length(tempaaa)
tempbb <- dimnames(tempb)[[1]]
tempbbb <- str_replace_all(tempbb,"^.+@","")
length(tempbbb)
temp1 <- intersect(tempaaa,tempbbb)
length(temp1)
Final_Analysis_Ready_Counts_AZ_99_140 <- tempa[temp1,]
write.table(Final_Analysis_Ready_Counts_AZ_99_140,"Final_Analysis_Ready_Counts_AZ_99_140.txt",sep="\t")
# Control_H190_260
tempa <- Collapsed_Counts_Control_H190_260
tempb <- final.Bulb.Locations.H190
tempaa <- dimnames(tempa)[[1]]
tempaaa <- str_replace_all(tempaa,"^.+@","")
dimnames(tempa)[[1]] <- tempaaa
length(tempaaa)
tempbb <- dimnames(tempb)[[1]]
tempbbb <- str_replace_all(tempbb,"^.+@","")
length(tempbbb)
temp1 <- intersect(tempaaa,tempbbb)
length(temp1)
Final_Analysis_Ready_Counts_Control_H190_260 <- tempa[temp1,]
write.table(Final_Analysis_Ready_Counts_Control_H190_260,"Final_Analysis_Ready_Counts_Control_H190_260.txt",sep="\t")
# Control_H250_185
tempa <- Collapsed_Counts_Control_H250_185
tempb <- final.Bulb.Locations.H250
tempaa <- dimnames(tempa)[[1]]
tempaaa <- str_replace_all(tempaa,"^.+@","")
dimnames(tempa)[[1]] <- tempaaa
length(tempaaa)
tempbb <- dimnames(tempb)[[1]]
tempbbb <- str_replace_all(tempbb,"^.+@","")
length(tempbbb)
temp1 <- intersect(tempaaa,tempbbb)
length(temp1)
Final_Analysis_Ready_Counts_Control_H250_185 <- tempa[temp1,]
write.table(Final_Analysis_Ready_Counts_Control_H250_185,"Final_Analysis_Ready_Counts_Control_H250_185.txt",sep="\t")
# Control_OFB57_194
tempa <- Collapsed_Counts_Control_OFB57_194
tempb <- final.Bulb.Locations.OFB57
tempaa <- dimnames(tempa)[[1]]
tempaaa <- str_replace_all(tempaa,"^.+@","")
dimnames(tempa)[[1]] <- tempaaa
length(tempaaa)
tempbb <- dimnames(tempb)[[1]]
tempbbb <- str_replace_all(tempbb,"^.+@","")
length(tempbbb)
temp1 <- intersect(tempaaa,tempbbb)
length(temp1)
Final_Analysis_Ready_Counts_Control_OFB57_194 <- tempa[temp1,]
write.table(Final_Analysis_Ready_Counts_Control_OFB57_194,"Final_Analysis_Ready_Counts_Control_OFB57_194.txt",sep="\t")
# Control_OFB6A_107
tempa <- Collapsed_Counts_Control_OFB6A_107
tempb <- final.Bulb.Locations.OFB6a
tempaa <- dimnames(tempa)[[1]]
tempaaa <- str_replace_all(tempaa,"^.+@","")
dimnames(tempa)[[1]] <- tempaaa
length(tempaaa)
tempbb <- dimnames(tempb)[[1]]
tempbbb <- str_replace_all(tempbb,"^.+@","")
length(tempbbb)
temp1 <- intersect(tempaaa,tempbbb)
length(temp1)
Final_Analysis_Ready_Counts_Control_OFB6A_107 <- tempa[temp1,]
write.table(Final_Analysis_Ready_Counts_Control_OFB6A_107,"Final_Analysis_Ready_Counts_Control_OFB6A_107.txt",sep="\t")
# PD_52_247
tempa <- Collapsed_Counts_PD_52_247
tempb <- final.Bulb.Locations.PD52
tempaa <- dimnames(tempa)[[1]]
tempaaa <- str_replace_all(tempaa,"^.+@","")
dimnames(tempa)[[1]] <- tempaaa
length(tempaaa)
tempbb <- dimnames(tempb)[[1]]
tempbbb <- str_replace_all(tempbb,"^.+@","")
length(tempbbb)
temp1 <- intersect(tempaaa,tempbbb)
length(temp1)
Final_Analysis_Ready_Counts_PD_52_247 <- tempa[temp1,]
write.table(Final_Analysis_Ready_Counts_PD_52_247,"Final_Analysis_Ready_Counts_PD_52_247.txt",sep="\t")
# PD_56_236
tempa <- Collapsed_Counts_PD_56_236
tempb <- final.Bulb.Locations.PD56
tempaa <- dimnames(tempa)[[1]]
tempaaa <- str_replace_all(tempaa,"^.+@","")
dimnames(tempa)[[1]] <- tempaaa
length(tempaaa)
tempbb <- dimnames(tempb)[[1]]
tempbbb <- str_replace_all(tempbb,"^.+@","")
length(tempbbb)
temp1 <- intersect(tempaaa,tempbbb)
length(temp1)
Final_Analysis_Ready_Counts_PD_56_236 <- tempa[temp1,]
write.table(Final_Analysis_Ready_Counts_PD_56_236,"Final_Analysis_Ready_Counts_PD_56_236.txt",sep="\t")
# PD_58_22
tempa <- Collapsed_Counts_PD_58_229
tempb <- final.Bulb.Locations.PD58
tempaa <- dimnames(tempa)[[1]]
tempaaa <- str_replace_all(tempaa,"^.+@","")
dimnames(tempa)[[1]] <- tempaaa
length(tempaaa)
tempbb <- dimnames(tempb)[[1]]
tempbbb <- str_replace_all(tempbb,"^.+@","")
length(tempbbb)
temp1 <- intersect(tempaaa,tempbbb)
length(temp1)
Final_Analysis_Ready_Counts_PD_58_229 <- tempa[temp1,]
write.table(Final_Analysis_Ready_Counts_PD_58_229,"Final_Analysis_Ready_Counts_PD_58_229.txt",sep="\t")
# PD_77_197
tempa <- Collapsed_Counts_PD_77_197
tempb <- final.Bulb.Locations.PD77
tempaa <- dimnames(tempa)[[1]]
tempaaa <- str_replace_all(tempaa,"^.+@","")
dimnames(tempa)[[1]] <- tempaaa
length(tempaaa)
tempbb <- dimnames(tempb)[[1]]
tempbbb <- str_replace_all(tempbb,"^.+@","")
length(tempbbb)
temp1 <- intersect(tempaaa,tempbbb)
length(temp1)
Final_Analysis_Ready_Counts_PD_77_197 <- tempa[temp1,]
write.table(Final_Analysis_Ready_Counts_PD_77_197,"Final_Analysis_Ready_Counts_PD_77_197.txt",sep="\t")
# PD_79_201
tempa <- Collapsed_Counts_PD_79_201
tempb <- final.Bulb.Locations.PD79
tempaa <- dimnames(tempa)[[1]]
tempaaa <- str_replace_all(tempaa,"^.+@","")
dimnames(tempa)[[1]] <- tempaaa
length(tempaaa)
tempbb <- dimnames(tempb)[[1]]
tempbbb <- str_replace_all(tempbb,"^.+@","")
length(tempbbb)
temp1 <- intersect(tempaaa,tempbbb)
length(temp1)
Final_Analysis_Ready_Counts_PD_79_201 <- tempa[temp1,]
write.table(Final_Analysis_Ready_Counts_PD_79_201,"Final_Analysis_Ready_Counts_PD_79_201.txt",sep="\t")
#############################################################
# Cross label Poisson Crunch
# https://protect-au.mimecast.com/s/E2X4CMwGq5hkOrYgcNe4Qk?domain=dataquest.io
#############################################################
setwd("/data/johnsonko/SideProject/Helen/20200811/Analysis_Ready")
Final_Analysis_Ready_Counts_AZ_109_146 <- read.table("Final_Analysis_Ready_Counts_AZ_109_146.txt",header=T)
Final_Analysis_Ready_Counts_AZ_251_146 <- read.table("Final_Analysis_Ready_Counts_AZ_251_146.txt",header=T)
Final_Analysis_Ready_Counts_AZ_84_145 <- read.table("Final_Analysis_Ready_Counts_AZ_84_145.txt",header=T)
Final_Analysis_Ready_Counts_AZ_90_242 <- read.table("Final_Analysis_Ready_Counts_AZ_90_242.txt",header=T)
Final_Analysis_Ready_Counts_AZ_99_140 <- read.table("Final_Analysis_Ready_Counts_AZ_99_140.txt",header=T)
Final_Analysis_Ready_Counts_Control_H190_260 <- read.table("Final_Analysis_Ready_Counts_Control_H190_260.txt",header=T)
Final_Analysis_Ready_Counts_Control_H250_185 <- read.table("Final_Analysis_Ready_Counts_Control_H250_185.txt",header=T)
Final_Analysis_Ready_Counts_Control_OFB57_194 <- read.table("Final_Analysis_Ready_Counts_Control_OFB57_194.txt",header=T)
Final_Analysis_Ready_Counts_Control_OFB6A_107 <- read.table("Final_Analysis_Ready_Counts_Control_OFB6A_107.txt",header=T)
Final_Analysis_Ready_Counts_PD_52_247 <- read.table("Final_Analysis_Ready_Counts_PD_52_247.txt",header=T)
Final_Analysis_Ready_Counts_PD_56_236 <- read.table("Final_Analysis_Ready_Counts_PD_56_236.txt",header=T)
Final_Analysis_Ready_Counts_PD_58_229 <- read.table("Final_Analysis_Ready_Counts_PD_58_229.txt",header=T)
Final_Analysis_Ready_Counts_PD_77_197 <- read.table("Final_Analysis_Ready_Counts_PD_77_197.txt",header=T)
Final_Analysis_Ready_Counts_PD_79_201 <- read.table("Final_Analysis_Ready_Counts_PD_79_201.txt",header=T)
setwd("/data/johnsonko/SideProject/Helen/20200811/Analysis_Slides")
ttt <- c("Final_Analysis_Ready_Counts_AZ_109_146",
"Final_Analysis_Ready_Counts_AZ_251_146",
"Final_Analysis_Ready_Counts_AZ_84_145",
"Final_Analysis_Ready_Counts_AZ_90_242",
"Final_Analysis_Ready_Counts_AZ_99_140",
"Final_Analysis_Ready_Counts_Control_H190_260",
"Final_Analysis_Ready_Counts_Control_H250_185",
"Final_Analysis_Ready_Counts_Control_OFB57_194",
"Final_Analysis_Ready_Counts_Control_OFB6A_107",
"Final_Analysis_Ready_Counts_PD_52_247",
"Final_Analysis_Ready_Counts_PD_56_236",
"Final_Analysis_Ready_Counts_PD_58_229",
"Final_Analysis_Ready_Counts_PD_77_197",
"Final_Analysis_Ready_Counts_PD_79_201")
tttppp <- c("Reconstructed_Slide_For_AZ_109_146.pdf",
"Reconstructed_Slide_For_AZ_251_146.pdf",
"Reconstructed_Slide_For_AZ_84_145.pdf",
"Reconstructed_Slide_For_AZ_90_242.pdf",
"Reconstructed_Slide_For_AZ_99_140.pdf",
"Reconstructed_Slide_For_Control_H190_260.pdf",
"Reconstructed_Slide_For_Control_H250_185.pdf",
"Reconstructed_Slide_For_Control_OFB57_194.pdf",
"Reconstructed_Slide_For_Control_OFB6A_107.pdf",
"Reconstructed_Slide_For_PD_52_247.pdf",
"Reconstructed_Slide_For_PD_56_236.pdf",
"Reconstructed_Slide_For_PD_58_229.pdf",
"Reconstructed_Slide_For_PD_77_197.pdf",
"Reconstructed_Slide_For_PD_79_201.pdf")
running.bin.tally <- NULL
p.cut.off.to.use <- 0.05
for (t in 1:length(ttt)) {
print(t)
temp0 <- eval(parse(text=ttt[t]))
temp0 <- as.matrix(temp0)
temp0 <- round(temp0*100,0)
temp1 <- ifelse(temp0==0,NA,temp0)
temp2 <- as.numeric(unlist(temp1))
temp3 <- temp2[!is.na(temp2)]
temp4 <- density(temp3)
pdf(tttppp[t],width = 28950/1000, height = 6030/1000)
plot(temp4,type='l',main="Cross-Label Distribution",xlab="Intensity",ylab="Frequency")
temp5 <- round(mean(temp3,trim=0.95),0)
temp6 <- max(temp3)
temp7 <- c(0:temp6)
temp8 <- dpois(temp7, temp5)
lines(temp8,col=2,type='l')
abline(v=qpois(p.cut.off.to.use,temp5,lower.tail=FALSE),col=2,lty=2)
legend("topright", title="Legend",c("Actual","Poisson","Pr(<0.05)"), col=c("black","red","red"), horiz=TRUE, cex=0.8, lty=c(1,1,2))
temp9 <- ppois((temp1-1), temp5, lower.tail = FALSE)
temp10 <- ifelse(temp9
0) {
temp111 <- names(temp99)
temp100 <- NULL
for (j in 1:length(temp111)) {
temp122 <- strsplit(temp111[j], "X")[[1]]
temp100 <- rbind(temp100,as.numeric(temp122))
}
newnew.x <- as.numeric(temp100[,1])
newnew.y <- as.numeric(temp100[,2])
points(as.numeric(newnew.x),-1*as.numeric(newnew.y),cex=1,pch=15,col=col.2.use[i])
}
}
dev.off()
running.bin.tally <- cbind(running.bin.tally,temp11)
}
dimnames(running.bin.tally)[[1]] <- dimnames(temp0)[[2]]
dimnames(running.bin.tally)[[2]] <- c("AZ_109_146",
"AZ_251_146",
"AZ_84_145",
"AZ_90_242",
"AZ_99_140",
"Control_H190_260",
"Control_H250_185",
"Control_OFB57_194",
"Control_OFB6A_107",
"PD_52_247",
"PD_56_236",
"PD_58_229",
"PD_77_197",
"PD_79_201")
original.running.bin.tally <- running.bin.tally
running.bin.tally <- rbind(c(dim(final.Bulb.Locations.AZ109)[1],
dim(final.Bulb.Locations.H251)[1],
dim(final.Bulb.Locations.AZ84)[1],
dim(final.Bulb.Locations.AZ90)[1],
dim(final.Bulb.Locations.AZ99)[1],
dim(final.Bulb.Locations.H190)[1],
dim(final.Bulb.Locations.H250)[1],
dim(final.Bulb.Locations.OFB57)[1],
dim(final.Bulb.Locations.OFB6a)[1],
dim(final.Bulb.Locations.PD52)[1],
dim(final.Bulb.Locations.PD56)[1],
dim(final.Bulb.Locations.PD58)[1],
dim(final.Bulb.Locations.PD77)[1],
dim(final.Bulb.Locations.PD79)[1]),running.bin.tally)
dimnames(running.bin.tally)[[1]][1] <- "Bulb_Counts"
setwd("/data/johnsonko/SideProject/Helen/20200811/Analysis_Results")
write.table(running.bin.tally,"running.bin.tally.txt",sep="\t")
running.bin.tally <- read.table("running.bin.tally.txt",header=T,row.names=1)
#############################################################
# Test Bin Counts
#############################################################
running.tally <- running.bin.tally
ratio.tally <- running.tally
for (i in 1:dim(running.tally)[2]) {
temp1 <- running.tally[,i]
temp2 <- temp1[1]
temp3 <- temp1/temp2
ratio.tally[,i] <- temp3
}
ratio.tally <- ratio.tally[-c(1),]
ratio.tally <- as.matrix(ratio.tally)
write.table(ratio.tally,"ratio.tally.txt",sep="\t")
ratio.tally <- read.table("ratio.tally.txt",header=T,row.names=1)
ratio.tally <- as.matrix(ratio.tally)
library(gplots)
library(ggplot2)
tiff("Clustered_HeatMap_PreTesting.tiff",width = 8, height = 8, units = "in", res=300)
cc <- greenred(64)
hhh <- heatmap.2(ratio.tally,trace="none",density.info="none",col=cc,labRow=dimnames(ratio.tally)[[1]],labCol=dimnames(ratio.tally)[[2]],scale="none",mar=c(10,10))
dev.off()
tiff("PCA_PreTesting.tiff",width = 8, height = 8, units = "in", res=300)
col.2.use <- c(rep("red",5),rep("green",4),rep("blue",5))
pca.results <- prcomp(t(ratio.tally))
totalvar <- (pca.results[[1]]^2)
variancePer <- round(totalvar/sum(totalvar)*100,1)
variancePer <- variancePer[1:3]
pca.x <- pca.results$x[,c(1:3)]
palette("default")
x.range <- range(pca.x[,1])
y.range <- range(pca.x[,2])
xlab <- paste(c("Principal Component 1 (",variancePer[1],"%)"),collapse="")
ylab <- paste(c("Principal Component 2 (",variancePer[2],"%)"),collapse="")
plot(pca.x[,1],pca.x[,2],xlab=xlab,ylab=ylab,type='n')
points(pca.x[,1],pca.x[,2],lwd=0.5,col=col.2.use,cex=2,pch=19)
points(pca.x[,1],pca.x[,2],lwd=0.5,col=1,cex=2,pch=21)
legend("topright", title="Legend",c("AZ","Control","PD"), col=c("red","green","blue"), horiz=TRUE, cex=0.8, pch = 19)
dev.off()
#-------------------------------------------------------------------------------
# ANOVA
#-------------------------------------------------------------------------------
library(car)
Final.working.data <- ratio.tally
ggg <- c(rep("AZ",5),rep("Control",4),rep("PD",5))
running.pvs <- NULL
for (i in 1:dim(Final.working.data)[1]) {
print(i)
probe.data <- as.numeric(unlist(Final.working.data[i,]))
temp <- data.frame(DATA=probe.data,GROUP=factor(ggg))
aov.results <- aov(DATA~GROUP,data=temp)
if (deviance(aov.results ) > sqrt(.Machine$double.eps)) {
temp <- Anova(aov.results,type="III",singular.ok=TRUE)
temp <- summary(aov.results)
temp <- as.numeric(unlist(temp))[9]
running.pvs <- c(running.pvs,temp)
} else {
running.pvs <- c(running.pvs,1)
}
}
names(running.pvs) <- dimnames(Final.working.data)[[1]]
#-------------------------------------------------------------------------------
# FDR MCC (alpha=0.05)
#-------------------------------------------------------------------------------
library("multtest")
temp1 <- mt.rawp2adjp(running.pvs, proc="BH")
temp2 <- temp1$adj[order(temp1$index),]
temp2 <- as.data.frame(temp2)
dimnames(temp2)[[1]] <- names(running.pvs)
write.table(temp2,"ANOVA_Results.txt",sep="\t",row.names=T)
#-------------------------------------------------------------------------------
# Perform Pos-Hoc Testing
#-------------------------------------------------------------------------------
posthoc.data <- Final.working.data
running.posthoc <- NULL
running.diffs <- NULL
for (i in 1:dim(posthoc.data)[1]) {
print(i)
print(dim(posthoc.data)[1])
temp1 <- as.numeric(posthoc.data[i,])
temp2 <- data.frame(DATA=temp1,Class=ggg)
temp3 <- aov(DATA~Class,data=temp2)
temp4 <- TukeyHSD(temp3)
temp5 <- temp4[[1]]
temp6 <- temp5[,4]
running.posthoc <- rbind(running.posthoc,temp6)
temp7 <- temp5[,1]
running.diffs <- rbind(running.diffs,temp7)
}
running.posthoc <- as.data.frame(running.posthoc)
dimnames(running.posthoc)[[1]] <- dimnames(posthoc.data)[[1]]
write.table(running.posthoc,"PostHoc_Results.txt",sep="\t",row.names=T)
dd <- Final.working.data
temp0 <- dd[,c(1:5)]
AZ.mean <- apply(temp0,1,mean)
AZ.sd <- apply(temp0,1,sd)
temp0 <- dd[,c(6:9)]
Control.mean <- apply(temp0,1,mean)
Control.sd <- apply(temp0,1,sd)
temp0 <- dd[,c(10:14)]
PD.mean <- apply(temp0,1,mean)
PD.sd <- apply(temp0,1,sd)
cx.out <- cbind(Control.mean,Control.sd,AZ.mean,AZ.sd,PD.mean,PD.sd)
write.table(cx.out,"Mean_SD_Results.txt",sep="\t",row.names=T)
# Post testing plots
fff <- ratio.tally[c(2,9,13,19,20,21,22,24),]
tiff("Clustered_HeatMap_PostTesting.tiff",width = 8, height = 8, units = "in", res=300)
cc <- greenred(64)
cc <- colorpanel(64, "blue", "white","red")
hhh <- heatmap.2(fff,trace="none",density.info="none",col=cc,Colv=FALSE,dendrogram="row",labRow=dimnames(fff)[[1]],labCol=dimnames(fff)[[2]],scale="row",mar=c(10,15))
dev.off()
tiff("dendrogram_20201207.tiff",width = 8, height = 5, res=300, units = "in")
pheatmap::pheatmap(fff, cluster_rows=TRUE, cluster_cols=FALSE,
scale="row",
fontsize=12)
dev.off()
# https://protect-au.mimecast.com/s/qyCHCNLJr5tPlWpAilslQA?domain=cran.r-project.org
library(cluster)
tiff("PCA_PostTesting.tiff",width = 8, height = 8, units = "in", res=300)
col.2.use <- c(rep("red",5),rep("green",4),rep("blue",5))
pca.results <- prcomp(t(fff))
totalvar <- (pca.results[[1]]^2)
variancePer <- round(totalvar/sum(totalvar)*100,1)
variancePer <- variancePer[1:3]
pca.x <- pca.results$x[,c(1:3)]
palette("default")
x.range <- range(pca.x[,1])
y.range <- range(pca.x[,2])
xlab <- paste(c("Principal Component 1 (",variancePer[1],"%)"),collapse="")
ylab <- paste(c("Principal Component 2 (",variancePer[2],"%)"),collapse="")
plot(pca.x[,1],pca.x[,2],xlab=xlab,ylab=ylab,type='n')
points(pca.x[,1],pca.x[,2],lwd=0.5,col=col.2.use,cex=2,pch=19)
points(pca.x[,1],pca.x[,2],lwd=0.5,col=1,cex=2,pch=21)
legend("top", title="Legend",c("AZ","Control","PD"), col=c("red","green","blue"), horiz=TRUE, cex=0.8, pch = 19)
dev.off()
ddd <- c(rep("AZ",5),rep("Control",4),rep("PD",5))
ddd <- as.data.frame(ddd)
dimnames(ddd)[[2]][1] <- "Legend"
tiff("PCA_No_Ellipses_20201207.tiff",width = 8, height = 8, units = "in", res=300)
autoplot(pca.results,data=ddd,colour='Legend',size=4)
dev.off()
tiff("PCA_With_Ellipses_20201207.tiff",width = 8, height = 8, units = "in", res=300)
autoplot(pca.results,data=ddd,colour='Legend',size=4,frame = TRUE, frame.type = 'norm')
dev.off()
https://protect-au.mimecast.com/s/lCCiCOMKv5hZR8kJuRSZ06?domain=stackoverflow.com
library(gmodels)
az.stats <- apply(fff[,(1:5)],1,ci)
control.stats <- apply(fff[,(6:9)],1,ci)
pd.stats <- apply(fff[,(10:14)],1,ci)
dd.stats <- rbind(az.stats[1,],control.stats[1,],pd.stats[1,])
dimnames(dd.stats)[[1]] <- c("AZ","Control","PD")
ci.l.stats <- rbind(az.stats[2,],control.stats[2,],pd.stats[2,])
dimnames(ci.l.stats)[[1]] <- c("AZ","Control","PD")
ci.l.stats <- ifelse(ci.l.stats<0,0,ci.l.stats)
ci.u.stats <- rbind(az.stats[3,],control.stats[3,],pd.stats[3,])
dimnames(ci.u.stats)[[1]] <- c("AZ","Control","PD")
mp <- barplot2(dd.stats, beside = TRUE,
col = c("red","green","blue"),
legend = rownames(dd.stats), ylim = c(0, 1),
plot.ci = TRUE, ci.l = ci.l.stats, ci.u = ci.u.stats )
ci.u <- ci.u.stats
y.cord <- rbind(c(ci.u[1,]+1),c(apply(ci.u,2,max)+5), c(apply(ci.u,2,max)+5),c(ci.u[2,]+1))
x.cord <- apply(mp,2,function(x) rep(x,each=2))
sapply(1:5,function(x) lines(x.cord[,x],y.cord[,x]))
https://protect-au.mimecast.com/s/5zj8CP7Lw5FNGwnOF96uNR?domain=ncbi.nlm.nih.gov
final.ci.l <- NULL
for(i in 1:dim(ci.l.stats)[2]) {
final.ci.l <- c(final.ci.l,ci.l.stats[,i])
}
final.ci.l <- ifelse(final.ci.l<0,0,final.ci.l)
ci.u.stats <- rbind(az.stats[3,],control.stats[3,],pd.stats[3,])
final.ci.u <- NULL
for(i in 1:dim(ci.u.stats)[2]) {
final.ci.u <- c(final.ci.u,ci.u.stats[,i])
}
final.ci.u <- ifelse(final.ci.u>1,1,final.ci.u)
, plot.ci = TRUE, ci.l = ci.l, ci.u = ci.u)
mybarcol <- c("red","green","blue")
hh <- t(VADeaths)[1:2, 5:1]
mybarcol <- "gray20"
ci.l <- hh * 0.85
ci.u <- hh * 1.15
mp <- barplot2(hh, beside = TRUE,
col = c("grey12", "grey82"),
legend = colnames(VADeaths)[1:2], ylim = c(0, 100),
cex.names = 1.5, plot.ci = TRUE, ci.l = ci.l, ci.u = ci.u)
#############################################################
# Perform Clustering
#############################################################
setwd("/data/johnsonko/SideProject/Helen/20200811/Bulb_Locations")
final.Bulb.Locations.AZ109 <- read.table("Bulb_Locations_AZ109.txt") # AZ_109_146
final.Bulb.Locations.AZ84 <- read.table("Bulb_Locations_AZ84.txt") # AZ_84_145
final.Bulb.Locations.AZ90 <- read.table("Bulb_Locations_AZ90.txt") # AZ_90_242
final.Bulb.Locations.AZ99 <- read.table("Bulb_Locations_AZ99.txt") # AZ_99_140
final.Bulb.Locations.H190 <- read.table("Bulb_Locations_H190.txt") # Control_H190_260
final.Bulb.Locations.H250 <- read.table("Bulb_Locations_H250.txt") # Control_H250_185
final.Bulb.Locations.H251 <- read.table("Bulb_Locations_H251.txt") # AZ_251_146
final.Bulb.Locations.OFB57 <- read.table("Bulb_Locations_OFB57.txt") # Control_OFB57_194
final.Bulb.Locations.OFB6a <- read.table("Bulb_Locations_OFB6a.txt") # Control_OFB6A_107
final.Bulb.Locations.PD52 <- read.table("Bulb_Locations_PD52.txt") # PD_52_247
final.Bulb.Locations.PD56 <- read.table("Bulb_Locations_PD56.txt") # PD_56_236
final.Bulb.Locations.PD58 <- read.table("Bulb_Locations_PD58.txt") # PD_58_22
final.Bulb.Locations.PD77 <- read.table("Bulb_Locations_PD77.txt") # PD_77_197
final.Bulb.Locations.PD79 <- read.table("Bulb_Locations_PD79.txt") # PD_79_201
setwd("/data/johnsonko/SideProject/Helen/20200811/Analysis_Ready")
Final_Analysis_Ready_Counts_AZ_109_146 <- read.table("Final_Analysis_Ready_Counts_AZ_109_146.txt",header=T)
Final_Analysis_Ready_Counts_AZ_251_146 <- read.table("Final_Analysis_Ready_Counts_AZ_251_146.txt",header=T)
Final_Analysis_Ready_Counts_AZ_84_145 <- read.table("Final_Analysis_Ready_Counts_AZ_84_145.txt",header=T)
Final_Analysis_Ready_Counts_AZ_90_242 <- read.table("Final_Analysis_Ready_Counts_AZ_90_242.txt",header=T)
Final_Analysis_Ready_Counts_AZ_99_140 <- read.table("Final_Analysis_Ready_Counts_AZ_99_140.txt",header=T)
Final_Analysis_Ready_Counts_Control_H190_260 <- read.table("Final_Analysis_Ready_Counts_Control_H190_260.txt",header=T)
Final_Analysis_Ready_Counts_Control_H250_185 <- read.table("Final_Analysis_Ready_Counts_Control_H250_185.txt",header=T)
Final_Analysis_Ready_Counts_Control_OFB57_194 <- read.table("Final_Analysis_Ready_Counts_Control_OFB57_194.txt",header=T)
Final_Analysis_Ready_Counts_Control_OFB6A_107 <- read.table("Final_Analysis_Ready_Counts_Control_OFB6A_107.txt",header=T)
Final_Analysis_Ready_Counts_PD_52_247 <- read.table("Final_Analysis_Ready_Counts_PD_52_247.txt",header=T)
Final_Analysis_Ready_Counts_PD_56_236 <- read.table("Final_Analysis_Ready_Counts_PD_56_236.txt",header=T)
Final_Analysis_Ready_Counts_PD_58_229 <- read.table("Final_Analysis_Ready_Counts_PD_58_229.txt",header=T)
Final_Analysis_Ready_Counts_PD_77_197 <- read.table("Final_Analysis_Ready_Counts_PD_77_197.txt",header=T)
Final_Analysis_Ready_Counts_PD_79_201 <- read.table("Final_Analysis_Ready_Counts_PD_79_201.txt",header=T)
ttt <- c("Final_Analysis_Ready_Counts_AZ_109_146",
"Final_Analysis_Ready_Counts_AZ_251_146",
"Final_Analysis_Ready_Counts_AZ_84_145",
"Final_Analysis_Ready_Counts_AZ_90_242",
"Final_Analysis_Ready_Counts_AZ_99_140",
"Final_Analysis_Ready_Counts_Control_H190_260",
"Final_Analysis_Ready_Counts_Control_H250_185",
"Final_Analysis_Ready_Counts_Control_OFB57_194",
"Final_Analysis_Ready_Counts_Control_OFB6A_107",
"Final_Analysis_Ready_Counts_PD_52_247",
"Final_Analysis_Ready_Counts_PD_56_236",
"Final_Analysis_Ready_Counts_PD_58_229",
"Final_Analysis_Ready_Counts_PD_77_197",
"Final_Analysis_Ready_Counts_PD_79_201")
p.cut.off.to.use <- 0.05
size.2.use <- NULL
for (t in 1:length(ttt)) {
print(t)
temp0 <- eval(parse(text=ttt[t]))
print(dim(temp0))
temp0 <- as.matrix(temp0)
temp0 <- round(temp0*100,0)
temp1 <- ifelse(temp0==0,NA,temp0)
temp2 <- as.numeric(unlist(temp1))
temp3 <- temp2[!is.na(temp2)]
temp4 <- density(temp3)
temp5 <- round(mean(temp3,trim=0.95),0)
temp6 <- max(temp3)
temp7 <- c(0:temp6)
temp8 <- dpois(temp7, temp5)
temp9 <- ppois((temp1-1), temp5, lower.tail = FALSE)
temp10 <- ifelse(is.na(temp9),1,temp9)
temp11 <- ifelse(temp100
temp13 <- temp11[temp12,]
size.2.use <- c(size.2.use,dim(temp13)[1])
}
size.2.use <- round(size.2.use/size.2.use[1])
size.2.use
ensure.order <- dimnames(Final_Analysis_Ready_Counts_AZ_109_146)[[2]]
ready.to.cluster <- NULL
perform.quantile.coding <- 0
if (perform.quantile.coding==1) {
for (t in 1:length(ttt)) {
print(t)
temp0 <- eval(parse(text=ttt[t]))
temp0 <- as.matrix(temp0)
temp0 <- round(temp0*100,0)
temp1 <- ifelse(temp0==0,NA,temp0)
temp2 <- as.numeric(unlist(temp1))
temp3 <- temp2[!is.na(temp2)]
temp4 <- density(temp3)
temp5 <- round(mean(temp3,trim=0.95),0)
temp6 <- max(temp3)
temp7 <- c(0:temp6)
temp8 <- dpois(temp7, temp5)
temp9 <- ppois((temp1-1), temp5, lower.tail = FALSE)
temp10 <- ifelse(is.na(temp9),1,temp9)
temp11 <- ifelse(temp100
temp13 <- temp11[temp12,]
temptemp <- seq(from=1,to=dim(temp13)[1],by=size.2.use[t])
temp13 <- temp13[temptemp,]
temp14 <- t(temp13)
temp15 <- dimnames(temp14)[[2]]
temp16 <- ttt[t]
temp17 <- str_replace_all(temp16,"Final_Analysis_Ready_Counts_","")
for (i in 1:length(temp15)) {
temp15[i] <- paste(c("Sample_",temp17,"_Pos_",temp15[i]),collapse="",sep="")
}
dimnames(temp14)[[2]] <- temp15
temp14 <- temp14[ensure.order,]
temp18 <- as.numeric(temp14)
temp18 <- temp18[temp18>0]
temp19 <- as.numeric(quantile(temp18,0.25))
temp20 <- as.numeric(quantile(temp18,0.5))
temp21 <- as.numeric(quantile(temp18,0.75))
temp22 <- ifelse(temp14>temp21,100,0)
temp23a <- ifelse(temp14<=temp21,1,0)
temp23b <- ifelse(temp14>temp20,1,0)
temp23 <- temp23a+temp23b
temp23 <- ifelse(temp23==2,75,0)
temp24a <- ifelse(temp14<=temp20,1,0)
temp24b <- ifelse(temp14>temp19,1,0)
temp24 <- temp24a+temp24b
temp24 <- ifelse(temp24==2,50,0)
temp25a <- ifelse(temp14<=temp19,1,0)
temp25b <- ifelse(temp14>0,1,0)
temp25 <- temp25a+temp25b
temp25 <- ifelse(temp25==2,25,0)
temp26 <- temp22+temp23+temp24+temp25
ready.to.cluster <- cbind(ready.to.cluster,temp26)
print(dim(ready.to.cluster))
}
} else {
for (t in 1:length(ttt)) {
print(t)
temp0 <- eval(parse(text=ttt[t]))
temp0 <- as.matrix(temp0)
temp0 <- round(temp0*100,0)
temp1 <- ifelse(temp0==0,NA,temp0)
temp2 <- as.numeric(unlist(temp1))
temp3 <- temp2[!is.na(temp2)]
temp4 <- density(temp3)
temp5 <- round(mean(temp3,trim=0.95),0)
temp6 <- max(temp3)
temp7 <- c(0:temp6)
temp8 <- dpois(temp7, temp5)
temp9 <- ppois((temp1-1), temp5, lower.tail = FALSE)
temp10 <- ifelse(is.na(temp9),1,temp9)
temp11 <- ifelse(temp100
temp13 <- temp11[temp12,]
temptemp <- seq(from=1,to=dim(temp13)[1],by=size.2.use[t])
temp13 <- temp13[temptemp,]
temp14 <- t(temp13)
temp15 <- dimnames(temp14)[[2]]
temp16 <- ttt[t]
temp17 <- str_replace_all(temp16,"Final_Analysis_Ready_Counts_","")
for (i in 1:length(temp15)) {
temp15[i] <- paste(c("Sample_",temp17,"_Pos_",temp15[i]),collapse="",sep="")
}
dimnames(temp14)[[2]] <- temp15
temp14 <- temp14[ensure.order,]
ready.to.cluster <- cbind(ready.to.cluster,temp14)
print(dim(ready.to.cluster))
}
}
setwd("/data/johnsonko/SideProject/Helen/20200811/BinaryCodeAndCrunch")
write.table(ready.to.cluster,"ready.to.cluster.txt",sep="\t")
coded.ready.to.cluster <- ifelse(ready.to.cluster>0,1,0)
write.table(coded.ready.to.cluster,"coded.ready.to.cluster.txt",sep="\t")
working <- apply(coded.ready.to.cluster,2,paste,collapse="_",sep="_")
temp <- names(working)
temptemp <- strsplit(temp,"_Pos_")
temptemp <- as.data.frame(temptemp)
temptemp <- t(temptemp)
dimnames(temptemp)[[1]] <- temp
temp <- as.character(temptemp[,2])
temp <- strsplit(temp,"X")
temp <- as.data.frame(temp)
temp <- t(temp)
temptemp <- cbind(temptemp,temp)
temptemp <- cbind(temptemp,as.character(working))
ready.to.crunch <- temptemp
dimnames(ready.to.crunch)[[2]] <- c("Sample","Location","Xpos","Ypos","String")
write.table(ready.to.crunch,"ready.to.crunch.txt",sep="\t")
unique.samples <- unique(sort(unlist(ready.to.crunch[,1])))
unique.strings <- unique(sort(unlist(ready.to.crunch[,5])))
final.crunch <- NULL
for (i in 1:length(unique.samples)) {
print(i)
temp1 <- ready.to.crunch[ready.to.crunch[,1]==unique.samples[i],]
temp2 <- table(temp1[,5])
temp3 <- setdiff(unique.strings,names(temp2))
temp4 <- rep(0,length(temp3))
names(temp4) <- temp3
temp5 <- c(temp2,temp4)
temp5 <- temp5[unique.strings]
final.crunch <- cbind(final.crunch,temp5)
}
dimnames(final.crunch)[[2]] <- unique.samples
dimnames(final.crunch)[[1]] <- unique.strings
write.table(final.crunch,"final.crunch.txt",sep="\t")
#############################################################
# Test Bin Counts
#############################################################
library(gplots)
library(ggplot2)
tiff("Clustered_HeatMap_PreTesting.tiff",width = 8, height = 8, units = "in", res=300)
cc <- greenred(64)
hhh <- heatmap.2(final.crunch,trace="none",density.info="none",col=cc,labRow=dimnames(final.crunch)[[1]],labCol=dimnames(final.crunch)[[2]],scale="none",mar=c(10,10))
dev.off()
tiff("PCA_PreTesting.tiff",width = 8, height = 8, units = "in", res=300)
col.2.use <- c(rep("red",5),rep("green",4),rep("blue",5))
pca.results <- prcomp(t(final.crunch))
totalvar <- (pca.results[[1]]^2)
variancePer <- round(totalvar/sum(totalvar)*100,1)
variancePer <- variancePer[1:3]
pca.x <- pca.results$x[,c(1:3)]
palette("default")
x.range <- range(pca.x[,1])
y.range <- range(pca.x[,2])
xlab <- paste(c("Principal Component 1 (",variancePer[1],"%)"),collapse="")
ylab <- paste(c("Principal Component 2 (",variancePer[2],"%)"),collapse="")
plot(pca.x[,1],pca.x[,2],xlab=xlab,ylab=ylab,type='n')
points(pca.x[,1],pca.x[,2],lwd=0.5,col=col.2.use,cex=2,pch=19)
points(pca.x[,1],pca.x[,2],lwd=0.5,col=1,cex=2,pch=21)
legend("topright", title="Legend",c("AZ","Control","PD"), col=c("red","green","blue"), horiz=TRUE, cex=0.8, pch = 19)
dev.off()
#-------------------------------------------------------------------------------
# ANOVA
#-------------------------------------------------------------------------------
library(car)
Final.working.data <- final.crunch
ggg <- c(rep("AZ",5),rep("Control",4),rep("PD",5))
running.pvs <- NULL
for (i in 1:dim(Final.working.data)[1]) {
print(i)
probe.data <- as.numeric(unlist(Final.working.data[i,]))
temp <- data.frame(DATA=probe.data,GROUP=factor(ggg))
aov.results <- aov(DATA~GROUP,data=temp)
if (deviance(aov.results ) > sqrt(.Machine$double.eps)) {
temp <- Anova(aov.results,type="III",singular.ok=TRUE)
temp <- summary(aov.results)
temp <- as.numeric(unlist(temp))[9]
running.pvs <- c(running.pvs,temp)
} else {
running.pvs <- c(running.pvs,1)
}
}
names(running.pvs) <- dimnames(Final.working.data)[[1]]
#-------------------------------------------------------------------------------
# FDR MCC (alpha=0.05)
#-------------------------------------------------------------------------------
library("multtest")
temp1 <- mt.rawp2adjp(running.pvs, proc="BH")
temp2 <- temp1$adj[order(temp1$index),]
temp2 <- as.data.frame(temp2)
dimnames(temp2)[[1]] <- names(running.pvs)
write.table(temp2,"ANOVA_Results.txt",sep="\t",row.names=T)
#-------------------------------------------------------------------------------
# Non-Parametric ANOVA
#-------------------------------------------------------------------------------
Final.working.data <- final.crunch
ggg <- c(rep("AZ",5),rep("Control",4),rep("PD",5))
running.pvs <- NULL
for (i in 1:dim(Final.working.data)[1]) {
print(i)
probe.data <- as.numeric(unlist(Final.working.data[i,]))
temp <- data.frame(DATA=probe.data,GROUP=factor(ggg))
temptemp <- kruskal.test(DATA ~ GROUP, data = temp)
temp <- temptemp$p.value
running.pvs <- c(running.pvs,temp)
}
names(running.pvs) <- dimnames(Final.working.data)[[1]]
#-------------------------------------------------------------------------------
# FDR MCC (alpha=0.05)
#-------------------------------------------------------------------------------
library("multtest")
temp1 <- mt.rawp2adjp(running.pvs, proc="BH")
temp2 <- temp1$adj[order(temp1$index),]
temp2 <- as.data.frame(temp2)
dimnames(temp2)[[1]] <- names(running.pvs)
write.table(temp2,"NonParametric_ANOVA_Results.txt",sep="\t",row.names=T)
#-------------------------------------------------------------------------------
# Parametric Pos-Hoc Testing
#-------------------------------------------------------------------------------
posthoc.data <- Final.working.data
running.posthoc <- NULL
running.diffs <- NULL
for (i in 1:dim(posthoc.data)[1]) {
print(i)
print(dim(posthoc.data)[1])
temp1 <- as.numeric(posthoc.data[i,])
temp2 <- data.frame(DATA=temp1,Class=ggg)
temp3 <- aov(DATA~Class,data=temp2)
temp4 <- TukeyHSD(temp3)
temp5 <- temp4[[1]]
temp6 <- temp5[,4]
running.posthoc <- rbind(running.posthoc,temp6)
temp7 <- temp5[,1]
running.diffs <- rbind(running.diffs,temp7)
}
running.posthoc <- as.data.frame(running.posthoc)
dimnames(running.posthoc)[[1]] <- dimnames(posthoc.data)[[1]]
write.table(running.posthoc,"Parametric_PostHoc_Results.txt",sep="\t",row.names=T)
#-------------------------------------------------------------------------------
# Non-Parametric Pos-Hoc Testing
#-------------------------------------------------------------------------------
library(FSA)
posthoc.data <- Final.working.data
running.posthoc <- NULL
for (i in 1:dim(posthoc.data)[1]) {
print(i)
print(dim(posthoc.data)[1])
temp1 <- as.numeric(posthoc.data[i,])
temp2 <- data.frame(DATA=temp1,GROUP=ggg)
temp3 <- dunnTest(DATA~GROUP,data=temp2)
temp4 <- temp3[2][[1]][,3]
running.posthoc <- rbind(running.posthoc,temp4)
}
running.posthoc <- as.data.frame(running.posthoc)
dimnames(running.posthoc)[[1]] <- dimnames(posthoc.data)[[1]]
temp <- as.character(unlist(temp3[2][[1]][,1]))
temp <- str_replace_all(temp," ","")
temp <- str_replace_all(temp,"-","_vs_")
dimnames(running.posthoc)[[2]] <- temp
write.table(running.posthoc,"Non_Paramatric_PostHoc_Results.txt",sep="\t",row.names=T)
#-------------------------------------------------------------------------------
# Mean calculation
#-------------------------------------------------------------------------------
dd <- Final.working.data
temp0 <- dd[,c(1:5)]
AZ.mean <- apply(temp0,1,mean)
AZ.sd <- apply(temp0,1,sd)
temp0 <- dd[,c(6:9)]
Control.mean <- apply(temp0,1,mean)
Control.sd <- apply(temp0,1,sd)
temp0 <- dd[,c(10:14)]
PD.mean <- apply(temp0,1,mean)
PD.sd <- apply(temp0,1,sd)
cx.out <- cbind(Control.mean,Control.sd,AZ.mean,AZ.sd,PD.mean,PD.sd)
write.table(cx.out,"Mean_SD_Results.txt",sep="\t",row.names=T)
#-------------------------------------------------------------------------------
# Post Testing Plots
#-------------------------------------------------------------------------------
hits <- read.table("hits.txt",header=F)
hits <- as.character(hits[,1])
fff <- Final.working.data[hits,]
tiff("Clustered_HeatMap_PostTesting.tiff",width = 8, height = 8, units = "in", res=300)
cc <- greenred(64)
hhh <- heatmap.2(fff,trace="none",density.info="none",col=cc,labRow=dimnames(fff)[[1]],labCol=dimnames(fff)[[2]],mar=c(10,15))
dev.off()
tiff("PCA_PostTesting.tiff",width = 8, height = 8, units = "in", res=300)
col.2.use <- c(rep("red",5),rep("green",4),rep("blue",5))
pca.results <- prcomp(t(fff))
totalvar <- (pca.results[[1]]^2)
variancePer <- round(totalvar/sum(totalvar)*100,1)
variancePer <- variancePer[1:3]
pca.x <- pca.results$x[,c(1:3)]
palette("default")
x.range <- range(pca.x[,1])
y.range <- range(pca.x[,2])
xlab <- paste(c("Principal Component 1 (",variancePer[1],"%)"),collapse="")
ylab <- paste(c("Principal Component 2 (",variancePer[2],"%)"),collapse="")
plot(pca.x[,1],pca.x[,2],xlab=xlab,ylab=ylab,type='n')
points(pca.x[,1],pca.x[,2],lwd=0.5,col=col.2.use,cex=2,pch=19)
points(pca.x[,1],pca.x[,2],lwd=0.5,col=1,cex=2,pch=21)
legend("top", title="Legend",c("AZ","Control","PD"), col=c("red","green","blue"), horiz=TRUE, cex=0.8, pch = 19)
dev.off()
#-------------------------------------------------------------------------------
# Plotting out patterns
#-------------------------------------------------------------------------------
setwd("/data/johnsonko/SideProject/Helen/20200811/BinaryCodeAndCrunch")
load("20200924.RData")
#final.crunch <- read.table("final.crunch.txt")
#ready.to.cluster <- read.table("ready.to.cluster.txt")
coded.ready.to.cluster <- read.table("coded.ready.to.cluster.txt")
working <- apply(coded.ready.to.cluster,2,paste,collapse="_",sep="_")
setwd("/data/johnsonko/SideProject/Helen/20200811/Bulb_Locations")
final.Bulb.Locations.AZ109 <- read.table("Bulb_Locations_AZ109.txt") # AZ_109_146
final.Bulb.Locations.AZ84 <- read.table("Bulb_Locations_AZ84.txt") # AZ_84_145
final.Bulb.Locations.AZ90 <- read.table("Bulb_Locations_AZ90.txt") # AZ_90_242
final.Bulb.Locations.AZ99 <- read.table("Bulb_Locations_AZ99.txt") # AZ_99_140
final.Bulb.Locations.H190 <- read.table("Bulb_Locations_H190.txt") # Control_H190_260
final.Bulb.Locations.H250 <- read.table("Bulb_Locations_H250.txt") # Control_H250_185
final.Bulb.Locations.H251 <- read.table("Bulb_Locations_H251.txt") # AZ_251_146
final.Bulb.Locations.OFB57 <- read.table("Bulb_Locations_OFB57.txt") # Control_OFB57_194
final.Bulb.Locations.OFB6a <- read.table("Bulb_Locations_OFB6a.txt") # Control_OFB6A_107
final.Bulb.Locations.PD52 <- read.table("Bulb_Locations_PD52.txt") # PD_52_247
final.Bulb.Locations.PD56 <- read.table("Bulb_Locations_PD56.txt") # PD_56_236
final.Bulb.Locations.PD58 <- read.table("Bulb_Locations_PD58.txt") # PD_58_22
final.Bulb.Locations.PD77 <- read.table("Bulb_Locations_PD77.txt") # PD_77_197
final.Bulb.Locations.PD79 <- read.table("Bulb_Locations_PD79.txt") # PD_79_201
setwd("/data/johnsonko/SideProject/Helen/20200811/Analysis_Ready")
Final_Analysis_Ready_Counts_AZ_109_146 <- read.table("Final_Analysis_Ready_Counts_AZ_109_146.txt",header=T)
Final_Analysis_Ready_Counts_AZ_251_146 <- read.table("Final_Analysis_Ready_Counts_AZ_251_146.txt",header=T)
Final_Analysis_Ready_Counts_AZ_84_145 <- read.table("Final_Analysis_Ready_Counts_AZ_84_145.txt",header=T)
Final_Analysis_Ready_Counts_AZ_90_242 <- read.table("Final_Analysis_Ready_Counts_AZ_90_242.txt",header=T)
Final_Analysis_Ready_Counts_AZ_99_140 <- read.table("Final_Analysis_Ready_Counts_AZ_99_140.txt",header=T)
Final_Analysis_Ready_Counts_Control_H190_260 <- read.table("Final_Analysis_Ready_Counts_Control_H190_260.txt",header=T)
Final_Analysis_Ready_Counts_Control_H250_185 <- read.table("Final_Analysis_Ready_Counts_Control_H250_185.txt",header=T)
Final_Analysis_Ready_Counts_Control_OFB57_194 <- read.table("Final_Analysis_Ready_Counts_Control_OFB57_194.txt",header=T)
Final_Analysis_Ready_Counts_Control_OFB6A_107 <- read.table("Final_Analysis_Ready_Counts_Control_OFB6A_107.txt",header=T)
Final_Analysis_Ready_Counts_PD_52_247 <- read.table("Final_Analysis_Ready_Counts_PD_52_247.txt",header=T)
Final_Analysis_Ready_Counts_PD_56_236 <- read.table("Final_Analysis_Ready_Counts_PD_56_236.txt",header=T)
Final_Analysis_Ready_Counts_PD_58_229 <- read.table("Final_Analysis_Ready_Counts_PD_58_229.txt",header=T)
Final_Analysis_Ready_Counts_PD_77_197 <- read.table("Final_Analysis_Ready_Counts_PD_77_197.txt",header=T)
Final_Analysis_Ready_Counts_PD_79_201 <- read.table("Final_Analysis_Ready_Counts_PD_79_201.txt",header=T)
ttt <- dimnames(final.Bulb.Locations.AZ109)[[1]]
ttt <- strsplit(ttt,"@")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
ttt <- ttt[,2]
ttt <- as.character(ttt)
ttt <- strsplit(ttt,"X")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
dimnames(ttt)[[1]] <- NULL
plot.bulb.locations.AZ109 <- ttt
ttt <- dimnames(final.Bulb.Locations.AZ84)[[1]]
ttt <- strsplit(ttt,"@")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
ttt <- ttt[,2]
ttt <- as.character(ttt)
ttt <- strsplit(ttt,"X")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
dimnames(ttt)[[1]] <- NULL
plot.bulb.locations.AZ84 <- ttt
ttt <- dimnames(final.Bulb.Locations.AZ90)[[1]]
ttt <- strsplit(ttt,"@")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
ttt <- ttt[,2]
ttt <- as.character(ttt)
ttt <- strsplit(ttt,"X")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
dimnames(ttt)[[1]] <- NULL
plot.bulb.locations.AZ90 <- ttt
ttt <- dimnames(final.Bulb.Locations.AZ99)[[1]]
ttt <- strsplit(ttt,"@")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
ttt <- ttt[,2]
ttt <- as.character(ttt)
ttt <- strsplit(ttt,"X")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
dimnames(ttt)[[1]] <- NULL
plot.bulb.locations.AZ99 <- ttt
ttt <- dimnames(final.Bulb.Locations.H190)[[1]]
ttt <- strsplit(ttt,"@")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
ttt <- ttt[,2]
ttt <- as.character(ttt)
ttt <- strsplit(ttt,"X")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
dimnames(ttt)[[1]] <- NULL
plot.bulb.locations.ControlH190 <- ttt
ttt <- dimnames(final.Bulb.Locations.H250)[[1]]
ttt <- strsplit(ttt,"@")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
ttt <- ttt[,2]
ttt <- as.character(ttt)
ttt <- strsplit(ttt,"X")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
dimnames(ttt)[[1]] <- NULL
plot.bulb.locations.ControlH250 <- ttt
ttt <- dimnames(final.Bulb.Locations.H251)[[1]]
ttt <- strsplit(ttt,"@")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
ttt <- ttt[,2]
ttt <- as.character(ttt)
ttt <- strsplit(ttt,"X")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
dimnames(ttt)[[1]] <- NULL
plot.bulb.locations.AZH251 <- ttt
ttt <- dimnames(final.Bulb.Locations.OFB57)[[1]]
ttt <- strsplit(ttt,"@")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
ttt <- ttt[,2]
ttt <- as.character(ttt)
ttt <- strsplit(ttt,"X")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
dimnames(ttt)[[1]] <- NULL
plot.bulb.locations.ControlOFB57 <- ttt
ttt <- dimnames(final.Bulb.Locations.OFB6a)[[1]]
ttt <- strsplit(ttt,"@")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
ttt <- ttt[,2]
ttt <- as.character(ttt)
ttt <- strsplit(ttt,"X")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
dimnames(ttt)[[1]] <- NULL
plot.bulb.locations.ControlOFB6a <- ttt
ttt <- dimnames(final.Bulb.Locations.PD52)[[1]]
ttt <- strsplit(ttt,"@")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
ttt <- ttt[,2]
ttt <- as.character(ttt)
ttt <- strsplit(ttt,"X")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
dimnames(ttt)[[1]] <- NULL
plot.bulb.locations.PD52 <- ttt
ttt <- dimnames(final.Bulb.Locations.PD56)[[1]]
ttt <- strsplit(ttt,"@")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
ttt <- ttt[,2]
ttt <- as.character(ttt)
ttt <- strsplit(ttt,"X")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
dimnames(ttt)[[1]] <- NULL
plot.bulb.locations.PD56 <- ttt
ttt <- dimnames(final.Bulb.Locations.PD58)[[1]]
ttt <- strsplit(ttt,"@")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
ttt <- ttt[,2]
ttt <- as.character(ttt)
ttt <- strsplit(ttt,"X")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
dimnames(ttt)[[1]] <- NULL
plot.bulb.locations.PD58 <- ttt
ttt <- dimnames(final.Bulb.Locations.PD77)[[1]]
ttt <- strsplit(ttt,"@")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
ttt <- ttt[,2]
ttt <- as.character(ttt)
ttt <- strsplit(ttt,"X")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
dimnames(ttt)[[1]] <- NULL
plot.bulb.locations.PD77 <- ttt
ttt <- dimnames(final.Bulb.Locations.PD79)[[1]]
ttt <- strsplit(ttt,"@")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
ttt <- ttt[,2]
ttt <- as.character(ttt)
ttt <- strsplit(ttt,"X")
ttt <- as.data.frame(ttt)
ttt <- t(ttt)
dimnames(ttt)[[1]] <- NULL
plot.bulb.locations.PD79 <- ttt
write.table(plot.bulb.locations.AZ109,"plot.bulb.locations.AZ109.txt",sep="\t")
write.table(plot.bulb.locations.AZ84,"plot.bulb.locations.AZ84.txt",sep="\t")
write.table(plot.bulb.locations.AZ90,"plot.bulb.locations.AZ90.txt",sep="\t")
write.table(plot.bulb.locations.AZ99,"plot.bulb.locations.AZ99.txt",sep="\t")
write.table(plot.bulb.locations.AZH251,"plot.bulb.locations.AZH251.txt",sep="\t")
write.table(plot.bulb.locations.ControlH190,"plot.bulb.locations.ControlH190.txt",sep="\t")
write.table(plot.bulb.locations.ControlH250,"plot.bulb.locations.ControlH250.txt",sep="\t")
write.table(plot.bulb.locations.ControlOFB57,"plot.bulb.locations.ControlOFB57.txt",sep="\t")
write.table(plot.bulb.locations.ControlOFB6a,"plot.bulb.locations.ControlOFB6a.txt",sep="\t")
write.table(plot.bulb.locations.PD52,"plot.bulb.locations.PD52.txt",sep="\t")
write.table(plot.bulb.locations.PD56,"plot.bulb.locations.PD56.txt",sep="\t")
write.table(plot.bulb.locations.PD58,"plot.bulb.locations.PD58.txt",sep="\t")
write.table(plot.bulb.locations.PD77,"plot.bulb.locations.PD77.txt",sep="\t")
write.table(plot.bulb.locations.PD79,"plot.bulb.locations.PD79.txt",sep="\t")
plot.bulb.locations.AZ109 <- read.table("plot.bulb.locations.AZ109.txt")
plot.bulb.locations.AZ84 <- read.table("plot.bulb.locations.AZ84.txt")
plot.bulb.locations.AZ90 <- read.table("plot.bulb.locations.AZ90.txt")
plot.bulb.locations.AZ99 <- read.table("plot.bulb.locations.AZ99.txt")
plot.bulb.locations.AZH251 <- read.table("plot.bulb.locations.AZH251.txt")
plot.bulb.locations.ControlH190 <- read.table("plot.bulb.locations.ControlH190.txt")
plot.bulb.locations.ControlH250 <- read.table("plot.bulb.locations.ControlH250.txt")
plot.bulb.locations.ControlOFB57 <- read.table("plot.bulb.locations.ControlOFB57.txt")
plot.bulb.locations.ControlOFB6a <- read.table("plot.bulb.locations.ControlOFB6a.txt")
plot.bulb.locations.PD52 <- read.table("plot.bulb.locations.PD52.txt")
plot.bulb.locations.PD56 <- read.table("plot.bulb.locations.PD56.txt")
plot.bulb.locations.PD58 <- read.table("plot.bulb.locations.PD58.txt")
plot.bulb.locations.PD77 <- read.table("plot.bulb.locations.PD77.txt")
plot.bulb.locations.PD79 <- read.table("plot.bulb.locations.PD79.txt")
omp.hits <- c("0_0_0_0_0_0_1_0_0_0_0_0_0_0_0_0_1_0_0_0_0_0_0_0_1",
"0_0_1_0_0_0_1_0_0_0_0_0_0_0_0_0_1_0_0_0_0_0_0_0_0",
"0_0_0_0_0_0_0_1_1_1_0_0_0_0_0_0_1_0_0_0_0_0_0_0_0",
"0_0_0_0_0_0_1_0_0_0_0_0_0_0_0_0_1_0_0_0_0_0_1_0_0",
"0_0_0_0_0_0_1_0_0_0_0_0_0_0_0_0_1_1_0_0_0_0_0_0_1",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_1_0_0_0_0_0_0_0",
"0_0_0_0_0_0_0_0_0_1_0_0_1_0_0_0_1_1_1_0_0_0_1_0_1",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_0_1_0_1_0_1",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_1_1_1_0_1_0_0_0_1",
"0_0_0_0_0_0_0_0_0_1_0_0_1_0_0_0_1_1_1_0_0_0_0_0_1",
"0_0_0_0_0_0_0_0_1_0_0_0_0_0_0_0_1_1_1_1_0_0_0_0_1",
"0_0_0_0_0_0_0_0_1_1_0_0_0_0_0_0_1_1_1_1_0_0_0_0_1",
"0_0_0_0_0_0_0_1_1_1_0_0_1_0_0_0_1_1_1_1_0_0_1_0_1",
"0_0_0_1_0_0_0_0_0_0_0_0_0_0_0_0_1_1_1_0_0_0_1_0_1",
"0_0_0_1_0_0_0_0_0_0_0_0_1_0_0_0_1_1_1_1_0_0_1_0_1",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_1_0_0_1_0_1",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_1_1_0_1_0_0_0_1",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_1_1_1_0_0_0_0_1",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_1_1_1_0_0_1_0_1",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_0_1_1_1_1_0_0_0_0_1",
"0_0_0_0_0_0_0_0_0_1_0_0_1_0_0_0_1_1_1_1_0_0_1_0_1",
"0_0_0_0_0_0_0_0_1_0_0_0_0_0_0_0_1_1_1_1_0_0_1_0_1",
"0_0_0_0_0_0_0_1_0_1_0_0_0_0_0_0_1_0_0_1_0_0_0_0_1",
"0_0_0_0_0_0_0_1_0_1_0_0_1_0_0_0_1_1_1_0_0_0_1_0_1",
"0_0_0_1_0_0_0_0_0_0_0_0_0_0_0_0_1_1_1_0_1_0_1_0_1",
"0_0_0_1_0_0_0_0_0_0_0_0_1_0_0_0_1_1_1_0_0_0_1_0_1",
"0_0_0_1_0_0_0_0_0_0_0_0_1_0_0_0_1_1_1_0_1_0_1_0_1",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_1_0_0_0_0_1_0_1",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_1_0_0_0_0_0_0_1",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0_1_1_0_0_1_0_1_0_1",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0_1_1_0_1_1_0_0_0_1",
"0_0_0_0_0_0_0_0_0_1_0_0_0_0_0_0_1_0_0_0_0_0_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0_1_0_0_0_1_0_0_0_1",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0_1_0_0_0_0_0_0_0_0",
"0_0_0_0_0_0_1_0_1_0_0_0_0_0_0_0_1_0_0_0_0_0_0_0_1",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_1_1_0_1_0_1_0_1",
"0_0_0_0_0_0_0_1_1_1_0_0_1_0_0_0_1_0_0_0_0_0_1_0_1",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_1_0_1_0_0_1_0_1",
"0_0_0_0_0_0_0_1_0_1_0_0_1_0_0_0_1_1_1_1_0_0_1_0_1",
"0_0_0_0_0_0_1_0_1_0_0_0_1_0_0_0_1_0_0_0_0_0_1_0_0",
"0_0_0_0_1_0_0_0_1_0_0_0_0_0_0_0_1_0_0_1_0_0_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_1_0_0_0_0_0_1_0",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_0_1_1_0_1_1_0_0_0_1",
"0_0_0_0_0_0_0_0_0_1_0_0_0_0_0_0_1_1_0_0_0_0_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0_1_0_0_0_1_0_1_0_1",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0_1_1_0_0_1_0_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_1_1_1_0_0_1_0_0_0_0",
"0_0_0_0_0_0_0_0_1_0_1_0_1_0_0_0_1_1_1_0_0_0_1_0_1",
"0_0_0_0_0_0_0_0_1_1_0_0_0_0_0_0_1_0_0_0_0_0_0_0_0",
"0_0_0_0_0_0_0_1_1_1_0_0_1_0_0_1_1_1_0_0_0_0_0_0_1",
"0_0_0_0_0_0_1_0_1_0_0_0_1_0_0_0_1_1_0_1_0_0_0_1_1",
"0_0_0_0_1_0_0_1_0_1_0_0_1_0_0_0_1_1_1_0_0_0_1_0_1",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0_1_0_0_1_1_0_0_0_1",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0_1_0_0_1_1_0_1_0_1",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0_1_1_1_0_1_0_0_0_1",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_1_0_1_1_1_0_0_0_1_0_1",
"0_0_0_0_0_0_0_1_0_1_0_0_0_0_0_0_1_1_0_0_0_0_0_0_0",
"0_0_1_0_0_0_1_0_0_0_0_0_0_0_0_0_1_0_0_0_0_0_0_1_0",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_0_1_1_1_1_0_0_1_0_1",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_0_1_1_1_1_1_0_1_0_1")
omp.hits <- sort(omp.hits)
temp1 <- strsplit(omp.hits,"_")
temp2 <- as.data.frame(temp1)
dimnames(temp2)[[2]] <- omp.hits
dimnames(temp2)[[1]] <- c("ALPHA_SYNUCLEIN",
"BETA_AMYLOID",
"CALBINDIN",
"CD31",
"CH_NEUN",
"CNPASE",
"COLLAGEN_IV",
"DAPI",
"GFAP",
"HISTONES",
"HLADR",
"IBA1",
"MAP2",
"MBP",
"NEURO_HEAVY",
"NEURO_LIGHT",
"OMP",
"PGP9_5",
"RB_CALRETININ",
"S100",
"SYNAPTOPHYSIN",
"TAU",
"TOMATO_LECTIN",
"TYR_HYDROXYLASE",
"UEA_LECTIN")
temp3 <- matrix(as.numeric(unlist(temp2)),dim(temp2)[1],dim(temp2)[2])
temp3 <- ifelse(temp3==1,0,1)
dimnames(temp3) <- dimnames(temp2)
col.2.use <- rainbow(length(omp.hits))
hhh <- heatmap.2(as.matrix(temp3),density.info="none",labRow=dimnames(temp3)[[1]],labCol="",scale="none",Rowv=TRUE,Colv=TRUE,col=c(0,1),ColSideColors=col.2.use,key=FALSE,trace="both",linecol=NULL,tracecol="gray",margins = c(5, 10))
temp <- hhh[2][[1]]
names(temp) <- col.2.use
temp <- sort(temp)
col.2.use <- names(temp)
temp <- hhh[2][[1]]
names(temp) <- c(1:60)
temp <- sort(temp)
combo <- names(temp)
hhh <- heatmap.2(as.matrix(temp3),density.info="none",labCol=combo,labRow=dimnames(temp3)[[1]],scale="none",Rowv=TRUE,Colv=TRUE,col=c(0,1),ColSideColors=col.2.use,key=FALSE,trace="both",linecol=NULL,tracecol="gray",margins = c(5, 10),xlab="Label Combinations (OMP+, Kruskal-Wallis P < 0.05)",adjCol = c(NA,0.4))
#tiff("Omp_String_HeatMap_20200930.tiff",res=300,width = 6, height = 3, units = "in")
svg("Omp_String_HeatMap_20200930.svg",width = 12, height = 5)
hhh <- heatmap.2(as.matrix(temp3),density.info="none",labCol=combo,labRow=dimnames(temp3)[[1]],scale="none",Rowv=TRUE,Colv=TRUE,col=c(0,1),ColSideColors=col.2.use,key=FALSE,trace="both",linecol=NULL,tracecol="gray",margins = c(5, 10),xlab="Label Combinations (OMP+, Kruskal-Wallis P < 0.05)",adjCol = c(NA,0.4))
dev.off()
omp.hits <- dimnames(temp3)[[2]][hhh[[2]]]
col.2.use <- rainbow(length(omp.hits))
for (t in 1:length(omp.hits)) {
print(t)
print(length(omp.hits))
temp <- omp.hits[t]
temp <- working[working==temp]
temp <- names(temp)
temptemp <- strsplit(temp,"_Pos_")
temptemp <- as.data.frame(temptemp)
temptemp <- t(temptemp)
dimnames(temptemp)[[1]] <- temp
temp <- as.character(temptemp[,2])
temp <- strsplit(temp,"X")
temp <- as.data.frame(temp)
temp <- t(temp)
temptemp <- cbind(temptemp,temp)
dimnames(temptemp)[[2]] <- c("Sample","Location","Xpos","Ypos")
#pdf(paste(c("OMP_",omp.hits[t],".pdf"),collapse="",sep=""),width = 28950/1000, height = 6030/1000)
pdf(paste(c("OMP_String_Combination_",t,".pdf"),collapse="",sep=""),width = 28950/1000, height = 6030/1000)
# AZ_109_146
new.x <- as.numeric(unlist(plot.bulb.locations.AZ109[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ109[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_109_146")
if(length(grep("AZ_109_146",temptemp[,1]))>0) {
if(length(grep("AZ_109_146",temptemp[,1]))==1) {
temp <- temptemp[grep("AZ_109_146",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("AZ_109_146",temptemp[,1]))>1) {
temp <- temptemp[grep("AZ_109_146",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# AZ_84_145
new.x <- as.numeric(unlist(plot.bulb.locations.AZ84[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ84[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_84_145")
if(length(grep("AZ_84_145",temptemp[,1]))>0) {
if(length(grep("AZ_84_145",temptemp[,1]))==1) {
temp <- temptemp[grep("AZ_84_145",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("AZ_84_145",temptemp[,1]))>1) {
temp <- temptemp[grep("AZ_84_145",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# AZ_90_242
new.x <- as.numeric(unlist(plot.bulb.locations.AZ90[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ90[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_90_242")
if(length(grep("AZ_90_242",temptemp[,1]))>0) {
if(length(grep("AZ_90_242",temptemp[,1]))==1) {
temp <- temptemp[grep("AZ_90_242",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("AZ_90_242",temptemp[,1]))>1) {
temp <- temptemp[grep("AZ_90_242",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# AZ_99_140
new.x <- as.numeric(unlist(plot.bulb.locations.AZ99[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ99[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_99_140")
if(length(grep("AZ_99_140",temptemp[,1]))>0) {
if(length(grep("AZ_99_140",temptemp[,1]))==1) {
temp <- temptemp[grep("AZ_99_140",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("AZ_99_140",temptemp[,1]))>1) {
temp <- temptemp[grep("AZ_99_140",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# AZ_251_146
new.x <- as.numeric(unlist(plot.bulb.locations.AZH251[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZH251[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_251_146")
if(length(grep("AZ_251_146",temptemp[,1]))>0) {
if(length(grep("AZ_251_146",temptemp[,1]))==1) {
temp <- temptemp[grep("AZ_251_146",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("AZ_251_146",temptemp[,1]))>1) {
temp <- temptemp[grep("AZ_251_146",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# Control_H190_260
new.x <- as.numeric(unlist(plot.bulb.locations.ControlH190[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlH190[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_H190_260")
if(length(grep("Control_H190_260",temptemp[,1]))>0) {
if(length(grep("Control_H190_260",temptemp[,1]))==1) {
temp <- temptemp[grep("Control_H190_260",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("Control_H190_260",temptemp[,1]))>1) {
temp <- temptemp[grep("Control_H190_260",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# Control_H250_185
new.x <- as.numeric(unlist(plot.bulb.locations.ControlH250[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlH250[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_H250_185")
if(length(grep("Control_H250_185",temptemp[,1]))>0) {
if(length(grep("Control_H250_185",temptemp[,1]))==1) {
temp <- temptemp[grep("Control_H250_185",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("Control_H250_185",temptemp[,1]))>1) {
temp <- temptemp[grep("Control_H250_185",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# Control_OFB57_194
new.x <- as.numeric(unlist(plot.bulb.locations.ControlOFB57[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlOFB57[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_OFB57_194")
if(length(grep("Control_OFB57_194",temptemp[,1]))>0) {
if(length(grep("Control_OFB57_194",temptemp[,1]))==1) {
temp <- temptemp[grep("Control_OFB57_194",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("Control_OFB57_194",temptemp[,1]))>1) {
temp <- temptemp[grep("Control_OFB57_194",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# Control_OFB6A_107
new.x <- as.numeric(unlist(plot.bulb.locations.ControlOFB6a[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlOFB6a[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_OFB6A_107")
if(length(grep("Control_OFB6A_107",temptemp[,1]))>0) {
if(length(grep("Control_OFB6A_107",temptemp[,1]))==1) {
temp <- temptemp[grep("Control_OFB6A_107",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("Control_OFB6A_107",temptemp[,1]))>1) {
temp <- temptemp[grep("Control_OFB6A_107",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# PD_52_247
new.x <- as.numeric(unlist(plot.bulb.locations.PD52[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD52[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_52_247")
if(length(grep("PD_52_247",temptemp[,1]))>0) {
if(length(grep("PD_52_247",temptemp[,1]))==1) {
temp <- temptemp[grep("PD_52_247",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("PD_52_247",temptemp[,1]))>1) {
temp <- temptemp[grep("PD_52_247",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# PD_56_236
new.x <- as.numeric(unlist(plot.bulb.locations.PD56[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD56[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_56_236")
if(length(grep("PD_56_236",temptemp[,1]))>0) {
if(length(grep("PD_56_236",temptemp[,1]))==1) {
temp <- temptemp[grep("PD_56_236",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("PD_56_236",temptemp[,1]))>1) {
temp <- temptemp[grep("PD_56_236",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# PD_58_22
new.x <- as.numeric(unlist(plot.bulb.locations.PD58[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD58[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_58_22")
if(length(grep("PD_58_22",temptemp[,1]))>0) {
if(length(grep("PD_58_22",temptemp[,1]))==1) {
temp <- temptemp[grep("PD_58_22",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("PD_58_22",temptemp[,1]))>1) {
temp <- temptemp[grep("PD_58_22",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# PD_77_197
new.x <- as.numeric(unlist(plot.bulb.locations.PD77[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD77[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_77_197")
if(length(grep("PD_77_197",temptemp[,1]))>0) {
if(length(grep("PD_77_197",temptemp[,1]))==1) {
temp <- temptemp[grep("PD_77_197",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("PD_77_197",temptemp[,1]))>1) {
temp <- temptemp[grep("PD_77_197",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# PD_79_201
new.x <- as.numeric(unlist(plot.bulb.locations.PD79[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD79[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_79_201")
if(length(grep("PD_79_201",temptemp[,1]))>0) {
if(length(grep("PD_79_201",temptemp[,1]))==1) {
temp <- temptemp[grep("PD_79_201",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("PD_79_201",temptemp[,1]))>1) {
temp <- temptemp[grep("PD_79_201",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
dev.off()
}
omp.catch.all <- NULL
for (t in 1:length(omp.hits)) {
print(t)
print(length(omp.hits))
temp <- omp.hits[t]
temp <- working[working==temp]
temp <- names(temp)
temptemp <- strsplit(temp,"_Pos_")
temptemp <- as.data.frame(temptemp)
temptemp <- t(temptemp)
dimnames(temptemp)[[1]] <- temp
temp <- as.character(temptemp[,2])
temp <- strsplit(temp,"X")
temp <- as.data.frame(temp)
temp <- t(temp)
temptemp <- cbind(temptemp,temp)
temptemp <- cbind(temptemp,col.2.use[t])
dimnames(temptemp)[[2]] <- c("Sample","Location","Xpos","Ypos","Color")
omp.catch.all <- rbind(omp.catch.all,temptemp)
}
pdf("OMP_All_String_Hits.pdf",width = 28950/1000, height = 6030/1000)
# AZ_109_146
new.x <- as.numeric(unlist(plot.bulb.locations.AZ109[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ109[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_109_146")
if(length(grep("AZ_109_146",omp.catch.all[,1]))>0) {
if(length(grep("AZ_109_146",omp.catch.all[,1]))==1) {
temp <- omp.catch.all[grep("AZ_109_146",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("AZ_109_146",omp.catch.all[,1]))>1) {
temp <- omp.catch.all[grep("AZ_109_146",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# AZ_84_145
new.x <- as.numeric(unlist(plot.bulb.locations.AZ84[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ84[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_84_145")
if(length(grep("AZ_84_145",omp.catch.all[,1]))>0) {
if(length(grep("AZ_84_145",omp.catch.all[,1]))==1) {
temp <- omp.catch.all[grep("AZ_84_145",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("AZ_84_145",omp.catch.all[,1]))>1) {
temp <- omp.catch.all[grep("AZ_84_145",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# AZ_90_242
new.x <- as.numeric(unlist(plot.bulb.locations.AZ90[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ90[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_90_242")
if(length(grep("AZ_90_242",omp.catch.all[,1]))>0) {
if(length(grep("AZ_90_242",omp.catch.all[,1]))==1) {
temp <- omp.catch.all[grep("AZ_90_242",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("AZ_90_242",omp.catch.all[,1]))>1) {
temp <- omp.catch.all[grep("AZ_90_242",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# AZ_99_140
new.x <- as.numeric(unlist(plot.bulb.locations.AZ99[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ99[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_99_140")
if(length(grep("AZ_99_140",omp.catch.all[,1]))>0) {
if(length(grep("AZ_99_140",omp.catch.all[,1]))==1) {
temp <- omp.catch.all[grep("AZ_99_140",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("AZ_99_140",omp.catch.all[,1]))>1) {
temp <- omp.catch.all[grep("AZ_99_140",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# AZ_251_146
new.x <- as.numeric(unlist(plot.bulb.locations.AZH251[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZH251[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_251_146")
if(length(grep("AZ_251_146",omp.catch.all[,1]))>0) {
if(length(grep("AZ_251_146",omp.catch.all[,1]))==1) {
temp <- omp.catch.all[grep("AZ_251_146",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("AZ_251_146",omp.catch.all[,1]))>1) {
temp <- omp.catch.all[grep("AZ_251_146",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# Control_H190_260
new.x <- as.numeric(unlist(plot.bulb.locations.ControlH190[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlH190[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_H190_260")
if(length(grep("Control_H190_260",omp.catch.all[,1]))>0) {
if(length(grep("Control_H190_260",omp.catch.all[,1]))==1) {
temp <- omp.catch.all[grep("Control_H190_260",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("Control_H190_260",omp.catch.all[,1]))>1) {
temp <- omp.catch.all[grep("Control_H190_260",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# Control_H250_185
new.x <- as.numeric(unlist(plot.bulb.locations.ControlH250[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlH250[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_H250_185")
if(length(grep("Control_H250_185",omp.catch.all[,1]))>0) {
if(length(grep("Control_H250_185",omp.catch.all[,1]))==1) {
temp <- omp.catch.all[grep("Control_H250_185",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("Control_H250_185",omp.catch.all[,1]))>1) {
temp <- omp.catch.all[grep("Control_H250_185",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# Control_OFB57_194
new.x <- as.numeric(unlist(plot.bulb.locations.ControlOFB57[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlOFB57[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_OFB57_194")
if(length(grep("Control_OFB57_194",omp.catch.all[,1]))>0) {
if(length(grep("Control_OFB57_194",omp.catch.all[,1]))==1) {
temp <- omp.catch.all[grep("Control_OFB57_194",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("Control_OFB57_194",omp.catch.all[,1]))>1) {
temp <- omp.catch.all[grep("Control_OFB57_194",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# Control_OFB6A_107
new.x <- as.numeric(unlist(plot.bulb.locations.ControlOFB6a[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlOFB6a[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_OFB6A_107")
if(length(grep("Control_OFB6A_107",omp.catch.all[,1]))>0) {
if(length(grep("Control_OFB6A_107",omp.catch.all[,1]))==1) {
temp <- omp.catch.all[grep("Control_OFB6A_107",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("Control_OFB6A_107",omp.catch.all[,1]))>1) {
temp <- omp.catch.all[grep("Control_OFB6A_107",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# PD_52_247
new.x <- as.numeric(unlist(plot.bulb.locations.PD52[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD52[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_52_247")
if(length(grep("PD_52_247",omp.catch.all[,1]))>0) {
if(length(grep("PD_52_247",omp.catch.all[,1]))==1) {
temp <- omp.catch.all[grep("PD_52_247",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("PD_52_247",omp.catch.all[,1]))>1) {
temp <- omp.catch.all[grep("PD_52_247",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# PD_56_236
new.x <- as.numeric(unlist(plot.bulb.locations.PD56[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD56[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_56_236")
if(length(grep("PD_56_236",omp.catch.all[,1]))>0) {
if(length(grep("PD_56_236",omp.catch.all[,1]))==1) {
temp <- omp.catch.all[grep("PD_56_236",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("PD_56_236",omp.catch.all[,1]))>1) {
temp <- omp.catch.all[grep("PD_56_236",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# PD_58_22
new.x <- as.numeric(unlist(plot.bulb.locations.PD58[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD58[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_58_22")
if(length(grep("PD_58_22",omp.catch.all[,1]))>0) {
if(length(grep("PD_58_22",omp.catch.all[,1]))==1) {
temp <- omp.catch.all[grep("PD_58_22",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("PD_58_22",omp.catch.all[,1]))>1) {
temp <- omp.catch.all[grep("PD_58_22",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# PD_77_197
new.x <- as.numeric(unlist(plot.bulb.locations.PD77[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD77[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_77_197")
if(length(grep("PD_77_197",omp.catch.all[,1]))>0) {
if(length(grep("PD_77_197",omp.catch.all[,1]))==1) {
temp <- omp.catch.all[grep("PD_77_197",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("PD_77_197",omp.catch.all[,1]))>1) {
temp <- omp.catch.all[grep("PD_77_197",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# PD_79_201
new.x <- as.numeric(unlist(plot.bulb.locations.PD79[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD79[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_79_201")
if(length(grep("PD_79_201",omp.catch.all[,1]))>0) {
if(length(grep("PD_79_201",omp.catch.all[,1]))==1) {
temp <- omp.catch.all[grep("PD_79_201",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("PD_79_201",omp.catch.all[,1]))>1) {
temp <- omp.catch.all[grep("PD_79_201",omp.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
dev.off()
tau.hits <- c("0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0_0_1_0_0_1_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_0_0_1_0_0_0_1_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_1_0_1_0_0_1_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_0_0_1_0_0_1_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_1_0_0_0_0_0_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_1_0_1_0_0_0_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_1_0_1_0_0_1_1_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_1_0_1_0_0_0_1_0_0_0",
"0_0_0_0_1_0_0_0_0_0_0_0_1_0_0_1_0_1_0_0_0_1_0_0_0",
"0_0_1_0_1_0_0_0_0_0_0_0_1_0_0_1_0_1_0_0_1_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_0_0_0_0_0_0_1_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0_0_1_0_0_0_1_0_0_0",
"0_0_1_0_0_0_0_0_0_0_0_0_1_0_0_1_0_1_0_0_1_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_1_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_0_1_0_0_0_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_0_1_0_0_1_1_0_0_0",
"0_0_0_0_0_0_0_0_0_1_0_0_1_0_0_1_0_1_0_0_0_1_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_0_0_0_0_0_1_0_0_1_1_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_0_0_0_1_0_1_0_0_0_1_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_0_0_0_1_0_1_0_0_1_1_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0_0_0_0_0_0_1_0_0_0",
"0_0_1_0_0_0_0_0_0_0_0_0_1_0_0_1_0_1_0_0_0_1_0_0_0",
"0_0_1_0_1_0_0_0_0_0_0_0_1_0_0_1_0_1_0_0_0_1_0_0_0")
tau.hits <- sort(tau.hits)
temp1 <- strsplit(tau.hits,"_")
temp2 <- as.data.frame(temp1)
dimnames(temp2)[[2]] <- tau.hits
dimnames(temp2)[[1]] <- c("ALPHA_SYNUCLEIN",
"BETA_AMYLOID",
"CALBINDIN",
"CD31",
"CH_NEUN",
"CNPASE",
"COLLAGEN_IV",
"DAPI",
"GFAP",
"HISTONES",
"HLADR",
"IBA1",
"MAP2",
"MBP",
"NEURO_HEAVY",
"NEURO_LIGHT",
"OMP",
"PGP9_5",
"RB_CALRETININ",
"S100",
"SYNAPTOPHYSIN",
"TAU",
"TOMATO_LECTIN",
"TYR_HYDROXYLASE",
"UEA_LECTIN")
temp3 <- matrix(as.numeric(unlist(temp2)),dim(temp2)[1],dim(temp2)[2])
temp3 <- ifelse(temp3==1,0,1)
dimnames(temp3) <- dimnames(temp2)
col.2.use <- rainbow(length(tau.hits))
hhh <- heatmap.2(as.matrix(temp3),density.info="none",labRow=dimnames(temp3)[[1]],labCol="",scale="none",Rowv=TRUE,Colv=TRUE,col=c(0,1),ColSideColors=col.2.use,key=FALSE,trace="both",linecol=NULL,tracecol="gray",margins = c(5, 10))
temp <- hhh[2][[1]]
names(temp) <- col.2.use
temp <- sort(temp)
col.2.use <- names(temp)
temp <- hhh[2][[1]]
names(temp) <- c(1:26)
temp <- sort(temp)
combo <- names(temp)
hhh <- heatmap.2(as.matrix(temp3),density.info="none",labCol=combo,labRow=dimnames(temp3)[[1]],scale="none",Rowv=TRUE,Colv=TRUE,col=c(0,1),ColSideColors=col.2.use,key=FALSE,trace="both",linecol=NULL,tracecol="gray",margins = c(5, 10),xlab="Label Combinations (TAU+, Kruskal-Wallis P < 0.05)",adjCol = c(NA,0.4))
#tiff("TAU_String_HeatMap_20200930.tiff",res=300,width = 6, height = 3, units = "in")
svg("TAU_String_HeatMap_20200930.svg",width = 12, height = 5)
hhh <- heatmap.2(as.matrix(temp3),density.info="none",labCol=combo,labRow=dimnames(temp3)[[1]],scale="none",Rowv=TRUE,Colv=TRUE,col=c(0,1),ColSideColors=col.2.use,key=FALSE,trace="both",linecol=NULL,tracecol="gray",margins = c(5, 10),xlab="Label Combinations (TAU+, Kruskal-Wallis P < 0.05)",adjCol = c(NA,0.4))
dev.off()
tau.hits <- dimnames(temp3)[[2]][hhh[[2]]]
col.2.use <- rainbow(length(tau.hits))
for (t in 1:length(tau.hits)) {
print(t)
print(length(tau.hits))
temp <- tau.hits[t]
temp <- working[working==temp]
temp <- names(temp)
temptemp <- strsplit(temp,"_Pos_")
temptemp <- as.data.frame(temptemp)
temptemp <- t(temptemp)
dimnames(temptemp)[[1]] <- temp
temp <- as.character(temptemp[,2])
temp <- strsplit(temp,"X")
temp <- as.data.frame(temp)
temp <- t(temp)
temptemp <- cbind(temptemp,temp)
dimnames(temptemp)[[2]] <- c("Sample","Location","Xpos","Ypos")
#pdf(paste(c("TAU_",tau.hits[t],".pdf"),collapse="",sep=""),width = 28950/1000, height = 6030/1000)
pdf(paste(c("TAU_String_Combination_",t,".pdf"),collapse="",sep=""),width = 28950/1000, height = 6030/1000)
# AZ_109_146
new.x <- as.numeric(unlist(plot.bulb.locations.AZ109[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ109[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_109_146")
if(length(grep("AZ_109_146",temptemp[,1]))>0) {
if(length(grep("AZ_109_146",temptemp[,1]))==1) {
temp <- temptemp[grep("AZ_109_146",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("AZ_109_146",temptemp[,1]))>1) {
temp <- temptemp[grep("AZ_109_146",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# AZ_84_145
new.x <- as.numeric(unlist(plot.bulb.locations.AZ84[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ84[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_84_145")
if(length(grep("AZ_84_145",temptemp[,1]))>0) {
if(length(grep("AZ_84_145",temptemp[,1]))==1) {
temp <- temptemp[grep("AZ_84_145",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("AZ_84_145",temptemp[,1]))>1) {
temp <- temptemp[grep("AZ_84_145",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# AZ_90_242
new.x <- as.numeric(unlist(plot.bulb.locations.AZ90[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ90[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_90_242")
if(length(grep("AZ_90_242",temptemp[,1]))>0) {
if(length(grep("AZ_90_242",temptemp[,1]))==1) {
temp <- temptemp[grep("AZ_90_242",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("AZ_90_242",temptemp[,1]))>1) {
temp <- temptemp[grep("AZ_90_242",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# AZ_99_140
new.x <- as.numeric(unlist(plot.bulb.locations.AZ99[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ99[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_99_140")
if(length(grep("AZ_99_140",temptemp[,1]))>0) {
if(length(grep("AZ_99_140",temptemp[,1]))==1) {
temp <- temptemp[grep("AZ_99_140",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("AZ_99_140",temptemp[,1]))>1) {
temp <- temptemp[grep("AZ_99_140",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# AZ_251_146
new.x <- as.numeric(unlist(plot.bulb.locations.AZH251[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZH251[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_251_146")
if(length(grep("AZ_251_146",temptemp[,1]))>0) {
if(length(grep("AZ_251_146",temptemp[,1]))==1) {
temp <- temptemp[grep("AZ_251_146",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("AZ_251_146",temptemp[,1]))>1) {
temp <- temptemp[grep("AZ_251_146",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# Control_H190_260
new.x <- as.numeric(unlist(plot.bulb.locations.ControlH190[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlH190[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_H190_260")
if(length(grep("Control_H190_260",temptemp[,1]))>0) {
if(length(grep("Control_H190_260",temptemp[,1]))==1) {
temp <- temptemp[grep("Control_H190_260",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("Control_H190_260",temptemp[,1]))>1) {
temp <- temptemp[grep("Control_H190_260",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# Control_H250_185
new.x <- as.numeric(unlist(plot.bulb.locations.ControlH250[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlH250[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_H250_185")
if(length(grep("Control_H250_185",temptemp[,1]))>0) {
if(length(grep("Control_H250_185",temptemp[,1]))==1) {
temp <- temptemp[grep("Control_H250_185",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("Control_H250_185",temptemp[,1]))>1) {
temp <- temptemp[grep("Control_H250_185",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# Control_OFB57_194
new.x <- as.numeric(unlist(plot.bulb.locations.ControlOFB57[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlOFB57[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_OFB57_194")
if(length(grep("Control_OFB57_194",temptemp[,1]))>0) {
if(length(grep("Control_OFB57_194",temptemp[,1]))==1) {
temp <- temptemp[grep("Control_OFB57_194",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("Control_OFB57_194",temptemp[,1]))>1) {
temp <- temptemp[grep("Control_OFB57_194",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# Control_OFB6A_107
new.x <- as.numeric(unlist(plot.bulb.locations.ControlOFB6a[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlOFB6a[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_OFB6A_107")
if(length(grep("Control_OFB6A_107",temptemp[,1]))>0) {
if(length(grep("Control_OFB6A_107",temptemp[,1]))==1) {
temp <- temptemp[grep("Control_OFB6A_107",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("Control_OFB6A_107",temptemp[,1]))>1) {
temp <- temptemp[grep("Control_OFB6A_107",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# PD_52_247
new.x <- as.numeric(unlist(plot.bulb.locations.PD52[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD52[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_52_247")
if(length(grep("PD_52_247",temptemp[,1]))>0) {
if(length(grep("PD_52_247",temptemp[,1]))==1) {
temp <- temptemp[grep("PD_52_247",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("PD_52_247",temptemp[,1]))>1) {
temp <- temptemp[grep("PD_52_247",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# PD_56_236
new.x <- as.numeric(unlist(plot.bulb.locations.PD56[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD56[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_56_236")
if(length(grep("PD_56_236",temptemp[,1]))>0) {
if(length(grep("PD_56_236",temptemp[,1]))==1) {
temp <- temptemp[grep("PD_56_236",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("PD_56_236",temptemp[,1]))>1) {
temp <- temptemp[grep("PD_56_236",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# PD_58_22
new.x <- as.numeric(unlist(plot.bulb.locations.PD58[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD58[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_58_22")
if(length(grep("PD_58_22",temptemp[,1]))>0) {
if(length(grep("PD_58_22",temptemp[,1]))==1) {
temp <- temptemp[grep("PD_58_22",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("PD_58_22",temptemp[,1]))>1) {
temp <- temptemp[grep("PD_58_22",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# PD_77_197
new.x <- as.numeric(unlist(plot.bulb.locations.PD77[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD77[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_77_197")
if(length(grep("PD_77_197",temptemp[,1]))>0) {
if(length(grep("PD_77_197",temptemp[,1]))==1) {
temp <- temptemp[grep("PD_77_197",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("PD_77_197",temptemp[,1]))>1) {
temp <- temptemp[grep("PD_77_197",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
# PD_79_201
new.x <- as.numeric(unlist(plot.bulb.locations.PD79[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD79[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_79_201")
if(length(grep("PD_79_201",temptemp[,1]))>0) {
if(length(grep("PD_79_201",temptemp[,1]))==1) {
temp <- temptemp[grep("PD_79_201",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
if(length(grep("PD_79_201",temptemp[,1]))>1) {
temp <- temptemp[grep("PD_79_201",temptemp[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use[t])
}
}
dev.off()
}
tau.catch.all <- NULL
for (t in 1:length(tau.hits)) {
print(t)
print(length(tau.hits))
temp <- tau.hits[t]
temp <- working[working==temp]
temp <- names(temp)
temptemp <- strsplit(temp,"_Pos_")
temptemp <- as.data.frame(temptemp)
temptemp <- t(temptemp)
dimnames(temptemp)[[1]] <- temp
temp <- as.character(temptemp[,2])
temp <- strsplit(temp,"X")
temp <- as.data.frame(temp)
temp <- t(temp)
temptemp <- cbind(temptemp,temp)
temptemp <- cbind(temptemp,col.2.use[t])
dimnames(temptemp)[[2]] <- c("Sample","Location","Xpos","Ypos","Color")
tau.catch.all <- rbind(tau.catch.all,temptemp)
}
pdf("TAU_All_String_Hits.pdf",width = 28950/1000, height = 6030/1000)
# AZ_109_146
new.x <- as.numeric(unlist(plot.bulb.locations.AZ109[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ109[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_109_146")
if(length(grep("AZ_109_146",tau.catch.all[,1]))>0) {
if(length(grep("AZ_109_146",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("AZ_109_146",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("AZ_109_146",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("AZ_109_146",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# AZ_84_145
new.x <- as.numeric(unlist(plot.bulb.locations.AZ84[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ84[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_84_145")
if(length(grep("AZ_84_145",tau.catch.all[,1]))>0) {
if(length(grep("AZ_84_145",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("AZ_84_145",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("AZ_84_145",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("AZ_84_145",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# AZ_90_242
new.x <- as.numeric(unlist(plot.bulb.locations.AZ90[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ90[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_90_242")
if(length(grep("AZ_90_242",tau.catch.all[,1]))>0) {
if(length(grep("AZ_90_242",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("AZ_90_242",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("AZ_90_242",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("AZ_90_242",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# AZ_99_140
new.x <- as.numeric(unlist(plot.bulb.locations.AZ99[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ99[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_99_140")
if(length(grep("AZ_99_140",tau.catch.all[,1]))>0) {
if(length(grep("AZ_99_140",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("AZ_99_140",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("AZ_99_140",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("AZ_99_140",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# AZ_251_146
new.x <- as.numeric(unlist(plot.bulb.locations.AZH251[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZH251[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_251_146")
if(length(grep("AZ_251_146",tau.catch.all[,1]))>0) {
if(length(grep("AZ_251_146",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("AZ_251_146",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("AZ_251_146",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("AZ_251_146",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# Control_H190_260
new.x <- as.numeric(unlist(plot.bulb.locations.ControlH190[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlH190[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_H190_260")
if(length(grep("Control_H190_260",tau.catch.all[,1]))>0) {
if(length(grep("Control_H190_260",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("Control_H190_260",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("Control_H190_260",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("Control_H190_260",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# Control_H250_185
new.x <- as.numeric(unlist(plot.bulb.locations.ControlH250[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlH250[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_H250_185")
if(length(grep("Control_H250_185",tau.catch.all[,1]))>0) {
if(length(grep("Control_H250_185",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("Control_H250_185",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("Control_H250_185",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("Control_H250_185",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# Control_OFB57_194
new.x <- as.numeric(unlist(plot.bulb.locations.ControlOFB57[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlOFB57[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_OFB57_194")
if(length(grep("Control_OFB57_194",tau.catch.all[,1]))>0) {
if(length(grep("Control_OFB57_194",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("Control_OFB57_194",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("Control_OFB57_194",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("Control_OFB57_194",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# Control_OFB6A_107
new.x <- as.numeric(unlist(plot.bulb.locations.ControlOFB6a[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlOFB6a[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_OFB6A_107")
if(length(grep("Control_OFB6A_107",tau.catch.all[,1]))>0) {
if(length(grep("Control_OFB6A_107",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("Control_OFB6A_107",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("Control_OFB6A_107",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("Control_OFB6A_107",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# PD_52_247
new.x <- as.numeric(unlist(plot.bulb.locations.PD52[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD52[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_52_247")
if(length(grep("PD_52_247",tau.catch.all[,1]))>0) {
if(length(grep("PD_52_247",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("PD_52_247",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("PD_52_247",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("PD_52_247",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# PD_56_236
new.x <- as.numeric(unlist(plot.bulb.locations.PD56[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD56[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_56_236")
if(length(grep("PD_56_236",tau.catch.all[,1]))>0) {
if(length(grep("PD_56_236",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("PD_56_236",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("PD_56_236",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("PD_56_236",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# PD_58_22
new.x <- as.numeric(unlist(plot.bulb.locations.PD58[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD58[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_58_22")
if(length(grep("PD_58_22",tau.catch.all[,1]))>0) {
if(length(grep("PD_58_22",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("PD_58_22",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("PD_58_22",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("PD_58_22",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# PD_77_197
new.x <- as.numeric(unlist(plot.bulb.locations.PD77[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD77[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_77_197")
if(length(grep("PD_77_197",tau.catch.all[,1]))>0) {
if(length(grep("PD_77_197",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("PD_77_197",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("PD_77_197",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("PD_77_197",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
# PD_79_201
new.x <- as.numeric(unlist(plot.bulb.locations.PD79[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD79[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_79_201")
if(length(grep("PD_79_201",tau.catch.all[,1]))>0) {
if(length(grep("PD_79_201",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("PD_79_201",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("PD_79_201",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("PD_79_201",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
dev.off()
# ALL SIGNIF HITS
all.signif.hits <- c("0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_1_0_1_0_1_0_0_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_0_1_1_1_0_1_0_1_0_0_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0_0_1_0_0_1_1_0_0_0",
"0_0_0_0_1_0_0_1_1_1_0_1_1_0_0_1_0_1_0_0_0_0_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_0_0_1_0_0_0_1_0_0_0",
"0_0_0_0_0_0_0_0_1_1_0_0_1_0_0_1_0_0_0_1_0_0_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0_0_0_0_0_0_0_0_1_0",
"0_0_0_0_0_0_0_1_1_1_0_0_1_0_0_1_0_0_0_1_0_0_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_1_0_1_0_0_1_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_0_0_1_0_0_1_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_1_0_0_0_0_0_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_1_0_1_0_0_0_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_1_0_1_0_0_1_1_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_1_0_1_0_0_0_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_0_1_0_0_1_0_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_0_1_0_1_0_1_0_1_0_0_0_0_0",
"0_0_0_0_0_0_0_0_0_1_0_0_0_0_0_1_0_1_0_1_0_0_0_0_0",
"0_0_0_0_0_1_0_0_1_0_0_0_1_1_0_1_0_1_0_1_1_0_0_0_0",
"0_0_0_0_0_0_1_0_1_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_0",
"0_0_0_0_0_0_1_0_0_0_0_0_0_0_0_0_1_0_0_0_0_0_0_0_1",
"0_0_0_0_0_0_0_0_1_0_0_0_1_1_0_1_0_1_0_1_1_0_0_0_0",
"0_0_0_0_1_0_0_0_0_0_0_0_1_0_0_1_0_1_0_1_0_0_0_0_0",
"0_0_0_0_0_0_0_0_0_1_0_0_0_0_0_1_0_0_0_1_0_0_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_0_1_0_1_0_0_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_1_0_1_0_0_1_0_0_0_0",
"0_0_0_0_0_0_1_0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0_0_0",
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"0_0_0_0_1_0_0_1_0_1_0_0_0_0_0_0_0_1_0_0_0_0_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_1_0_1_0_0_0_0_0_1_0",
"0_0_0_0_0_0_1_0_1_0_0_0_1_0_0_1_0_1_0_0_0_0_0_0_0",
"0_0_0_0_0_1_0_0_0_0_0_0_0_1_1_1_0_1_0_0_1_0_0_0_0",
"0_0_0_0_0_1_0_0_1_0_0_0_1_1_1_1_0_1_0_1_1_0_0_0_0",
"0_0_0_0_0_1_0_0_0_0_0_0_0_0_0_1_0_1_0_0_0_0_0_0_0")
all.signif.hits <- sort(all.signif.hits)
temp1 <- strsplit(all.signif.hits,"_")
temp2 <- as.data.frame(temp1)
dimnames(temp2)[[2]] <- all.signif.hits
dimnames(temp2)[[1]] <- c("ALPHA_SYNUCLEIN",
"BETA_AMYLOID",
"CALBINDIN",
"CD31",
"CH_NEUN",
"CNPASE",
"COLLAGEN_IV",
"DAPI",
"GFAP",
"HISTONES",
"HLADR",
"IBA1",
"MAP2",
"MBP",
"NEURO_HEAVY",
"NEURO_LIGHT",
"OMP",
"PGP9_5",
"RB_CALRETININ",
"S100",
"SYNAPTOPHYSIN",
"TAU",
"TOMATO_LECTIN",
"TYR_HYDROXYLASE",
"UEA_LECTIN")
temp3 <- matrix(as.numeric(unlist(temp2)),dim(temp2)[1],dim(temp2)[2])
temp3 <- ifelse(temp3==1,0,1)
dimnames(temp3) <- dimnames(temp2)
col.2.use <- rainbow(length(all.signif.hits))
hhh <- heatmap.2(as.matrix(temp3),density.info="none",labRow=dimnames(temp3)[[1]],labCol="",scale="none",Rowv=TRUE,Colv=TRUE,col=c(0,1),ColSideColors=col.2.use,key=FALSE,trace="both",linecol=NULL,tracecol="gray",margins = c(5, 10))
temp <- hhh[2][[1]]
names(temp) <- col.2.use
temp <- sort(temp)
col.2.use <- names(temp)
temp <- hhh[2][[1]]
names(temp) <- c(1:324)
temp <- sort(temp)
combo <- names(temp)
hhh <- heatmap.2(as.matrix(temp3),density.info="none",labCol="",labRow=dimnames(temp3)[[1]],scale="none",Rowv=TRUE,Colv=TRUE,col=c(0,1),key=FALSE,trace="both",linecol=NULL,tracecol="gray",margins = c(5, 10),xlab="Label Combinations (Kruskal-Wallis P < 0.05)",adjCol = c(NA,0.4))
#tiff("TAU_String_HeatMap_20200930.tiff",res=300,width = 6, height = 3, units = "in")
svg("All_Signif_String_HeatMap_20200930.svg",width = 12, height = 5)
hhh <- heatmap.2(as.matrix(temp3),density.info="none",labCol="",labRow=dimnames(temp3)[[1]],scale="none",Rowv=TRUE,Colv=TRUE,col=c(0,1),key=FALSE,trace="both",linecol=NULL,tracecol="gray",margins = c(5, 10),xlab="Label Combinations (Kruskal-Wallis P < 0.05)",adjCol = c(NA,0.4))
dev.off()
tau.catch.all <- NULL
for (t in 1:length(tau.hits)) {
print(t)
print(length(tau.hits))
temp <- tau.hits[t]
temp <- working[working==temp]
temp <- names(temp)
temptemp <- strsplit(temp,"_Pos_")
temptemp <- as.data.frame(temptemp)
temptemp <- t(temptemp)
dimnames(temptemp)[[1]] <- temp
temp <- as.character(temptemp[,2])
temp <- strsplit(temp,"X")
temp <- as.data.frame(temp)
temp <- t(temp)
temptemp <- cbind(temptemp,temp)
dimnames(temptemp)[[2]] <- c("Sample","Location","Xpos","Ypos")
tau.catch.all <- rbind(tau.catch.all,temptemp)
}
pdf("TAU_Catch_All_Hits.pdf")
# AZ_109_146
new.x <- as.numeric(unlist(plot.bulb.locations.AZ109[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ109[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_109_146")
if(length(grep("AZ_109_146",tau.catch.all[,1]))>0) {
if(length(grep("AZ_109_146",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("AZ_109_146",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
if(length(grep("AZ_109_146",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("AZ_109_146",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
}
# AZ_84_145
new.x <- as.numeric(unlist(plot.bulb.locations.AZ84[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ84[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_84_145")
if(length(grep("AZ_84_145",tau.catch.all[,1]))>0) {
if(length(grep("AZ_84_145",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("AZ_84_145",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
if(length(grep("AZ_84_145",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("AZ_84_145",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
}
# AZ_90_242
new.x <- as.numeric(unlist(plot.bulb.locations.AZ90[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ90[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_90_242")
if(length(grep("AZ_90_242",tau.catch.all[,1]))>0) {
if(length(grep("AZ_90_242",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("AZ_90_242",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
if(length(grep("AZ_90_242",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("AZ_90_242",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
}
# AZ_99_140
new.x <- as.numeric(unlist(plot.bulb.locations.AZ99[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ99[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_99_140")
if(length(grep("AZ_99_140",tau.catch.all[,1]))>0) {
if(length(grep("AZ_99_140",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("AZ_99_140",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
if(length(grep("AZ_99_140",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("AZ_99_140",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
}
# AZ_251_146
new.x <- as.numeric(unlist(plot.bulb.locations.AZH251[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZH251[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_251_146")
if(length(grep("AZ_251_146",tau.catch.all[,1]))>0) {
if(length(grep("AZ_251_146",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("AZ_251_146",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
if(length(grep("AZ_251_146",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("AZ_251_146",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
}
# Control_H190_260
new.x <- as.numeric(unlist(plot.bulb.locations.ControlH190[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlH190[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_H190_260")
if(length(grep("Control_H190_260",tau.catch.all[,1]))>0) {
if(length(grep("Control_H190_260",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("Control_H190_260",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
if(length(grep("Control_H190_260",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("Control_H190_260",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
}
# Control_H250_185
new.x <- as.numeric(unlist(plot.bulb.locations.ControlH250[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlH250[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_H250_185")
if(length(grep("Control_H250_185",tau.catch.all[,1]))>0) {
if(length(grep("Control_H250_185",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("Control_H250_185",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
if(length(grep("Control_H250_185",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("Control_H250_185",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
}
# Control_OFB57_194
new.x <- as.numeric(unlist(plot.bulb.locations.ControlOFB57[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlOFB57[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_OFB57_194")
if(length(grep("Control_OFB57_194",tau.catch.all[,1]))>0) {
if(length(grep("Control_OFB57_194",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("Control_OFB57_194",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
if(length(grep("Control_OFB57_194",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("Control_OFB57_194",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
}
# Control_OFB6A_107
new.x <- as.numeric(unlist(plot.bulb.locations.ControlOFB6a[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.ControlOFB6a[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Control_OFB6A_107")
if(length(grep("Control_OFB6A_107",tau.catch.all[,1]))>0) {
if(length(grep("Control_OFB6A_107",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("Control_OFB6A_107",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
if(length(grep("Control_OFB6A_107",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("Control_OFB6A_107",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
}
# PD_52_247
new.x <- as.numeric(unlist(plot.bulb.locations.PD52[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD52[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_52_247")
if(length(grep("PD_52_247",tau.catch.all[,1]))>0) {
if(length(grep("PD_52_247",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("PD_52_247",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
if(length(grep("PD_52_247",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("PD_52_247",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
}
# PD_56_236
new.x <- as.numeric(unlist(plot.bulb.locations.PD56[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD56[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_56_236")
if(length(grep("PD_56_236",tau.catch.all[,1]))>0) {
if(length(grep("PD_56_236",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("PD_56_236",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
if(length(grep("PD_56_236",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("PD_56_236",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
}
# PD_58_22
new.x <- as.numeric(unlist(plot.bulb.locations.PD58[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD58[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_58_22")
if(length(grep("PD_58_22",tau.catch.all[,1]))>0) {
if(length(grep("PD_58_22",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("PD_58_22",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
if(length(grep("PD_58_22",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("PD_58_22",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
}
# PD_77_197
new.x <- as.numeric(unlist(plot.bulb.locations.PD77[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD77[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_77_197")
if(length(grep("PD_77_197",tau.catch.all[,1]))>0) {
if(length(grep("PD_77_197",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("PD_77_197",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
if(length(grep("PD_77_197",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("PD_77_197",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
}
# PD_79_201
new.x <- as.numeric(unlist(plot.bulb.locations.PD79[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.PD79[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="PD_79_201")
if(length(grep("PD_79_201",tau.catch.all[,1]))>0) {
if(length(grep("PD_79_201",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("PD_79_201",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
if(length(grep("PD_79_201",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("PD_79_201",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col="black")
}
}
dev.off()
/gpfs/gsfs10/users/johnsonko/SideProject/Helen/20200811/Analysis_Ready"
https://protect-au.mimecast.com/s/c4y2CQnMx5ClAQL8fVgE92?domain=people.cs.uchicago.edu
save.session("Current_20201001.RSession")
save.image("Current_20201001.RData")
load("Current_20201001.RData")
HEREHERE
#####################################################
# Spatial Analysis
#####################################################
all.signif.hits <- c("0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_1_0_1_0_1_0_0_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_0_1_1_1_0_1_0_1_0_0_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0_0_1_0_0_1_1_0_0_0",
"0_0_0_0_1_0_0_1_1_1_0_1_1_0_0_1_0_1_0_0_0_0_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_0_0_1_0_0_0_1_0_0_0",
"0_0_0_0_0_0_0_0_1_1_0_0_1_0_0_1_0_0_0_1_0_0_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0_0_0_0_0_0_0_0_1_0",
"0_0_0_0_0_0_0_1_1_1_0_0_1_0_0_1_0_0_0_1_0_0_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_1_0_1_0_0_1_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_0_0_1_0_0_1_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_1_0_0_0_0_0_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_1_0_1_0_0_0_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_1_0_0_1_0_1_0_0_1_1_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_1_0_1_0_0_0_1_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_0_1_0_0_1_0_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_0_1_0_1_0_1_0_1_0_0_0_0_0",
"0_0_0_0_0_0_0_0_0_1_0_0_0_0_0_1_0_1_0_1_0_0_0_0_0",
"0_0_0_0_0_1_0_0_1_0_0_0_1_1_0_1_0_1_0_1_1_0_0_0_0",
"0_0_0_0_0_0_1_0_1_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_0",
"0_0_0_0_0_0_1_0_0_0_0_0_0_0_0_0_1_0_0_0_0_0_0_0_1",
"0_0_0_0_0_0_0_0_1_0_0_0_1_1_0_1_0_1_0_1_1_0_0_0_0",
"0_0_0_0_1_0_0_0_0_0_0_0_1_0_0_1_0_1_0_1_0_0_0_0_0",
"0_0_0_0_0_0_0_0_0_1_0_0_0_0_0_1_0_0_0_1_0_0_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_0_1_0_1_0_0_0_0_0",
"0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_1_0_1_0_0_1_0_0_0_0",
"0_0_0_0_0_0_1_0_0_0_0_0_0_0_0_1_0_0_0_1_0_0_0_0_0",
"0_0_0_0_0_1_0_0_1_0_0_0_1_0_1_1_0_1_0_0_1_0_0_0_0",
"0_0_0_0_0_0_0_0_1_0_0_0_0_0_0_0_0_0_0_0_0_0_0_1_0",
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"0_0_0_0_0_1_0_0_0_0_0_0_0_0_0_1_0_1_0_0_0_0_0_0_0")
all.signif.hits <- sort(all.signif.hits)
library(Rcpp)
library(dbscan)
# https://protect-au.mimecast.com/s/TWTnCRONy5u0m6XJup59Rs?domain=cran.r-project.org
# https://protect-au.mimecast.com/s/K7XKCVARD0TkwJ87c8OGKl?domain=datanovia.com
# Sample_AZ_109_146_Pos_
temp1 <- working[grep("Sample_AZ_109_146_Pos_",names(working))]
temp2 <- strsplit(names(temp1),"Pos_")
temp3 <- as.data.frame(temp2)
temp3 <- t(temp3)
temp3 <- strsplit(temp3[,2],"X")
temp3 <- as.data.frame(temp3)
temp3 <- t(temp3)
dimnames(temp3) <- NULL
temp3 <- cbind(as.character(temp1),temp3)
temp4 <- NULL
for (i in 1:length(all.signif.hits)) {
print(i)
print(length(all.signif.hits))
temp <- all.signif.hits[i]
temp <- grep(temp,temp3[,1])
if(length(temp)>0) {
temp1 <- temp3[temp,]
temp4 <- rbind(temp4,temp1)
}
}
temp5 <- cbind(as.numeric(temp4[,2]),(-1*as.numeric(temp4[,3])))
pdf("Signif_String_kNN_Inspection_AZ_109_146.pdf",width = 28950/1000, height = 6030/1000)
kNNdistplot(temp5, k = 3)
kkk <- kNNdist(temp5, k = 3)
kkk <- sort(unique(kkk))
for(i in 1:length(kkk)) {
abline(h=kkk[i],col=2,lty=2)
}
dev.off()
final.temp7 <- temp4
pdf("Signif_String_DBSCAN_Clustering_AZ_109_146.pdf",width = 28950/1000, height = 6030/1000)
for(i in 1:length(kkk)) {
temp6 <- dbscan(temp5,borderPoints = FALSE, minPts=3,eps=kkk[i])
temp7 <- cbind(temp5,temp6$cluster)
final.temp7 <- cbind(final.temp7,temp6$cluster)
new.x <- as.numeric(unlist(plot.bulb.locations.AZ109[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ109[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main=as.character(kkk[i]))
num.clust <- (length(unique(sort(temp6$cluster))))-1
col.2.use <- rainbow(num.clust)
points(temp7[,1],temp7[,2], col = "black",pch=15)
for (j in 1:length(col.2.use)) {
if(sum(temp7[,3]==j)>=2) {
temp8 <- temp7[temp7[,3]==j,]
points(temp8[,1],temp8[,2],col=col.2.use[j],pch=15)
}
}
}
dev.off()
dimnames(final.temp7)[[2]] <- c("String","X","Y",as.character(kkk))
write.table(final.temp7,"Signif_String_DBSCAN_Clustering_AZ_109_146.txt",sep="\t")
temp6$cluster + 1L)
# https://protect-au.mimecast.com/s/TWTnCRONy5u0m6XJup59Rs?domain=cran.r-project.org
# AZ_109_146
new.x <- as.numeric(unlist(plot.bulb.locations.AZ109[,1]))
new.y <- as.numeric(unlist(plot.bulb.locations.AZ109[,2]))
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="AZ_109_146")
if(length(grep("AZ_109_146",tau.catch.all[,1]))>0) {
if(length(grep("AZ_109_146",tau.catch.all[,1]))==1) {
temp <- tau.catch.all[grep("AZ_109_146",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[3]))
new.y <- as.numeric(unlist(temp[4]))
ccc <- as.character(unlist(temp[5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
if(length(grep("AZ_109_146",tau.catch.all[,1]))>1) {
temp <- tau.catch.all[grep("AZ_109_146",tau.catch.all[,1]),]
new.x <- as.numeric(unlist(temp[,3]))
new.y <- as.numeric(unlist(temp[,4]))
ccc <- as.character(unlist(temp[,5]))
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=ccc)
}
}
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# By Bulb Seurat Analysis - 20201006
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# HEREHERE
setwd("/data/johnsonko/SideProject/Helen/20200811/Analysis_Ready")
load("Current_20201006.RData")
setwd("/data/johnsonko/SideProject/Helen/20200811/Bulb_Locations")
final.Bulb.Locations.AZ109 <- read.table("Bulb_Locations_AZ109.txt") # AZ_109_146
final.Bulb.Locations.AZ84 <- read.table("Bulb_Locations_AZ84.txt") # AZ_84_145
final.Bulb.Locations.AZ90 <- read.table("Bulb_Locations_AZ90.txt") # AZ_90_242
final.Bulb.Locations.AZ99 <- read.table("Bulb_Locations_AZ99.txt") # AZ_99_140
final.Bulb.Locations.H251 <- read.table("Bulb_Locations_H251.txt") # AZ_251_146
final.Bulb.Locations.H190 <- read.table("Bulb_Locations_H190.txt") # Control_H190_260
final.Bulb.Locations.H250 <- read.table("Bulb_Locations_H250.txt") # Control_H250_185
final.Bulb.Locations.OFB57 <- read.table("Bulb_Locations_OFB57.txt") # Control_OFB57_194
final.Bulb.Locations.OFB6a <- read.table("Bulb_Locations_OFB6a.txt") # Control_OFB6A_107
final.Bulb.Locations.PD52 <- read.table("Bulb_Locations_PD52.txt") # PD_52_247
final.Bulb.Locations.PD56 <- read.table("Bulb_Locations_PD56.txt") # PD_56_236
final.Bulb.Locations.PD58 <- read.table("Bulb_Locations_PD58.txt") # PD_58_22
final.Bulb.Locations.PD77 <- read.table("Bulb_Locations_PD77.txt") # PD_77_197
final.Bulb.Locations.PD79 <- read.table("Bulb_Locations_PD79.txt") # PD_79_201
setwd("/data/johnsonko/SideProject/Helen/20200811/Analysis_Ready")
Final_Analysis_Ready_Counts_AZ_109_146 <- read.table("Final_Analysis_Ready_Counts_AZ_109_146.txt",header=T)
Final_Analysis_Ready_Counts_AZ_251_146 <- read.table("Final_Analysis_Ready_Counts_AZ_251_146.txt",header=T)
Final_Analysis_Ready_Counts_AZ_84_145 <- read.table("Final_Analysis_Ready_Counts_AZ_84_145.txt",header=T)
Final_Analysis_Ready_Counts_AZ_90_242 <- read.table("Final_Analysis_Ready_Counts_AZ_90_242.txt",header=T)
Final_Analysis_Ready_Counts_AZ_99_140 <- read.table("Final_Analysis_Ready_Counts_AZ_99_140.txt",header=T)
Final_Analysis_Ready_Counts_Control_H190_260 <- read.table("Final_Analysis_Ready_Counts_Control_H190_260.txt",header=T)
Final_Analysis_Ready_Counts_Control_H250_185 <- read.table("Final_Analysis_Ready_Counts_Control_H250_185.txt",header=T)
Final_Analysis_Ready_Counts_Control_OFB57_194 <- read.table("Final_Analysis_Ready_Counts_Control_OFB57_194.txt",header=T)
Final_Analysis_Ready_Counts_Control_OFB6A_107 <- read.table("Final_Analysis_Ready_Counts_Control_OFB6A_107.txt",header=T)
Final_Analysis_Ready_Counts_PD_52_247 <- read.table("Final_Analysis_Ready_Counts_PD_52_247.txt",header=T)
Final_Analysis_Ready_Counts_PD_56_236 <- read.table("Final_Analysis_Ready_Counts_PD_56_236.txt",header=T)
Final_Analysis_Ready_Counts_PD_58_229 <- read.table("Final_Analysis_Ready_Counts_PD_58_229.txt",header=T)
Final_Analysis_Ready_Counts_PD_77_197 <- read.table("Final_Analysis_Ready_Counts_PD_77_197.txt",header=T)
Final_Analysis_Ready_Counts_PD_79_201 <- read.table("Final_Analysis_Ready_Counts_PD_79_201.txt",header=T)
ttt <- c("Final_Analysis_Ready_Counts_AZ_109_146",
"Final_Analysis_Ready_Counts_AZ_251_146",
"Final_Analysis_Ready_Counts_AZ_84_145",
"Final_Analysis_Ready_Counts_AZ_90_242",
"Final_Analysis_Ready_Counts_AZ_99_140",
"Final_Analysis_Ready_Counts_Control_H190_260",
"Final_Analysis_Ready_Counts_Control_H250_185",
"Final_Analysis_Ready_Counts_Control_OFB57_194",
"Final_Analysis_Ready_Counts_Control_OFB6A_107",
"Final_Analysis_Ready_Counts_PD_52_247",
"Final_Analysis_Ready_Counts_PD_56_236",
"Final_Analysis_Ready_Counts_PD_58_229",
"Final_Analysis_Ready_Counts_PD_77_197",
"Final_Analysis_Ready_Counts_PD_79_201")
ppp <- c("AZ_109_146",
"AZ_251_146",
"AZ_84_145",
"AZ_90_242",
"AZ_99_140",
"Control_H190_260",
"Control_H250_185",
"Control_OFB57_194",
"Control_OFB6A_107",
"PD_52_247",
"PD_56_236",
"PD_58_229",
"PD_77_197",
"PD_79_201")
bbb <- c("final.Bulb.Locations.AZ109",
"final.Bulb.Locations.H251",
"final.Bulb.Locations.AZ84",
"final.Bulb.Locations.AZ90",
"final.Bulb.Locations.AZ99",
"final.Bulb.Locations.H190",
"final.Bulb.Locations.H250",
"final.Bulb.Locations.OFB57",
"final.Bulb.Locations.OFB6a",
"final.Bulb.Locations.PD52",
"final.Bulb.Locations.PD56",
"final.Bulb.Locations.PD58",
"final.Bulb.Locations.PD77",
"final.Bulb.Locations.PD79")
setwd("/data/johnsonko/SideProject/Helen/20200811/Analysis_Slides")
library(Seurat)
library(ggplot2)
library(sctransform)
library(patchwork)
library(slides)
library("scales")
library(stringr)
for (t in 1:length(ttt)) {
rm(pbmc)
print(t)
temp0 <- eval(parse(text=ttt[t]))
temp0 <- as.matrix(temp0)
temp0 <- round(temp0*100,0)
temp1 <- temp0[apply(temp0,1,max)>0,]
temp1 <- t(temp1)
pbmc <- CreateSeuratObject(counts = temp1)
pbmc <- SCTransform(pbmc, verbose = FALSE)
pbmc <- RunPCA(pbmc, verbose = FALSE,approx=FALSE)
#DimPlot(pbmc,reduction="pca",label=FALSE)
e <- ElbowPlot(pbmc, ndims=dim(pbmc$pca)[2],reduction="pca")
e <- e[[1]]
e <- e[e[,2]>1,]
e <- max(e[,1])
dims2use <- e
pbmc <- RunUMAP(pbmc, dims = 1:dims2use, verbose = FALSE)
#DimPlot(pbmc,reduction="umap",label=FALSE)
pbmc <- FindNeighbors(pbmc, dims = 1:dims2use, verbose = FALSE)
pbmc <- FindClusters(pbmc, verbose = FALSE)
write.table(table(pbmc@meta.data$seurat_clusters),paste(c("Cluster_Bin_Counts_",ppp[t],".txt"),sep="",collapse=""))
tiff(paste(c("UMAP_Plot_Label_Yes_Legend_Yes_",ppp[t],".tiff"),sep="",collapse=""))
print(DimPlot(pbmc,reduction="umap",label=TRUE))
dev.off()
tiff(paste(c("UMAP_Plot_Label_Yes_Legend_No_",ppp[t],".tiff"),sep="",collapse=""))
print(DimPlot(pbmc,reduction="umap",label = TRUE) + NoLegend())
dev.off()
tiff(paste(c("UMAP_Plot_Label_No_Legend_No_",ppp[t],".tiff"),sep="",collapse=""))
print(DimPlot(pbmc,reduction="umap",label = FALSE) + NoLegend())
dev.off()
tiff(paste(c("UMAP_Plot_Label_No_Legend_Yes_",ppp[t],".tiff"),sep="",collapse=""))
print(DimPlot(pbmc,reduction="umap",label = FALSE))
dev.off()
if (length(grep("ALPHA-SYNUCLEIN",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_ALPHA_SYNUCLEIN_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("ALPHA-SYNUCLEIN")))
dev.off()
tiff(paste(c("Violin_Plot_ALPHA_SYNUCLEIN_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("ALPHA-SYNUCLEIN")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_ALPHA_SYNUCLEIN_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("ALPHA-SYNUCLEIN")))
dev.off()
}
if (length(grep("BETA-AMYLOID",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_BETA_AMYLOID_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("BETA-AMYLOID")))
dev.off()
tiff(paste(c("Violin_Plot_BETA_AMYLOID_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("BETA-AMYLOID")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_BETA_AMYLOID_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("BETA-AMYLOID")))
dev.off()
}
if (length(grep("CALBINDIN",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_CALBINDIN_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("CALBINDIN")))
dev.off()
tiff(paste(c("Violin_Plot_CALBINDIN_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("CALBINDIN")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_CALBINDIN_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("CALBINDIN")))
dev.off()
}
if (length(grep("CH-NEUN",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_CH_NEUN_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("CH-NEUN")))
dev.off()
tiff(paste(c("Violin_Plot_CH_NEUN_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("CH-NEUN")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_CH_NEUN_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("CH-NEUN")))
dev.off()
}
if (length(grep("CNPASE",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_CNPASE_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("CNPASE")))
dev.off()
tiff(paste(c("Violin_Plot_CNPASE_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("CNPASE")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_CNPASE_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("CNPASE")))
dev.off()
}
if (length(grep("COLLAGEN-IV",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_COLLAGEN_IV_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("COLLAGEN-IV")))
dev.off()
tiff(paste(c("Violin_Plot_COLLAGEN_IV_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("COLLAGEN-IV")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_COLLAGEN_IV_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("COLLAGEN-IV")))
dev.off()
}
if (length(grep("DAPI",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_DAPI_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("DAPI")))
dev.off()
tiff(paste(c("Violin_Plot_DAPI_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("DAPI")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_DAPI_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("DAPI")))
dev.off()
}
if (length(grep("GFAP",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_GFAP_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("GFAP")))
dev.off()
tiff(paste(c("Violin_Plot_GFAP_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("GFAP")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_GFAP_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("GFAP")))
dev.off()
}
if (length(grep("HISTONES",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_HISTONES_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("HISTONES")))
dev.off()
tiff(paste(c("Violin_Plot_HISTONES_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("HISTONES")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_HISTONES_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("HISTONES")))
dev.off()
}
if (length(grep("HLADR",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_HLADR_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("HLADR")))
dev.off()
tiff(paste(c("Violin_Plot_HLADR_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("HLADR")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_HLADR_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("HLADR")))
dev.off()
}
if (length(grep("IBA1",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_IBA1_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("IBA1")))
dev.off()
tiff(paste(c("Violin_Plot_IBA1_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("IBA1")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_IBA1_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("IBA1")))
dev.off()
}
if (length(grep("MAP2",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_MAP2_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("MAP2")))
dev.off()
tiff(paste(c("Violin_Plot_MAP2_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("MAP2")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_MAP2_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("MAP2")))
dev.off()
}
if (length(grep("MBP",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_MBP_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("MBP")))
dev.off()
tiff(paste(c("Violin_Plot_MBP_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("MBP")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_MBP_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("MBP")))
dev.off()
}
if (length(grep("NEURO-HEAVY",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_NEURO_HEAVY_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("NEURO-HEAVY")))
dev.off()
tiff(paste(c("Violin_Plot_NEURO_HEAVY_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("NEURO-HEAVY")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_NEURO_HEAVY_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("NEURO-HEAVY")))
dev.off()
}
if (length(grep("NEURO-LIGHT",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_NEURO_LIGHT_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("NEURO-LIGHT")))
dev.off()
tiff(paste(c("Violin_Plot_NEURO_LIGHT_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("NEURO-LIGHT")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_NEURO_LIGHT_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("NEURO-LIGHT")))
dev.off()
}
if (length(grep("OMP",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_OMP_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("OMP")))
dev.off()
tiff(paste(c("Violin_Plot_OMP_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("OMP")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_OMP_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("OMP")))
dev.off()
}
if (length(grep("PGP9-5",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_PGP9_5_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("PGP9-5")))
dev.off()
tiff(paste(c("Violin_Plot_PGP9_5_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("PGP9-5")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_PGP9_5_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("PGP9-5")))
dev.off()
}
if (length(grep("RB-CALRETININ",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_RB_CALRETININ_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("RB-CALRETININ")))
dev.off()
tiff(paste(c("Violin_Plot_RB_CALRETININ_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("RB-CALRETININ")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_RB_CALRETININ_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("RB-CALRETININ")))
dev.off()
}
if (length(grep("S100",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_S100_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("S100")))
dev.off()
tiff(paste(c("Violin_Plot_S100_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("S100")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_S100_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("S100")))
dev.off()
}
if (length(grep("SYNAPTOPHYSIN",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_SYNAPTOPHYSIN_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("SYNAPTOPHYSIN")))
dev.off()
tiff(paste(c("Violin_Plot_SYNAPTOPHYSIN_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("SYNAPTOPHYSIN")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_SYNAPTOPHYSIN_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("SYNAPTOPHYSIN")))
dev.off()
}
if (length(grep("TAU",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_TAU_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("TAU")))
dev.off()
tiff(paste(c("Violin_Plot_TAU_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("TAU")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_TAU_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("TAU")))
dev.off()
}
if (length(grep("TOMATO-LECTIN",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_TOMATO_LECTIN_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("TOMATO-LECTIN")))
dev.off()
tiff(paste(c("Violin_Plot_TOMATO_LECTIN_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("TOMATO-LECTIN")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_TOMATO_LECTIN_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("TOMATO-LECTIN")))
dev.off()
}
if (length(grep("TYR-HYDROXYLASE",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_TYR_HYDROXYLASE_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("TYR-HYDROXYLASE")))
dev.off()
tiff(paste(c("Violin_Plot_TYR_HYDROXYLASE_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("TYR-HYDROXYLASE")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_TYR_HYDROXYLASE_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("TYR-HYDROXYLASE")))
dev.off()
}
if (length(grep("UEA-LECTIN",rownames(pbmc)))>0) {
tiff(paste(c("Feature_Plot_UEA_LECTIN_",ppp[t],".tiff"),sep="",collapse=""))
print(FeaturePlot(pbmc, features = c("UEA-LECTIN")))
dev.off()
tiff(paste(c("Violin_Plot_UEA_LECTIN_",ppp[t],".tiff"),sep="",collapse=""))
print(VlnPlot(pbmc, features = c("UEA-LECTIN")) + NoLegend())
dev.off()
tiff(paste(c("Ridge_Plot_UEA_LECTIN_",ppp[t],".tiff"),sep="",collapse=""))
print(RidgePlot(pbmc, features = c("UEA-LECTIN")))
dev.off()
}
tiff(paste(c("DotPlot_",ppp[t],".tiff"),sep="",collapse=""))
print(DotPlot(pbmc, features = rownames(pbmc)) + RotatedAxis())
dev.off()
hex_codes1 <- hue_pal()(length(table(pbmc@meta.data$seurat_clusters)))
pdf(paste(c("Reconstructed_Slide_Plots_",ppp[t],".pdf"),sep="",collapse=""),width = 28950/1000, height = 6030/1000)
sr0 <- eval(parse(text=bbb[t]))
sr1 <- dimnames(sr0)[[1]]
sr2 <- NULL
for (j in 1:length(sr1)) {
sr3 <- strsplit(sr1[j], "@")[[1]][2]
sr3 <- strsplit(sr3, "X")[[1]]
sr2 <- rbind(sr2,as.numeric(sr3))
}
new.x <- as.numeric(sr2[,1])
new.y <- as.numeric(sr2[,2])
print(plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Bulb_Detected_Bins"))
print(plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="All_Cluster_Overlay"))
ssr1 <- colnames(pbmc)
ssr2 <- as.numeric(unlist(pbmc@meta.data$seurat_clusters))-1
names(ssr2) <- ssr1
for (i in 1:length(table(pbmc@meta.data$seurat_clusters))) {
sssr1 <- names(ssr2[ssr2==(i-1)])
sssr2 <- NULL
for (j in 1:length(sssr1)) {
sssr3 <- strsplit(sssr1[j], "X")[[1]]
sssr2 <- rbind(sssr2,as.numeric(sssr3))
}
newnew.x <- as.numeric(sssr2[,1])
newnew.y <- as.numeric(sssr2[,2])
print(points(as.numeric(newnew.x),-1*as.numeric(newnew.y),cex=1,pch=15,col=hex_codes1[i]))
}
ssr1 <- colnames(pbmc)
ssr2 <- as.numeric(unlist(pbmc@meta.data$seurat_clusters))-1
names(ssr2) <- ssr1
for (i in 1:length(table(pbmc@meta.data$seurat_clusters))) {
sssr1 <- names(ssr2[ssr2==(i-1)])
sssr2 <- NULL
for (j in 1:length(sssr1)) {
sssr3 <- strsplit(sssr1[j], "X")[[1]]
sssr2 <- rbind(sssr2,as.numeric(sssr3))
}
print(plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main=as.character(i-1)))
newnew.x <- as.numeric(sssr2[,1])
newnew.y <- as.numeric(sssr2[,2])
print(points(as.numeric(newnew.x),-1*as.numeric(newnew.y),cex=1,pch=15,col=hex_codes1[i]))
}
dev.off()
keep.it <- ppp[t]
keep.it <- str_replace_all(keep.it,"_",".")
eval(parse(text=paste(c("pbmc.",keep.it," <- pbmc"),collapse="",sep="")))
}
save.image("Current_20201006.RData")
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# Multi Cluster Overlay 20201204
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setwd("/data/johnsonko/SideProject/Helen/20200811/Bulb_Locations")
final.Bulb.Locations.AZ109 <- read.table("Bulb_Locations_AZ109.txt") # AZ_109_146
final.Bulb.Locations.AZ84 <- read.table("Bulb_Locations_AZ84.txt") # AZ_84_145
final.Bulb.Locations.AZ90 <- read.table("Bulb_Locations_AZ90.txt") # AZ_90_242
final.Bulb.Locations.AZ99 <- read.table("Bulb_Locations_AZ99.txt") # AZ_99_140
final.Bulb.Locations.H251 <- read.table("Bulb_Locations_H251.txt") # AZ_251_146
final.Bulb.Locations.H190 <- read.table("Bulb_Locations_H190.txt") # Control_H190_260
final.Bulb.Locations.H250 <- read.table("Bulb_Locations_H250.txt") # Control_H250_185
final.Bulb.Locations.OFB57 <- read.table("Bulb_Locations_OFB57.txt") # Control_OFB57_194
final.Bulb.Locations.OFB6a <- read.table("Bulb_Locations_OFB6a.txt") # Control_OFB6A_107
final.Bulb.Locations.PD52 <- read.table("Bulb_Locations_PD52.txt") # PD_52_247
final.Bulb.Locations.PD56 <- read.table("Bulb_Locations_PD56.txt") # PD_56_236
final.Bulb.Locations.PD58 <- read.table("Bulb_Locations_PD58.txt") # PD_58_22
final.Bulb.Locations.PD77 <- read.table("Bulb_Locations_PD77.txt") # PD_77_197
final.Bulb.Locations.PD79 <- read.table("Bulb_Locations_PD79.txt") # PD_79_201
setwd("/data/johnsonko/SideProject/Helen/20200811/Analysis_Ready")
Final_Analysis_Ready_Counts_AZ_109_146 <- read.table("Final_Analysis_Ready_Counts_AZ_109_146.txt",header=T)
Final_Analysis_Ready_Counts_AZ_251_146 <- read.table("Final_Analysis_Ready_Counts_AZ_251_146.txt",header=T)
Final_Analysis_Ready_Counts_AZ_84_145 <- read.table("Final_Analysis_Ready_Counts_AZ_84_145.txt",header=T)
Final_Analysis_Ready_Counts_AZ_90_242 <- read.table("Final_Analysis_Ready_Counts_AZ_90_242.txt",header=T)
Final_Analysis_Ready_Counts_AZ_99_140 <- read.table("Final_Analysis_Ready_Counts_AZ_99_140.txt",header=T)
Final_Analysis_Ready_Counts_Control_H190_260 <- read.table("Final_Analysis_Ready_Counts_Control_H190_260.txt",header=T)
Final_Analysis_Ready_Counts_Control_H250_185 <- read.table("Final_Analysis_Ready_Counts_Control_H250_185.txt",header=T)
Final_Analysis_Ready_Counts_Control_OFB57_194 <- read.table("Final_Analysis_Ready_Counts_Control_OFB57_194.txt",header=T)
Final_Analysis_Ready_Counts_Control_OFB6A_107 <- read.table("Final_Analysis_Ready_Counts_Control_OFB6A_107.txt",header=T)
Final_Analysis_Ready_Counts_PD_52_247 <- read.table("Final_Analysis_Ready_Counts_PD_52_247.txt",header=T)
Final_Analysis_Ready_Counts_PD_56_236 <- read.table("Final_Analysis_Ready_Counts_PD_56_236.txt",header=T)
Final_Analysis_Ready_Counts_PD_58_229 <- read.table("Final_Analysis_Ready_Counts_PD_58_229.txt",header=T)
Final_Analysis_Ready_Counts_PD_77_197 <- read.table("Final_Analysis_Ready_Counts_PD_77_197.txt",header=T)
Final_Analysis_Ready_Counts_PD_79_201 <- read.table("Final_Analysis_Ready_Counts_PD_79_201.txt",header=T)
ttt <- c("Final_Analysis_Ready_Counts_AZ_109_146",
"Final_Analysis_Ready_Counts_AZ_251_146",
"Final_Analysis_Ready_Counts_AZ_84_145",
"Final_Analysis_Ready_Counts_AZ_90_242",
"Final_Analysis_Ready_Counts_AZ_99_140",
"Final_Analysis_Ready_Counts_Control_H190_260",
"Final_Analysis_Ready_Counts_Control_H250_185",
"Final_Analysis_Ready_Counts_Control_OFB57_194",
"Final_Analysis_Ready_Counts_Control_OFB6A_107",
"Final_Analysis_Ready_Counts_PD_52_247",
"Final_Analysis_Ready_Counts_PD_56_236",
"Final_Analysis_Ready_Counts_PD_58_229",
"Final_Analysis_Ready_Counts_PD_77_197",
"Final_Analysis_Ready_Counts_PD_79_201")
ppp <- c("AZ_109_146",
"AZ_251_146",
"AZ_84_145",
"AZ_90_242",
"AZ_99_140",
"Control_H190_260",
"Control_H250_185",
"Control_OFB57_194",
"Control_OFB6A_107",
"PD_52_247",
"PD_56_236",
"PD_58_229",
"PD_77_197",
"PD_79_201")
bbb <- c("final.Bulb.Locations.AZ109",
"final.Bulb.Locations.H251",
"final.Bulb.Locations.AZ84",
"final.Bulb.Locations.AZ90",
"final.Bulb.Locations.AZ99",
"final.Bulb.Locations.H190",
"final.Bulb.Locations.H250",
"final.Bulb.Locations.OFB57",
"final.Bulb.Locations.OFB6a",
"final.Bulb.Locations.PD52",
"final.Bulb.Locations.PD56",
"final.Bulb.Locations.PD58",
"final.Bulb.Locations.PD77",
"final.Bulb.Locations.PD79")
setwd("/data/johnsonko/SideProject/Helen/20200811/Analysis_Slides")
pbmc <- pbmc.Control.H250.185 # <----------------------------------------------------------------------------------------------------------------------------------HERE
t <- 7
hex_codes1 <- hue_pal()(length(table(pbmc@meta.data$seurat_clusters)))
pdf(paste(c("Special_Reconstructed_Slide_Plot_20201204",ppp[t],".pdf"),sep="",collapse=""),width = 28950/1000, height = 6030/1000)
sr0 <- eval(parse(text=bbb[t]))
sr1 <- dimnames(sr0)[[1]]
sr2 <- NULL
for (j in 1:length(sr1)) {
sr3 <- strsplit(sr1[j], "@")[[1]][2]
sr3 <- strsplit(sr3, "X")[[1]]
sr2 <- rbind(sr2,as.numeric(sr3))
}
new.x <- as.numeric(sr2[,1])
new.y <- as.numeric(sr2[,2])
print(plot(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8,main="Select_Cluster_Overlay"))
ssr1 <- colnames(pbmc)
ssr2 <- as.numeric(unlist(pbmc@meta.data$seurat_clusters))-1
names(ssr2) <- ssr1
sssr1 <- names(ssr2[ssr2==0])
sssr2 <- NULL
for (j in 1:length(sssr1)) {
sssr3 <- strsplit(sssr1[j], "X")[[1]]
sssr2 <- rbind(sssr2,as.numeric(sssr3))
}
newnew.x <- as.numeric(sssr2[,1])
newnew.y <- as.numeric(sssr2[,2])
print(points(as.numeric(newnew.x),-1*as.numeric(newnew.y),cex=1,pch=15,col=hex_codes1[0+1]))
sssr1 <- names(ssr2[ssr2==1])
sssr2 <- NULL
for (j in 1:length(sssr1)) {
sssr3 <- strsplit(sssr1[j], "X")[[1]]
sssr2 <- rbind(sssr2,as.numeric(sssr3))
}
newnew.x <- as.numeric(sssr2[,1])
newnew.y <- as.numeric(sssr2[,2])
print(points(as.numeric(newnew.x),-1*as.numeric(newnew.y),cex=1,pch=15,col=hex_codes1[1+1]))
sssr1 <- names(ssr2[ssr2==2])
sssr2 <- NULL
for (j in 1:length(sssr1)) {
sssr3 <- strsplit(sssr1[j], "X")[[1]]
sssr2 <- rbind(sssr2,as.numeric(sssr3))
}
newnew.x <- as.numeric(sssr2[,1])
newnew.y <- as.numeric(sssr2[,2])
print(points(as.numeric(newnew.x),-1*as.numeric(newnew.y),cex=1,pch=15,col=hex_codes1[2+1]))
sssr1 <- names(ssr2[ssr2==5])
sssr2 <- NULL
for (j in 1:length(sssr1)) {
sssr3 <- strsplit(sssr1[j], "X")[[1]]
sssr2 <- rbind(sssr2,as.numeric(sssr3))
}
newnew.x <- as.numeric(sssr2[,1])
newnew.y <- as.numeric(sssr2[,2])
print(points(as.numeric(newnew.x),-1*as.numeric(newnew.y),cex=1,pch=15,col=hex_codes1[5+1]))
sssr1 <- names(ssr2[ssr2==6])
sssr2 <- NULL
for (j in 1:length(sssr1)) {
sssr3 <- strsplit(sssr1[j], "X")[[1]]
sssr2 <- rbind(sssr2,as.numeric(sssr3))
}
newnew.x <- as.numeric(sssr2[,1])
newnew.y <- as.numeric(sssr2[,2])
print(points(as.numeric(newnew.x),-1*as.numeric(newnew.y),cex=1,pch=15,col=hex_codes1[6+1]))
sssr1 <- names(ssr2[ssr2==11])
sssr2 <- NULL
for (j in 1:length(sssr1)) {
sssr3 <- strsplit(sssr1[j], "X")[[1]]
sssr2 <- rbind(sssr2,as.numeric(sssr3))
}
newnew.x <- as.numeric(sssr2[,1])
newnew.y <- as.numeric(sssr2[,2])
print(points(as.numeric(newnew.x),-1*as.numeric(newnew.y),cex=1,pch=15,col=hex_codes1[11+1]))
sssr1 <- names(ssr2[ssr2==13])
sssr2 <- NULL
for (j in 1:length(sssr1)) {
sssr3 <- strsplit(sssr1[j], "X")[[1]]
sssr2 <- rbind(sssr2,as.numeric(sssr3))
}
newnew.x <- as.numeric(sssr2[,1])
newnew.y <- as.numeric(sssr2[,2])
print(points(as.numeric(newnew.x),-1*as.numeric(newnew.y),cex=1,pch=15,col=hex_codes1[13+1]))
sssr1 <- names(ssr2[ssr2==15])
sssr2 <- NULL
for (j in 1:length(sssr1)) {
sssr3 <- strsplit(sssr1[j], "X")[[1]]
sssr2 <- rbind(sssr2,as.numeric(sssr3))
}
newnew.x <- as.numeric(sssr2[,1])
newnew.y <- as.numeric(sssr2[,2])
print(points(as.numeric(newnew.x),-1*as.numeric(newnew.y),cex=1,pch=15,col=hex_codes1[15+1]))
sssr1 <- names(ssr2[ssr2==19])
sssr2 <- NULL
for (j in 1:length(sssr1)) {
sssr3 <- strsplit(sssr1[j], "X")[[1]]
sssr2 <- rbind(sssr2,as.numeric(sssr3))
}
newnew.x <- as.numeric(sssr2[,1])
newnew.y <- as.numeric(sssr2[,2])
print(points(as.numeric(newnew.x),-1*as.numeric(newnew.y),cex=1,pch=15,col=hex_codes1[19+1]))
sssr1 <- names(ssr2[ssr2==21])
sssr2 <- NULL
for (j in 1:length(sssr1)) {
sssr3 <- strsplit(sssr1[j], "X")[[1]]
sssr2 <- rbind(sssr2,as.numeric(sssr3))
}
newnew.x <- as.numeric(sssr2[,1])
newnew.y <- as.numeric(sssr2[,2])
print(points(as.numeric(newnew.x),-1*as.numeric(newnew.y),cex=1,pch=15,col=hex_codes1[21+1]))
dev.off()
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# Focus FollowUp 20201116
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library(gplots)
pbmc <- pbmc.Control.H250.185 # <----------------------------------------------------------------------------------------------------------------------------------HERE
e <- ElbowPlot(pbmc, ndims=dim(pbmc$pca)[2],reduction="pca")
e <- e[[1]]
e <- e[e[,2]>1,]
e <- max(e[,1])
dims2use <- e
pbmc.tree <- BuildClusterTree(pbmc,dims=1:dims2use)
tttree <- Tool(object = pbmc.tree, slot = 'BuildClusterTree')
tiff("pbmc.Control.H250.185.dendrogram.tiff",width = 6, height = 8, units = "in", res=300) # <---------------------------------------------------------------------HERE
print(plot(tttree))
dev.off()
is_tip <- tttree$edge[,2] <= length(tttree$tip.label)
ordered_tips <- tttree$edge[is_tip, 2]
dend.order <- rev(tttree$tip.label[ordered_tips])
ppp <- AverageExpression(pbmc)
final.ttt <- ppp[[2]]
final.ttt <- final.ttt[,dend.order]
final.ttt <- t(final.ttt)
final.ttt <- ifelse(final.ttt<0,0,final.ttt)
pal <- colorRampPalette(c("white", "red"))
ccc <- pal(64)
tiff("pbmc.Control.H250.185.heatmap.no.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <--------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="none",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="No z-score",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
tiff("pbmc.Control.H250.185.heatmap.with.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="row",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="z-score (row)",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
pbmc <- pbmc.Control.H190.260 # <----------------------------------------------------------------------------------------------------------------------------------HERE
e <- ElbowPlot(pbmc, ndims=dim(pbmc$pca)[2],reduction="pca")
e <- e[[1]]
e <- e[e[,2]>1,]
e <- max(e[,1])
dims2use <- e
pbmc.tree <- BuildClusterTree(pbmc,dims=1:dims2use)
tttree <- Tool(object = pbmc.tree, slot = 'BuildClusterTree')
tiff("pbmc.Control.H190.260.dendrogram.tiff",width = 6, height = 8, units = "in", res=300) # <---------------------------------------------------------------------HERE
print(plot(tttree))
dev.off()
is_tip <- tttree$edge[,2] <= length(tttree$tip.label)
ordered_tips <- tttree$edge[is_tip, 2]
dend.order <- rev(tttree$tip.label[ordered_tips])
ppp <- AverageExpression(pbmc)
final.ttt <- ppp[[2]]
final.ttt <- final.ttt[,dend.order]
final.ttt <- t(final.ttt)
final.ttt <- ifelse(final.ttt<0,0,final.ttt)
pal <- colorRampPalette(c("white", "red"))
ccc <- pal(64)
tiff("pbmc.Control.H190.260.heatmap.no.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <--------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="none",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="No z-score",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
tiff("pbmc.Control.H190.260.heatmap.with.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="row",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="z-score (row)",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
pbmc <- pbmc.Control.OFB57.194 # <----------------------------------------------------------------------------------------------------------------------------------HERE
e <- ElbowPlot(pbmc, ndims=dim(pbmc$pca)[2],reduction="pca")
e <- e[[1]]
e <- e[e[,2]>1,]
e <- max(e[,1])
dims2use <- e
pbmc.tree <- BuildClusterTree(pbmc,dims=1:dims2use)
tttree <- Tool(object = pbmc.tree, slot = 'BuildClusterTree')
tiff("pbmc.Control.OFB57.194.dendrogram.tiff",width = 6, height = 8, units = "in", res=300) # <---------------------------------------------------------------------HERE
print(plot(tttree))
dev.off()
is_tip <- tttree$edge[,2] <= length(tttree$tip.label)
ordered_tips <- tttree$edge[is_tip, 2]
dend.order <- rev(tttree$tip.label[ordered_tips])
ppp <- AverageExpression(pbmc)
final.ttt <- ppp[[2]]
final.ttt <- final.ttt[,dend.order]
final.ttt <- t(final.ttt)
final.ttt <- ifelse(final.ttt<0,0,final.ttt)
pal <- colorRampPalette(c("white", "red"))
ccc <- pal(64)
tiff("pbmc.Control.OFB57.194.heatmap.no.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <--------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="none",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="No z-score",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
tiff("pbmc.Control.OFB57.194.heatmap.with.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="row",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="z-score (row)",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
pbmc <- pbmc.Control.OFB6A.107 # <----------------------------------------------------------------------------------------------------------------------------------HERE
e <- ElbowPlot(pbmc, ndims=dim(pbmc$pca)[2],reduction="pca")
e <- e[[1]]
e <- e[e[,2]>1,]
e <- max(e[,1])
dims2use <- e
pbmc.tree <- BuildClusterTree(pbmc,dims=1:dims2use)
tttree <- Tool(object = pbmc.tree, slot = 'BuildClusterTree')
tiff("pbmc.Control.OFB6A.107.dendrogram.tiff",width = 6, height = 8, units = "in", res=300) # <---------------------------------------------------------------------HERE
print(plot(tttree))
dev.off()
is_tip <- tttree$edge[,2] <= length(tttree$tip.label)
ordered_tips <- tttree$edge[is_tip, 2]
dend.order <- rev(tttree$tip.label[ordered_tips])
ppp <- AverageExpression(pbmc)
final.ttt <- ppp[[2]]
final.ttt <- final.ttt[,dend.order]
final.ttt <- t(final.ttt)
final.ttt <- ifelse(final.ttt<0,0,final.ttt)
pal <- colorRampPalette(c("white", "red"))
ccc <- pal(64)
tiff("pbmc.Control.OFB6A.107.heatmap.no.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <--------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="none",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="No z-score",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
tiff("pbmc.Control.OFB6A.107.heatmap.with.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="row",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="z-score (row)",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
pbmc <- pbmc.AZ.109.146 # <----------------------------------------------------------------------------------------------------------------------------------HERE
e <- ElbowPlot(pbmc, ndims=dim(pbmc$pca)[2],reduction="pca")
e <- e[[1]]
e <- e[e[,2]>1,]
e <- max(e[,1])
dims2use <- e
pbmc.tree <- BuildClusterTree(pbmc,dims=1:dims2use)
tttree <- Tool(object = pbmc.tree, slot = 'BuildClusterTree')
tiff("pbmc.AZ.109.146.dendrogram.tiff",width = 6, height = 8, units = "in", res=300) # <---------------------------------------------------------------------HERE
print(plot(tttree))
dev.off()
is_tip <- tttree$edge[,2] <= length(tttree$tip.label)
ordered_tips <- tttree$edge[is_tip, 2]
dend.order <- rev(tttree$tip.label[ordered_tips])
ppp <- AverageExpression(pbmc)
final.ttt <- ppp[[2]]
final.ttt <- final.ttt[,dend.order]
final.ttt <- t(final.ttt)
final.ttt <- ifelse(final.ttt<0,0,final.ttt)
pal <- colorRampPalette(c("white", "red"))
ccc <- pal(64)
tiff("pbmc.AZ.109.146.heatmap.no.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <--------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="none",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="No z-score",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
tiff("pbmc.AZ.109.146.heatmap.with.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="row",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="z-score (row)",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
pbmc <- pbmc.AZ.251.146 # <----------------------------------------------------------------------------------------------------------------------------------HERE
e <- ElbowPlot(pbmc, ndims=dim(pbmc$pca)[2],reduction="pca")
e <- e[[1]]
e <- e[e[,2]>1,]
e <- max(e[,1])
dims2use <- e
pbmc.tree <- BuildClusterTree(pbmc,dims=1:dims2use)
tttree <- Tool(object = pbmc.tree, slot = 'BuildClusterTree')
tiff("pbmc.AZ.251.146.dendrogram.tiff",width = 6, height = 8, units = "in", res=300) # <---------------------------------------------------------------------HERE
print(plot(tttree))
dev.off()
is_tip <- tttree$edge[,2] <= length(tttree$tip.label)
ordered_tips <- tttree$edge[is_tip, 2]
dend.order <- rev(tttree$tip.label[ordered_tips])
ppp <- AverageExpression(pbmc)
final.ttt <- ppp[[2]]
final.ttt <- final.ttt[,dend.order]
final.ttt <- t(final.ttt)
final.ttt <- ifelse(final.ttt<0,0,final.ttt)
pal <- colorRampPalette(c("white", "red"))
ccc <- pal(64)
tiff("pbmc.AZ.251.146.heatmap.no.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <--------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="none",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="No z-score",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
tiff("pbmc.AZ.251.146.heatmap.with.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="row",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="z-score (row)",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
pbmc <- pbmc.AZ.84.145 # <----------------------------------------------------------------------------------------------------------------------------------HERE
e <- ElbowPlot(pbmc, ndims=dim(pbmc$pca)[2],reduction="pca")
e <- e[[1]]
e <- e[e[,2]>1,]
e <- max(e[,1])
dims2use <- e
pbmc.tree <- BuildClusterTree(pbmc,dims=1:dims2use)
tttree <- Tool(object = pbmc.tree, slot = 'BuildClusterTree')
tiff("pbmc.AZ.84.145.dendrogram.tiff",width = 6, height = 8, units = "in", res=300) # <---------------------------------------------------------------------HERE
print(plot(tttree))
dev.off()
is_tip <- tttree$edge[,2] <= length(tttree$tip.label)
ordered_tips <- tttree$edge[is_tip, 2]
dend.order <- rev(tttree$tip.label[ordered_tips])
ppp <- AverageExpression(pbmc)
final.ttt <- ppp[[2]]
final.ttt <- final.ttt[,dend.order]
final.ttt <- t(final.ttt)
final.ttt <- ifelse(final.ttt<0,0,final.ttt)
pal <- colorRampPalette(c("white", "red"))
ccc <- pal(64)
tiff("pbmc.AZ.84.145.heatmap.no.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <--------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="none",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="No z-score",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
tiff("pbmc.AZ.84.145.heatmap.with.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="row",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="z-score (row)",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
pbmc <- pbmc.AZ.90.242 # <----------------------------------------------------------------------------------------------------------------------------------HERE
e <- ElbowPlot(pbmc, ndims=dim(pbmc$pca)[2],reduction="pca")
e <- e[[1]]
e <- e[e[,2]>1,]
e <- max(e[,1])
dims2use <- e
pbmc.tree <- BuildClusterTree(pbmc,dims=1:dims2use)
tttree <- Tool(object = pbmc.tree, slot = 'BuildClusterTree')
tiff("pbmc.AZ.90.242.dendrogram.tiff",width = 6, height = 12, units = "in", res=300) # <---------------------------------------------------------------------HERE
print(plot(tttree))
dev.off()
is_tip <- tttree$edge[,2] <= length(tttree$tip.label)
ordered_tips <- tttree$edge[is_tip, 2]
dend.order <- rev(tttree$tip.label[ordered_tips])
ppp <- AverageExpression(pbmc)
final.ttt <- ppp[[2]]
final.ttt <- final.ttt[,dend.order]
final.ttt <- t(final.ttt)
final.ttt <- ifelse(final.ttt<0,0,final.ttt)
pal <- colorRampPalette(c("white", "red"))
ccc <- pal(64)
tiff("pbmc.AZ.90.242.heatmap.no.zscore.tiff",width = 8, height = 12, units = "in", res=300) # <--------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="none",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="No z-score",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
tiff("pbmc.AZ.90.242.heatmap.with.zscore.tiff",width = 8, height = 12, units = "in", res=300) # <------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="row",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="z-score (row)",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
pbmc <- pbmc.AZ.99.140 # <----------------------------------------------------------------------------------------------------------------------------------HERE
e <- ElbowPlot(pbmc, ndims=dim(pbmc$pca)[2],reduction="pca")
e <- e[[1]]
e <- e[e[,2]>1,]
e <- max(e[,1])
dims2use <- e
pbmc.tree <- BuildClusterTree(pbmc,dims=1:dims2use)
tttree <- Tool(object = pbmc.tree, slot = 'BuildClusterTree')
tiff("pbmc.AZ.99.140.dendrogram.tiff",width = 6, height = 8, units = "in", res=300) # <---------------------------------------------------------------------HERE
print(plot(tttree))
dev.off()
is_tip <- tttree$edge[,2] <= length(tttree$tip.label)
ordered_tips <- tttree$edge[is_tip, 2]
dend.order <- rev(tttree$tip.label[ordered_tips])
ppp <- AverageExpression(pbmc)
final.ttt <- ppp[[2]]
final.ttt <- final.ttt[,dend.order]
final.ttt <- t(final.ttt)
final.ttt <- ifelse(final.ttt<0,0,final.ttt)
pal <- colorRampPalette(c("white", "red"))
ccc <- pal(64)
tiff("pbmc.AZ.99.140.heatmap.no.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <--------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="none",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="No z-score",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
tiff("pbmc.AZ.99.140.heatmap.with.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="row",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="z-score (row)",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
pbmc <- pbmc.PD.52.247 # <----------------------------------------------------------------------------------------------------------------------------------HERE
e <- ElbowPlot(pbmc, ndims=dim(pbmc$pca)[2],reduction="pca")
e <- e[[1]]
e <- e[e[,2]>1,]
e <- max(e[,1])
dims2use <- e
pbmc.tree <- BuildClusterTree(pbmc,dims=1:dims2use)
tttree <- Tool(object = pbmc.tree, slot = 'BuildClusterTree')
tiff("pbmc.PD.52.247.dendrogram.tiff",width = 6, height = 8, units = "in", res=300) # <---------------------------------------------------------------------HERE
print(plot(tttree))
dev.off()
is_tip <- tttree$edge[,2] <= length(tttree$tip.label)
ordered_tips <- tttree$edge[is_tip, 2]
dend.order <- rev(tttree$tip.label[ordered_tips])
ppp <- AverageExpression(pbmc)
final.ttt <- ppp[[2]]
final.ttt <- final.ttt[,dend.order]
final.ttt <- t(final.ttt)
final.ttt <- ifelse(final.ttt<0,0,final.ttt)
pal <- colorRampPalette(c("white", "red"))
ccc <- pal(64)
tiff("pbmc.PD.52.247.heatmap.no.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <--------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="none",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="No z-score",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
tiff("pbmc.PD.52.247.heatmap.with.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="row",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="z-score (row)",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
pbmc <- pbmc.PD.56.236 # <----------------------------------------------------------------------------------------------------------------------------------HERE
e <- ElbowPlot(pbmc, ndims=dim(pbmc$pca)[2],reduction="pca")
e <- e[[1]]
e <- e[e[,2]>1,]
e <- max(e[,1])
dims2use <- e
pbmc.tree <- BuildClusterTree(pbmc,dims=1:dims2use)
tttree <- Tool(object = pbmc.tree, slot = 'BuildClusterTree')
tiff("pbmc.PD.56.236.dendrogram.tiff",width = 6, height = 8, units = "in", res=300) # <---------------------------------------------------------------------HERE
print(plot(tttree))
dev.off()
is_tip <- tttree$edge[,2] <= length(tttree$tip.label)
ordered_tips <- tttree$edge[is_tip, 2]
dend.order <- rev(tttree$tip.label[ordered_tips])
ppp <- AverageExpression(pbmc)
final.ttt <- ppp[[2]]
final.ttt <- final.ttt[,dend.order]
final.ttt <- t(final.ttt)
final.ttt <- ifelse(final.ttt<0,0,final.ttt)
pal <- colorRampPalette(c("white", "red"))
ccc <- pal(64)
tiff("pbmc.PD.56.236.heatmap.no.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <--------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="none",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="No z-score",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
tiff("pbmc.PD.56.236.heatmap.with.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="row",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="z-score (row)",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
pbmc <- pbmc.PD.58.229 # <----------------------------------------------------------------------------------------------------------------------------------HERE
e <- ElbowPlot(pbmc, ndims=dim(pbmc$pca)[2],reduction="pca")
e <- e[[1]]
e <- e[e[,2]>1,]
e <- max(e[,1])
dims2use <- e
pbmc.tree <- BuildClusterTree(pbmc,dims=1:dims2use)
tttree <- Tool(object = pbmc.tree, slot = 'BuildClusterTree')
tiff("pbmc.PD.58.229.dendrogram.tiff",width = 6, height = 8, units = "in", res=300) # <---------------------------------------------------------------------HERE
print(plot(tttree))
dev.off()
is_tip <- tttree$edge[,2] <= length(tttree$tip.label)
ordered_tips <- tttree$edge[is_tip, 2]
dend.order <- rev(tttree$tip.label[ordered_tips])
ppp <- AverageExpression(pbmc)
final.ttt <- ppp[[2]]
final.ttt <- final.ttt[,dend.order]
final.ttt <- t(final.ttt)
final.ttt <- ifelse(final.ttt<0,0,final.ttt)
pal <- colorRampPalette(c("white", "red"))
ccc <- pal(64)
tiff("pbmc.PD.58.229.heatmap.no.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <--------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="none",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="No z-score",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
tiff("pbmc.PD.58.229.heatmap.with.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="row",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="z-score (row)",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
pbmc <- pbmc.PD.77.197 # <----------------------------------------------------------------------------------------------------------------------------------HERE
e <- ElbowPlot(pbmc, ndims=dim(pbmc$pca)[2],reduction="pca")
e <- e[[1]]
e <- e[e[,2]>1,]
e <- max(e[,1])
dims2use <- e
pbmc.tree <- BuildClusterTree(pbmc,dims=1:dims2use)
tttree <- Tool(object = pbmc.tree, slot = 'BuildClusterTree')
tiff("pbmc.PD.77.197.dendrogram.tiff",width = 6, height = 8, units = "in", res=300) # <---------------------------------------------------------------------HERE
print(plot(tttree))
dev.off()
is_tip <- tttree$edge[,2] <= length(tttree$tip.label)
ordered_tips <- tttree$edge[is_tip, 2]
dend.order <- rev(tttree$tip.label[ordered_tips])
ppp <- AverageExpression(pbmc)
final.ttt <- ppp[[2]]
final.ttt <- final.ttt[,dend.order]
final.ttt <- t(final.ttt)
final.ttt <- ifelse(final.ttt<0,0,final.ttt)
pal <- colorRampPalette(c("white", "red"))
ccc <- pal(64)
tiff("pbmc.PD.77.197.heatmap.no.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <--------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="none",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="No z-score",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
tiff("pbmc.PD.77.197.heatmap.with.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="row",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="z-score (row)",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
pbmc <- pbmc.PD.79.201 # <----------------------------------------------------------------------------------------------------------------------------------HERE
e <- ElbowPlot(pbmc, ndims=dim(pbmc$pca)[2],reduction="pca")
e <- e[[1]]
e <- e[e[,2]>1,]
e <- max(e[,1])
dims2use <- e
pbmc.tree <- BuildClusterTree(pbmc,dims=1:dims2use)
tttree <- Tool(object = pbmc.tree, slot = 'BuildClusterTree')
tiff("pbmc.PD.79.201.dendrogram.tiff",width = 6, height = 8, units = "in", res=300) # <---------------------------------------------------------------------HERE
print(plot(tttree))
dev.off()
is_tip <- tttree$edge[,2] <= length(tttree$tip.label)
ordered_tips <- tttree$edge[is_tip, 2]
dend.order <- rev(tttree$tip.label[ordered_tips])
ppp <- AverageExpression(pbmc)
final.ttt <- ppp[[2]]
final.ttt <- final.ttt[,dend.order]
final.ttt <- t(final.ttt)
final.ttt <- ifelse(final.ttt<0,0,final.ttt)
pal <- colorRampPalette(c("white", "red"))
ccc <- pal(64)
tiff("pbmc.PD.79.201.heatmap.no.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <--------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="none",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="No z-score",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
tiff("pbmc.PD.79.201.heatmap.with.zscore.tiff",width = 8, height = 8, units = "in", res=300) # <------------------------------------------------------------HERE
print(hhhmap <- heatmap.2(final.ttt,Rowv=FALSE,Colv=TRUE,dendrogram="column",scale="row",col=ccc,trace="none",margins=c(10, 5),denscol="black",key.xlab="z-score (row)",key.ylab="Frequency",key.title="SCT",symbreaks=FALSE,symkey=FALSE))
dev.off()
scp -vr *.tiff johnsonko@nindsdirbis7.ninds.nih.gov:/shares/PI/Kory_Johnson_and_Amar_Yavatkar/Alan_Koretsky/Helen_Murray/Spatial_Transcriptomics/20201006
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# SpatialDE Prep H250 - 20201029
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setwd("/data/johnsonko/SideProject/Helen/20200811/Analysis_Ready")
library(stringr)
load("Current_20201006.RData")
pbmc.Control.H250.185
DimPlot(pbmc.Control.H250.185,reduction="umap",label=TRUE)
############################################################################
# Comparison A
# Group 1 vs Groups 2, 3, 4, 5, 6, 7, 8
# Clusters 0, 1, 4, 14 vs clusters 2, 3, 6, 7, 11, 16, 13, 15, 9, 5, 8, 12
############################################################################
temp <- as.character(unlist(pbmc.Control.H250.185$seurat_clusters))
temp <- ifelse(temp=="0","A",temp)
temp <- ifelse(temp=="1","A",temp)
temp <- ifelse(temp=="2","B",temp)
temp <- ifelse(temp=="3","B",temp)
temp <- ifelse(temp=="4","A",temp)
temp <- ifelse(temp=="5","B",temp)
temp <- ifelse(temp=="6","B",temp)
temp <- ifelse(temp=="7","B",temp)
temp <- ifelse(temp=="8","B",temp)
temp <- ifelse(temp=="9","B",temp)
temp <- ifelse(temp=="10","X",temp)
temp <- ifelse(temp=="11","B",temp)
temp <- ifelse(temp=="12","B",temp)
temp <- ifelse(temp=="13","B",temp)
temp <- ifelse(temp=="14","A",temp)
temp <- ifelse(temp=="15","B",temp)
temp <- ifelse(temp=="16","B",temp)
temp <- ifelse(temp=="17","X",temp)
temp <- ifelse(temp=="18","X",temp)
temp <- ifelse(temp=="19","X",temp)
temp <- ifelse(temp=="20","X",temp)
temp <- ifelse(temp=="21","X",temp)
temp <- ifelse(temp=="22","X",temp)
temp <- ifelse(temp=="23","X",temp)
temp <- ifelse(temp=="24","X",temp)
temp <- ifelse(temp=="25","X",temp)
pbmc.Control.H250.185@meta.data$comparisonA <- as.factor(temp)
temp <- pbmc.Control.H250.185[,pbmc.Control.H250.185@meta.data$comparisonA!="X"]
temp.data <- GetAssayData(temp, slot = "counts")
temp.data <- as.matrix(temp.data)
temp.data <- t(temp.data)
temp <- dimnames(temp.data)[[1]]
temp <- str_replace_all(temp,"X","x")
dimnames(temp.data)[[1]] <- temp
check.it <- apply(temp.data,1,max)>0
write.csv(temp.data,"comparisonA.csv")
temp <- strsplit(temp,"x")
temp <- as.data.frame(temp)
temp <- t(temp)
dimnames(temp)[[1]] <- dimnames(temp.data)[[1]]
temp <- cbind(temp,apply(temp.data,1,sum))
dimnames(temp)[[2]] <- c("x","y","total_counts")
write.csv(temp,"comparisonA.meta.csv")
# SpatialDE
# https://protect-au.mimecast.com/s/-zOOCWLVEjtX9E82fwl5tK?domain=github.com
cd /data/johnsonko
source /data/johnsonko/conda/etc/profile.d/conda.sh
conda activate base
conda activate project2
pip install openpyxl
sinteractive --mem 500g --time 36:00:00 --gres=lscratch:500
python
import matplotlib.pyplot as plt
import pandas as pd
import NaiveDE
import SpatialDE
counts = pd.read_csv('comparisonA.csv', index_col=0)
sample_info = pd.read_csv('comparisonA.meta.csv', index_col=0)
sample_info
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_a_round1.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_a_round2.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_a_round3.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_a_round4.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_a_round5.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_a_round6.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_a_round7.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_a_round8.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_a_round9.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_a_round10.xlsx')
exit()
conda deactivate
conda deactivate
# crunch
library("openxlsx")
library("xlsx")
rnd1 <- read.xlsx("comparison_a_round1.xlsx","Sheet1",stringsAsFactors = T)
rnd2 <- read.xlsx("comparison_a_round2.xlsx","Sheet1",stringsAsFactors = T)
rnd3 <- read.xlsx("comparison_a_round3.xlsx","Sheet1",stringsAsFactors = T)
rnd4 <- read.xlsx("comparison_a_round4.xlsx","Sheet1",stringsAsFactors = T)
rnd5 <- read.xlsx("comparison_a_round5.xlsx","Sheet1",stringsAsFactors = T)
rnd6 <- read.xlsx("comparison_a_round6.xlsx","Sheet1",stringsAsFactors = T)
rnd7 <- read.xlsx("comparison_a_round7.xlsx","Sheet1",stringsAsFactors = T)
rnd8 <- read.xlsx("comparison_a_round8.xlsx","Sheet1",stringsAsFactors = T)
rnd9 <- read.xlsx("comparison_a_round9.xlsx","Sheet1",stringsAsFactors = T)
rnd10 <- read.xlsx("comparison_a_round10.xlsx","Sheet1",stringsAsFactors = T)
dimnames(rnd1)[[1]] <- rnd1$g
dimnames(rnd2)[[1]] <- rnd2$g
dimnames(rnd3)[[1]] <- rnd3$g
dimnames(rnd4)[[1]] <- rnd4$g
dimnames(rnd5)[[1]] <- rnd5$g
dimnames(rnd6)[[1]] <- rnd6$g
dimnames(rnd7)[[1]] <- rnd7$g
dimnames(rnd8)[[1]] <- rnd8$g
dimnames(rnd9)[[1]] <- rnd9$g
dimnames(rnd10)[[1]] <- rnd10$g
rnd2 <- rnd2[dimnames(rnd2)[[1]],]
rnd3 <- rnd2[dimnames(rnd3)[[1]],]
rnd4 <- rnd2[dimnames(rnd4)[[1]],]
rnd5 <- rnd2[dimnames(rnd5)[[1]],]
rnd6 <- rnd2[dimnames(rnd6)[[1]],]
rnd7 <- rnd2[dimnames(rnd7)[[1]],]
rnd8 <- rnd2[dimnames(rnd8)[[1]],]
rnd9 <- rnd2[dimnames(rnd9)[[1]],]
rnd10 <- rnd2[dimnames(rnd10)[[1]],]
# https://protect-au.mimecast.com/s/WLeyCXLWGktD5QZ1fgxL4B?domain=stats.seandolinar.com
rnd.cx <- cbind(rnd1$LLR,rnd2$LLR,rnd3$LLR,rnd4$LLR,rnd5$LLR,rnd6$LLR,rnd7$LLR,rnd8$LLR,rnd9$LLR,rnd10$LLR)
dimnames(rnd.cx)[[1]] <- dimnames(rnd1)[[1]]
rnd.cx.mean <- apply(rnd.cx,1,mean)
rnd.cx.sd <- apply(rnd.cx,1,sd)
rnd.cx.se <- https://protect-au.mimecast.com/s/T-okCYW8JlTp17XQcDWUHe?domain=rnd.cx.sd
rnd.cx.z <- NULL
rnd.cx.p <- NULL
for (i in 1:length(rnd.cx.mean)) {
rnd.temp1 <- (rnd.cx.mean[i]-mean(rnd.cx.mean[-c(i)]))/sd(rnd.cx.mean[-c(i)])
rnd.cx.z <- c(rnd.cx.z,rnd.temp1)
rnd.temp1 <- 1-pnorm(rnd.temp1)
rnd.cx.p <- c(rnd.cx.p,rnd.temp1)
}
rnd.cx.final <- cbind(rnd.cx.mean,rnd.cx.sd,rnd.cx.se,rnd.cx.z,rnd.cx.p)
write.table(rnd.cx.final,"comparison_a_bootstrap_stats.txt",sep="\t")
############################################################################
# Comparison B
# Group 2 vs Groups 1, 3, 4, 5, 6, 7, 8
# Cluster 2 vs clusters 0, 1, 4, 14, 3, 6, 7, 11, 16, 13, 15, 9, 5, 8, 12
############################################################################
temp <- as.character(unlist(pbmc.Control.H250.185$seurat_clusters))
temp <- ifelse(temp=="0","B",temp)
temp <- ifelse(temp=="1","B",temp)
temp <- ifelse(temp=="2","A",temp)
temp <- ifelse(temp=="3","B",temp)
temp <- ifelse(temp=="4","B",temp)
temp <- ifelse(temp=="5","B",temp)
temp <- ifelse(temp=="6","B",temp)
temp <- ifelse(temp=="7","B",temp)
temp <- ifelse(temp=="8","B",temp)
temp <- ifelse(temp=="9","B",temp)
temp <- ifelse(temp=="10","X",temp)
temp <- ifelse(temp=="11","B",temp)
temp <- ifelse(temp=="12","B",temp)
temp <- ifelse(temp=="13","B",temp)
temp <- ifelse(temp=="14","B",temp)
temp <- ifelse(temp=="15","B",temp)
temp <- ifelse(temp=="16","B",temp)
temp <- ifelse(temp=="17","X",temp)
temp <- ifelse(temp=="18","X",temp)
temp <- ifelse(temp=="19","X",temp)
temp <- ifelse(temp=="20","X",temp)
temp <- ifelse(temp=="21","X",temp)
temp <- ifelse(temp=="22","X",temp)
temp <- ifelse(temp=="23","X",temp)
temp <- ifelse(temp=="24","X",temp)
temp <- ifelse(temp=="25","X",temp)
pbmc.Control.H250.185@meta.data$comparisonA <- as.factor(temp)
temp <- pbmc.Control.H250.185[,pbmc.Control.H250.185@meta.data$comparisonA!="X"]
temp.data <- GetAssayData(temp, slot = "counts")
temp.data <- as.matrix(temp.data)
temp.data <- t(temp.data)
temp <- dimnames(temp.data)[[1]]
temp <- str_replace_all(temp,"X","x")
dimnames(temp.data)[[1]] <- temp
write.csv(temp.data,"comparisonB.csv")
temp <- strsplit(temp,"x")
temp <- as.data.frame(temp)
temp <- t(temp)
dimnames(temp)[[1]] <- dimnames(temp.data)[[1]]
temp <- cbind(temp,apply(temp.data,1,sum))
dimnames(temp)[[2]] <- c("x","y","total_counts")
write.csv(temp,"comparisonB.meta.csv")
# SpatialDE
# https://protect-au.mimecast.com/s/-zOOCWLVEjtX9E82fwl5tK?domain=github.com
cd /data/johnsonko
source /data/johnsonko/conda/etc/profile.d/conda.sh
conda activate base
conda activate project2
pip install openpyxl
sinteractive --mem 500g --time 36:00:00 --gres=lscratch:500
python
import matplotlib.pyplot as plt
import pandas as pd
import NaiveDE
import SpatialDE
counts = pd.read_csv('comparisonB.csv', index_col=0)
sample_info = pd.read_csv('comparisonB.meta.csv', index_col=0)
sample_info
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_b_round1.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_b_round2.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_b_round3.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_b_round4.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_b_round5.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_b_round6.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_b_round7.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_b_round8.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_b_round9.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_b_round10.xlsx')
exit()
conda deactivate
conda deactivate
# crunch
library("openxlsx")
library("xlsx")
rnd1 <- read.xlsx("comparison_b_round1.xlsx","Sheet1",stringsAsFactors = T)
rnd2 <- read.xlsx("comparison_b_round2.xlsx","Sheet1",stringsAsFactors = T)
rnd3 <- read.xlsx("comparison_b_round3.xlsx","Sheet1",stringsAsFactors = T)
rnd4 <- read.xlsx("comparison_b_round4.xlsx","Sheet1",stringsAsFactors = T)
rnd5 <- read.xlsx("comparison_b_round5.xlsx","Sheet1",stringsAsFactors = T)
rnd6 <- read.xlsx("comparison_b_round6.xlsx","Sheet1",stringsAsFactors = T)
rnd7 <- read.xlsx("comparison_b_round7.xlsx","Sheet1",stringsAsFactors = T)
rnd8 <- read.xlsx("comparison_b_round8.xlsx","Sheet1",stringsAsFactors = T)
rnd9 <- read.xlsx("comparison_b_round9.xlsx","Sheet1",stringsAsFactors = T)
rnd10 <- read.xlsx("comparison_b_round10.xlsx","Sheet1",stringsAsFactors = T)
dimnames(rnd1)[[1]] <- rnd1$g
dimnames(rnd2)[[1]] <- rnd2$g
dimnames(rnd3)[[1]] <- rnd3$g
dimnames(rnd4)[[1]] <- rnd4$g
dimnames(rnd5)[[1]] <- rnd5$g
dimnames(rnd6)[[1]] <- rnd6$g
dimnames(rnd7)[[1]] <- rnd7$g
dimnames(rnd8)[[1]] <- rnd8$g
dimnames(rnd9)[[1]] <- rnd9$g
dimnames(rnd10)[[1]] <- rnd10$g
rnd2 <- rnd2[dimnames(rnd2)[[1]],]
rnd3 <- rnd2[dimnames(rnd3)[[1]],]
rnd4 <- rnd2[dimnames(rnd4)[[1]],]
rnd5 <- rnd2[dimnames(rnd5)[[1]],]
rnd6 <- rnd2[dimnames(rnd6)[[1]],]
rnd7 <- rnd2[dimnames(rnd7)[[1]],]
rnd8 <- rnd2[dimnames(rnd8)[[1]],]
rnd9 <- rnd2[dimnames(rnd9)[[1]],]
rnd10 <- rnd2[dimnames(rnd10)[[1]],]
rnd.cx <- cbind(rnd1$LLR,rnd2$LLR,rnd3$LLR,rnd4$LLR,rnd5$LLR,rnd6$LLR,rnd7$LLR,rnd8$LLR,rnd9$LLR,rnd10$LLR)
dimnames(rnd.cx)[[1]] <- dimnames(rnd1)[[1]]
rnd.cx.mean <- apply(rnd.cx,1,mean)
rnd.cx.sd <- apply(rnd.cx,1,sd)
rnd.cx.se <- https://protect-au.mimecast.com/s/T-okCYW8JlTp17XQcDWUHe?domain=rnd.cx.sd
rnd.cx.z <- NULL
rnd.cx.p <- NULL
for (i in 1:length(rnd.cx.mean)) {
rnd.temp1 <- (rnd.cx.mean[i]-mean(rnd.cx.mean[-c(i)]))/sd(rnd.cx.mean[-c(i)])
rnd.cx.z <- c(rnd.cx.z,rnd.temp1)
rnd.temp1 <- 1-pnorm(rnd.temp1)
rnd.cx.p <- c(rnd.cx.p,rnd.temp1)
}
rnd.cx.final <- cbind(rnd.cx.mean,rnd.cx.sd,rnd.cx.se,rnd.cx.z,rnd.cx.p)
write.table(rnd.cx.final,"comparison_b_bootstrap_stats.txt",sep="\t")
############################################################################
# Comparison C
# Group 3 vs Groups 1, 2, 4, 5, 6, 7, 8
# Cluster 3 vs clusters 0, 1, 4, 14, 2, 6, 7, 11, 16, 13, 15, 9, 5, 8, 12
############################################################################
temp <- as.character(unlist(pbmc.Control.H250.185$seurat_clusters))
temp <- ifelse(temp=="0","B",temp)
temp <- ifelse(temp=="1","B",temp)
temp <- ifelse(temp=="2","B",temp)
temp <- ifelse(temp=="3","A",temp)
temp <- ifelse(temp=="4","B",temp)
temp <- ifelse(temp=="5","B",temp)
temp <- ifelse(temp=="6","B",temp)
temp <- ifelse(temp=="7","B",temp)
temp <- ifelse(temp=="8","B",temp)
temp <- ifelse(temp=="9","B",temp)
temp <- ifelse(temp=="10","X",temp)
temp <- ifelse(temp=="11","B",temp)
temp <- ifelse(temp=="12","B",temp)
temp <- ifelse(temp=="13","B",temp)
temp <- ifelse(temp=="14","B",temp)
temp <- ifelse(temp=="15","B",temp)
temp <- ifelse(temp=="16","B",temp)
temp <- ifelse(temp=="17","X",temp)
temp <- ifelse(temp=="18","X",temp)
temp <- ifelse(temp=="19","X",temp)
temp <- ifelse(temp=="20","X",temp)
temp <- ifelse(temp=="21","X",temp)
temp <- ifelse(temp=="22","X",temp)
temp <- ifelse(temp=="23","X",temp)
temp <- ifelse(temp=="24","X",temp)
temp <- ifelse(temp=="25","X",temp)
pbmc.Control.H250.185@meta.data$comparisonA <- as.factor(temp)
temp <- pbmc.Control.H250.185[,pbmc.Control.H250.185@meta.data$comparisonA!="X"]
temp.data <- GetAssayData(temp, slot = "counts")
temp.data <- as.matrix(temp.data)
temp.data <- t(temp.data)
temp <- dimnames(temp.data)[[1]]
temp <- str_replace_all(temp,"X","x")
dimnames(temp.data)[[1]] <- temp
write.csv(temp.data,"comparisonC.csv")
temp <- strsplit(temp,"x")
temp <- as.data.frame(temp)
temp <- t(temp)
dimnames(temp)[[1]] <- dimnames(temp.data)[[1]]
temp <- cbind(temp,apply(temp.data,1,sum))
dimnames(temp)[[2]] <- c("x","y","total_counts")
write.csv(temp,"comparisonC.meta.csv")
# SpatialDE
# https://protect-au.mimecast.com/s/-zOOCWLVEjtX9E82fwl5tK?domain=github.com
cd /data/johnsonko
source /data/johnsonko/conda/etc/profile.d/conda.sh
conda activate base
conda activate project2
pip install openpyxl
sinteractive --mem 500g --time 36:00:00 --gres=lscratch:500
python
import matplotlib.pyplot as plt
import pandas as pd
import NaiveDE
import SpatialDE
counts = pd.read_csv('comparisonC.csv', index_col=0)
sample_info = pd.read_csv('comparisonC.meta.csv', index_col=0)
sample_info
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_c_round1.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_c_round2.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_c_round3.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_c_round4.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_c_round5.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_c_round6.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_c_round7.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_c_round8.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_c_round9.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_c_round10.xlsx')
exit()
conda deactivate
conda deactivate
# crunch
library("openxlsx")
library("xlsx")
rnd1 <- read.xlsx("comparison_c_round1.xlsx","Sheet1",stringsAsFactors = T)
rnd2 <- read.xlsx("comparison_c_round2.xlsx","Sheet1",stringsAsFactors = T)
rnd3 <- read.xlsx("comparison_c_round3.xlsx","Sheet1",stringsAsFactors = T)
rnd4 <- read.xlsx("comparison_c_round4.xlsx","Sheet1",stringsAsFactors = T)
rnd5 <- read.xlsx("comparison_c_round5.xlsx","Sheet1",stringsAsFactors = T)
rnd6 <- read.xlsx("comparison_c_round6.xlsx","Sheet1",stringsAsFactors = T)
rnd7 <- read.xlsx("comparison_c_round7.xlsx","Sheet1",stringsAsFactors = T)
rnd8 <- read.xlsx("comparison_c_round8.xlsx","Sheet1",stringsAsFactors = T)
rnd9 <- read.xlsx("comparison_c_round9.xlsx","Sheet1",stringsAsFactors = T)
rnd10 <- read.xlsx("comparison_c_round10.xlsx","Sheet1",stringsAsFactors = T)
dimnames(rnd1)[[1]] <- rnd1$g
dimnames(rnd2)[[1]] <- rnd2$g
dimnames(rnd3)[[1]] <- rnd3$g
dimnames(rnd4)[[1]] <- rnd4$g
dimnames(rnd5)[[1]] <- rnd5$g
dimnames(rnd6)[[1]] <- rnd6$g
dimnames(rnd7)[[1]] <- rnd7$g
dimnames(rnd8)[[1]] <- rnd8$g
dimnames(rnd9)[[1]] <- rnd9$g
dimnames(rnd10)[[1]] <- rnd10$g
rnd2 <- rnd2[dimnames(rnd2)[[1]],]
rnd3 <- rnd2[dimnames(rnd3)[[1]],]
rnd4 <- rnd2[dimnames(rnd4)[[1]],]
rnd5 <- rnd2[dimnames(rnd5)[[1]],]
rnd6 <- rnd2[dimnames(rnd6)[[1]],]
rnd7 <- rnd2[dimnames(rnd7)[[1]],]
rnd8 <- rnd2[dimnames(rnd8)[[1]],]
rnd9 <- rnd2[dimnames(rnd9)[[1]],]
rnd10 <- rnd2[dimnames(rnd10)[[1]],]
rnd.cx <- cbind(rnd1$LLR,rnd2$LLR,rnd3$LLR,rnd4$LLR,rnd5$LLR,rnd6$LLR,rnd7$LLR,rnd8$LLR,rnd9$LLR,rnd10$LLR)
dimnames(rnd.cx)[[1]] <- dimnames(rnd1)[[1]]
rnd.cx.mean <- apply(rnd.cx,1,mean)
rnd.cx.sd <- apply(rnd.cx,1,sd)
rnd.cx.se <- https://protect-au.mimecast.com/s/T-okCYW8JlTp17XQcDWUHe?domain=rnd.cx.sd
rnd.cx.z <- NULL
rnd.cx.p <- NULL
for (i in 1:length(rnd.cx.mean)) {
rnd.temp1 <- (rnd.cx.mean[i]-mean(rnd.cx.mean[-c(i)]))/sd(rnd.cx.mean[-c(i)])
rnd.cx.z <- c(rnd.cx.z,rnd.temp1)
rnd.temp1 <- 1-pnorm(rnd.temp1)
rnd.cx.p <- c(rnd.cx.p,rnd.temp1)
}
rnd.cx.final <- cbind(rnd.cx.mean,rnd.cx.sd,rnd.cx.se,rnd.cx.z,rnd.cx.p)
write.table(rnd.cx.final,"comparison_c_bootstrap_stats.txt",sep="\t")
############################################################################
# Comparison D
# Group 4 vs Groups 1, 2, 3, 5, 6, 7, 8
# Cluster 6, 7, 11 vs clusters 0, 1, 4, 14, 2, 3, 16, 13, 15, 9, 5, 8, 12
############################################################################
temp <- as.character(unlist(pbmc.Control.H250.185$seurat_clusters))
temp <- ifelse(temp=="0","B",temp)
temp <- ifelse(temp=="1","B",temp)
temp <- ifelse(temp=="2","B",temp)
temp <- ifelse(temp=="3","B",temp)
temp <- ifelse(temp=="4","B",temp)
temp <- ifelse(temp=="5","B",temp)
temp <- ifelse(temp=="6","A",temp)
temp <- ifelse(temp=="7","A",temp)
temp <- ifelse(temp=="8","B",temp)
temp <- ifelse(temp=="9","B",temp)
temp <- ifelse(temp=="10","X",temp)
temp <- ifelse(temp=="11","A",temp)
temp <- ifelse(temp=="12","B",temp)
temp <- ifelse(temp=="13","B",temp)
temp <- ifelse(temp=="14","B",temp)
temp <- ifelse(temp=="15","B",temp)
temp <- ifelse(temp=="16","B",temp)
temp <- ifelse(temp=="17","X",temp)
temp <- ifelse(temp=="18","X",temp)
temp <- ifelse(temp=="19","X",temp)
temp <- ifelse(temp=="20","X",temp)
temp <- ifelse(temp=="21","X",temp)
temp <- ifelse(temp=="22","X",temp)
temp <- ifelse(temp=="23","X",temp)
temp <- ifelse(temp=="24","X",temp)
temp <- ifelse(temp=="25","X",temp)
pbmc.Control.H250.185@meta.data$comparisonA <- as.factor(temp)
temp <- pbmc.Control.H250.185[,pbmc.Control.H250.185@meta.data$comparisonA!="X"]
temp.data <- GetAssayData(temp, slot = "counts")
temp.data <- as.matrix(temp.data)
temp.data <- t(temp.data)
temp <- dimnames(temp.data)[[1]]
temp <- str_replace_all(temp,"X","x")
dimnames(temp.data)[[1]] <- temp
write.csv(temp.data,"comparisonD.csv")
temp <- strsplit(temp,"x")
temp <- as.data.frame(temp)
temp <- t(temp)
dimnames(temp)[[1]] <- dimnames(temp.data)[[1]]
temp <- cbind(temp,apply(temp.data,1,sum))
dimnames(temp)[[2]] <- c("x","y","total_counts")
write.csv(temp,"comparisonD.meta.csv")
# SpatialDE
# https://protect-au.mimecast.com/s/-zOOCWLVEjtX9E82fwl5tK?domain=github.com
cd /data/johnsonko
source /data/johnsonko/conda/etc/profile.d/conda.sh
conda activate base
conda activate project2
pip install openpyxl
sinteractive --mem 500g --time 36:00:00 --gres=lscratch:500
python
import matplotlib.pyplot as plt
import pandas as pd
import NaiveDE
import SpatialDE
counts = pd.read_csv('comparisonD.csv', index_col=0)
sample_info = pd.read_csv('comparisonD.meta.csv', index_col=0)
sample_info
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_d_round1.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_d_round2.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_d_round3.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_d_round4.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_d_round5.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_d_round6.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_d_round7.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_d_round8.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_d_round9.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_d_round10.xlsx')
exit()
conda deactivate
conda deactivate
# crunch
library("openxlsx")
library("xlsx")
rnd1 <- read.xlsx("comparison_d_round1.xlsx","Sheet1",stringsAsFactors = T)
rnd2 <- read.xlsx("comparison_d_round2.xlsx","Sheet1",stringsAsFactors = T)
rnd3 <- read.xlsx("comparison_d_round3.xlsx","Sheet1",stringsAsFactors = T)
rnd4 <- read.xlsx("comparison_d_round4.xlsx","Sheet1",stringsAsFactors = T)
rnd5 <- read.xlsx("comparison_d_round5.xlsx","Sheet1",stringsAsFactors = T)
rnd6 <- read.xlsx("comparison_d_round6.xlsx","Sheet1",stringsAsFactors = T)
rnd7 <- read.xlsx("comparison_d_round7.xlsx","Sheet1",stringsAsFactors = T)
rnd8 <- read.xlsx("comparison_d_round8.xlsx","Sheet1",stringsAsFactors = T)
rnd9 <- read.xlsx("comparison_d_round9.xlsx","Sheet1",stringsAsFactors = T)
rnd10 <- read.xlsx("comparison_d_round10.xlsx","Sheet1",stringsAsFactors = T)
dimnames(rnd1)[[1]] <- rnd1$g
dimnames(rnd2)[[1]] <- rnd2$g
dimnames(rnd3)[[1]] <- rnd3$g
dimnames(rnd4)[[1]] <- rnd4$g
dimnames(rnd5)[[1]] <- rnd5$g
dimnames(rnd6)[[1]] <- rnd6$g
dimnames(rnd7)[[1]] <- rnd7$g
dimnames(rnd8)[[1]] <- rnd8$g
dimnames(rnd9)[[1]] <- rnd9$g
dimnames(rnd10)[[1]] <- rnd10$g
rnd2 <- rnd2[dimnames(rnd2)[[1]],]
rnd3 <- rnd2[dimnames(rnd3)[[1]],]
rnd4 <- rnd2[dimnames(rnd4)[[1]],]
rnd5 <- rnd2[dimnames(rnd5)[[1]],]
rnd6 <- rnd2[dimnames(rnd6)[[1]],]
rnd7 <- rnd2[dimnames(rnd7)[[1]],]
rnd8 <- rnd2[dimnames(rnd8)[[1]],]
rnd9 <- rnd2[dimnames(rnd9)[[1]],]
rnd10 <- rnd2[dimnames(rnd10)[[1]],]
rnd.cx <- cbind(rnd1$LLR,rnd2$LLR,rnd3$LLR,rnd4$LLR,rnd5$LLR,rnd6$LLR,rnd7$LLR,rnd8$LLR,rnd9$LLR,rnd10$LLR)
dimnames(rnd.cx)[[1]] <- dimnames(rnd1)[[1]]
rnd.cx.mean <- apply(rnd.cx,1,mean)
rnd.cx.sd <- apply(rnd.cx,1,sd)
rnd.cx.se <- https://protect-au.mimecast.com/s/T-okCYW8JlTp17XQcDWUHe?domain=rnd.cx.sd
rnd.cx.z <- NULL
rnd.cx.p <- NULL
for (i in 1:length(rnd.cx.mean)) {
rnd.temp1 <- (rnd.cx.mean[i]-mean(rnd.cx.mean[-c(i)]))/sd(rnd.cx.mean[-c(i)])
rnd.cx.z <- c(rnd.cx.z,rnd.temp1)
rnd.temp1 <- 1-pnorm(rnd.temp1)
rnd.cx.p <- c(rnd.cx.p,rnd.temp1)
}
rnd.cx.final <- cbind(rnd.cx.mean,rnd.cx.sd,rnd.cx.se,rnd.cx.z,rnd.cx.p)
write.table(rnd.cx.final,"comparison_d_bootstrap_stats.txt",sep="\t")
############################################################################
# Comparison E
# Group 5 vs Groups 1, 2, 3, 4, 6, 7, 8
# Cluster 16 vs clusters 0, 1, 4, 14, 2, 3, 6, 7, 11, 13, 15, 9, 5, 8, 12
############################################################################
temp <- as.character(unlist(pbmc.Control.H250.185$seurat_clusters))
temp <- ifelse(temp=="0","B",temp)
temp <- ifelse(temp=="1","B",temp)
temp <- ifelse(temp=="2","B",temp)
temp <- ifelse(temp=="3","B",temp)
temp <- ifelse(temp=="4","B",temp)
temp <- ifelse(temp=="5","B",temp)
temp <- ifelse(temp=="6","B",temp)
temp <- ifelse(temp=="7","B",temp)
temp <- ifelse(temp=="8","B",temp)
temp <- ifelse(temp=="9","B",temp)
temp <- ifelse(temp=="10","X",temp)
temp <- ifelse(temp=="11","B",temp)
temp <- ifelse(temp=="12","B",temp)
temp <- ifelse(temp=="13","B",temp)
temp <- ifelse(temp=="14","B",temp)
temp <- ifelse(temp=="15","B",temp)
temp <- ifelse(temp=="16","A",temp)
temp <- ifelse(temp=="17","X",temp)
temp <- ifelse(temp=="18","X",temp)
temp <- ifelse(temp=="19","X",temp)
temp <- ifelse(temp=="20","X",temp)
temp <- ifelse(temp=="21","X",temp)
temp <- ifelse(temp=="22","X",temp)
temp <- ifelse(temp=="23","X",temp)
temp <- ifelse(temp=="24","X",temp)
temp <- ifelse(temp=="25","X",temp)
pbmc.Control.H250.185@meta.data$comparisonA <- as.factor(temp)
temp <- pbmc.Control.H250.185[,pbmc.Control.H250.185@meta.data$comparisonA!="X"]
temp.data <- GetAssayData(temp, slot = "counts")
temp.data <- as.matrix(temp.data)
temp.data <- t(temp.data)
temp <- dimnames(temp.data)[[1]]
temp <- str_replace_all(temp,"X","x")
dimnames(temp.data)[[1]] <- temp
write.csv(temp.data,"comparisonE.csv")
temp <- strsplit(temp,"x")
temp <- as.data.frame(temp)
temp <- t(temp)
dimnames(temp)[[1]] <- dimnames(temp.data)[[1]]
temp <- cbind(temp,apply(temp.data,1,sum))
dimnames(temp)[[2]] <- c("x","y","total_counts")
write.csv(temp,"comparisonE.meta.csv")
# SpatialDE
# https://protect-au.mimecast.com/s/-zOOCWLVEjtX9E82fwl5tK?domain=github.com
cd /data/johnsonko
source /data/johnsonko/conda/etc/profile.d/conda.sh
conda activate base
conda activate project2
pip install openpyxl
sinteractive --mem 500g --time 36:00:00 --gres=lscratch:500
python
import matplotlib.pyplot as plt
import pandas as pd
import NaiveDE
import SpatialDE
counts = pd.read_csv('comparisonE.csv', index_col=0)
sample_info = pd.read_csv('comparisonE.meta.csv', index_col=0)
sample_info
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_e_round1.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_e_round2.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_e_round3.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_e_round4.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_e_round5.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_e_round6.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_e_round7.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_e_round8.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_e_round9.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_e_round10.xlsx')
exit()
conda deactivate
conda deactivate
# crunch
library("openxlsx")
library("xlsx")
rnd1 <- read.xlsx("comparison_e_round1.xlsx","Sheet1",stringsAsFactors = T)
rnd2 <- read.xlsx("comparison_e_round2.xlsx","Sheet1",stringsAsFactors = T)
rnd3 <- read.xlsx("comparison_e_round3.xlsx","Sheet1",stringsAsFactors = T)
rnd4 <- read.xlsx("comparison_e_round4.xlsx","Sheet1",stringsAsFactors = T)
rnd5 <- read.xlsx("comparison_e_round5.xlsx","Sheet1",stringsAsFactors = T)
rnd6 <- read.xlsx("comparison_e_round6.xlsx","Sheet1",stringsAsFactors = T)
rnd7 <- read.xlsx("comparison_e_round7.xlsx","Sheet1",stringsAsFactors = T)
rnd8 <- read.xlsx("comparison_e_round8.xlsx","Sheet1",stringsAsFactors = T)
rnd9 <- read.xlsx("comparison_e_round9.xlsx","Sheet1",stringsAsFactors = T)
rnd10 <- read.xlsx("comparison_e_round10.xlsx","Sheet1",stringsAsFactors = T)
dimnames(rnd1)[[1]] <- rnd1$g
dimnames(rnd2)[[1]] <- rnd2$g
dimnames(rnd3)[[1]] <- rnd3$g
dimnames(rnd4)[[1]] <- rnd4$g
dimnames(rnd5)[[1]] <- rnd5$g
dimnames(rnd6)[[1]] <- rnd6$g
dimnames(rnd7)[[1]] <- rnd7$g
dimnames(rnd8)[[1]] <- rnd8$g
dimnames(rnd9)[[1]] <- rnd9$g
dimnames(rnd10)[[1]] <- rnd10$g
rnd2 <- rnd2[dimnames(rnd2)[[1]],]
rnd3 <- rnd2[dimnames(rnd3)[[1]],]
rnd4 <- rnd2[dimnames(rnd4)[[1]],]
rnd5 <- rnd2[dimnames(rnd5)[[1]],]
rnd6 <- rnd2[dimnames(rnd6)[[1]],]
rnd7 <- rnd2[dimnames(rnd7)[[1]],]
rnd8 <- rnd2[dimnames(rnd8)[[1]],]
rnd9 <- rnd2[dimnames(rnd9)[[1]],]
rnd10 <- rnd2[dimnames(rnd10)[[1]],]
rnd.cx <- cbind(rnd1$LLR,rnd2$LLR,rnd3$LLR,rnd4$LLR,rnd5$LLR,rnd6$LLR,rnd7$LLR,rnd8$LLR,rnd9$LLR,rnd10$LLR)
dimnames(rnd.cx)[[1]] <- dimnames(rnd1)[[1]]
rnd.cx.mean <- apply(rnd.cx,1,mean)
rnd.cx.sd <- apply(rnd.cx,1,sd)
rnd.cx.se <- https://protect-au.mimecast.com/s/T-okCYW8JlTp17XQcDWUHe?domain=rnd.cx.sd
rnd.cx.z <- NULL
rnd.cx.p <- NULL
for (i in 1:length(rnd.cx.mean)) {
rnd.temp1 <- (rnd.cx.mean[i]-mean(rnd.cx.mean[-c(i)]))/sd(rnd.cx.mean[-c(i)])
rnd.cx.z <- c(rnd.cx.z,rnd.temp1)
rnd.temp1 <- 1-pnorm(rnd.temp1)
rnd.cx.p <- c(rnd.cx.p,rnd.temp1)
}
rnd.cx.final <- cbind(rnd.cx.mean,rnd.cx.sd,rnd.cx.se,rnd.cx.z,rnd.cx.p)
write.table(rnd.cx.final,"comparison_e_bootstrap_stats.txt",sep="\t")
############################################################################
# Comparison F
# Group 6 vs Groups 1, 2, 3, 4, 5, 7, 8
# Cluster 13, 15 vs clusters 0, 1, 4, 14, 2, 3, 6, 7, 11, 16, 9, 5, 8, 12
############################################################################
temp <- as.character(unlist(pbmc.Control.H250.185$seurat_clusters))
temp <- ifelse(temp=="0","B",temp)
temp <- ifelse(temp=="1","B",temp)
temp <- ifelse(temp=="2","B",temp)
temp <- ifelse(temp=="3","B",temp)
temp <- ifelse(temp=="4","B",temp)
temp <- ifelse(temp=="5","B",temp)
temp <- ifelse(temp=="6","B",temp)
temp <- ifelse(temp=="7","B",temp)
temp <- ifelse(temp=="8","B",temp)
temp <- ifelse(temp=="9","B",temp)
temp <- ifelse(temp=="10","X",temp)
temp <- ifelse(temp=="11","B",temp)
temp <- ifelse(temp=="12","B",temp)
temp <- ifelse(temp=="13","A",temp)
temp <- ifelse(temp=="14","B",temp)
temp <- ifelse(temp=="15","A",temp)
temp <- ifelse(temp=="16","B",temp)
temp <- ifelse(temp=="17","X",temp)
temp <- ifelse(temp=="18","X",temp)
temp <- ifelse(temp=="19","X",temp)
temp <- ifelse(temp=="20","X",temp)
temp <- ifelse(temp=="21","X",temp)
temp <- ifelse(temp=="22","X",temp)
temp <- ifelse(temp=="23","X",temp)
temp <- ifelse(temp=="24","X",temp)
temp <- ifelse(temp=="25","X",temp)
pbmc.Control.H250.185@meta.data$comparisonA <- as.factor(temp)
temp <- pbmc.Control.H250.185[,pbmc.Control.H250.185@meta.data$comparisonA!="X"]
temp.data <- GetAssayData(temp, slot = "counts")
temp.data <- as.matrix(temp.data)
temp.data <- t(temp.data)
temp <- dimnames(temp.data)[[1]]
temp <- str_replace_all(temp,"X","x")
dimnames(temp.data)[[1]] <- temp
write.csv(temp.data,"comparisonF.csv")
temp <- strsplit(temp,"x")
temp <- as.data.frame(temp)
temp <- t(temp)
dimnames(temp)[[1]] <- dimnames(temp.data)[[1]]
temp <- cbind(temp,apply(temp.data,1,sum))
dimnames(temp)[[2]] <- c("x","y","total_counts")
write.csv(temp,"comparisonF.meta.csv")
# SpatialDE
# https://protect-au.mimecast.com/s/-zOOCWLVEjtX9E82fwl5tK?domain=github.com
cd /data/johnsonko
source /data/johnsonko/conda/etc/profile.d/conda.sh
conda activate base
conda activate project2
pip install openpyxl
sinteractive --mem 500g --time 36:00:00 --gres=lscratch:500
python
import matplotlib.pyplot as plt
import pandas as pd
import NaiveDE
import SpatialDE
counts = pd.read_csv('comparisonF.csv', index_col=0)
sample_info = pd.read_csv('comparisonF.meta.csv', index_col=0)
sample_info
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_f_round1.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_f_round2.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_f_round3.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_f_round4.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_f_round5.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_f_round6.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_f_round7.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_f_round8.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_f_round9.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_f_round10.xlsx')
exit()
conda deactivate
conda deactivate
# crunch
library("openxlsx")
library("xlsx")
rnd1 <- read.xlsx("comparison_f_round1.xlsx","Sheet1",stringsAsFactors = T)
rnd2 <- read.xlsx("comparison_f_round2.xlsx","Sheet1",stringsAsFactors = T)
rnd3 <- read.xlsx("comparison_f_round3.xlsx","Sheet1",stringsAsFactors = T)
rnd4 <- read.xlsx("comparison_f_round4.xlsx","Sheet1",stringsAsFactors = T)
rnd5 <- read.xlsx("comparison_f_round5.xlsx","Sheet1",stringsAsFactors = T)
rnd6 <- read.xlsx("comparison_f_round6.xlsx","Sheet1",stringsAsFactors = T)
rnd7 <- read.xlsx("comparison_f_round7.xlsx","Sheet1",stringsAsFactors = T)
rnd8 <- read.xlsx("comparison_f_round8.xlsx","Sheet1",stringsAsFactors = T)
rnd9 <- read.xlsx("comparison_f_round9.xlsx","Sheet1",stringsAsFactors = T)
rnd10 <- read.xlsx("comparison_f_round10.xlsx","Sheet1",stringsAsFactors = T)
dimnames(rnd1)[[1]] <- rnd1$g
dimnames(rnd2)[[1]] <- rnd2$g
dimnames(rnd3)[[1]] <- rnd3$g
dimnames(rnd4)[[1]] <- rnd4$g
dimnames(rnd5)[[1]] <- rnd5$g
dimnames(rnd6)[[1]] <- rnd6$g
dimnames(rnd7)[[1]] <- rnd7$g
dimnames(rnd8)[[1]] <- rnd8$g
dimnames(rnd9)[[1]] <- rnd9$g
dimnames(rnd10)[[1]] <- rnd10$g
rnd2 <- rnd2[dimnames(rnd2)[[1]],]
rnd3 <- rnd2[dimnames(rnd3)[[1]],]
rnd4 <- rnd2[dimnames(rnd4)[[1]],]
rnd5 <- rnd2[dimnames(rnd5)[[1]],]
rnd6 <- rnd2[dimnames(rnd6)[[1]],]
rnd7 <- rnd2[dimnames(rnd7)[[1]],]
rnd8 <- rnd2[dimnames(rnd8)[[1]],]
rnd9 <- rnd2[dimnames(rnd9)[[1]],]
rnd10 <- rnd2[dimnames(rnd10)[[1]],]
rnd.cx <- cbind(rnd1$LLR,rnd2$LLR,rnd3$LLR,rnd4$LLR,rnd5$LLR,rnd6$LLR,rnd7$LLR,rnd8$LLR,rnd9$LLR,rnd10$LLR)
dimnames(rnd.cx)[[1]] <- dimnames(rnd1)[[1]]
rnd.cx.mean <- apply(rnd.cx,1,mean)
rnd.cx.sd <- apply(rnd.cx,1,sd)
rnd.cx.se <- https://protect-au.mimecast.com/s/T-okCYW8JlTp17XQcDWUHe?domain=rnd.cx.sd
rnd.cx.z <- NULL
rnd.cx.p <- NULL
for (i in 1:length(rnd.cx.mean)) {
rnd.temp1 <- (rnd.cx.mean[i]-mean(rnd.cx.mean[-c(i)]))/sd(rnd.cx.mean[-c(i)])
rnd.cx.z <- c(rnd.cx.z,rnd.temp1)
rnd.temp1 <- 1-pnorm(rnd.temp1)
rnd.cx.p <- c(rnd.cx.p,rnd.temp1)
}
rnd.cx.final <- cbind(rnd.cx.mean,rnd.cx.sd,rnd.cx.se,rnd.cx.z,rnd.cx.p)
write.table(rnd.cx.final,"comparison_f_bootstrap_stats.txt",sep="\t")
############################################################################
# Comparison G
# Group 7 vs Groups 1, 2, 3, 4, 5, 6, 8
# Cluster 9 vs clusters 0, 1, 4, 14, 2, 3, 6, 7, 11, 13, 15, 16, 5, 8, 12
############################################################################
temp <- as.character(unlist(pbmc.Control.H250.185$seurat_clusters))
temp <- ifelse(temp=="0","B",temp)
temp <- ifelse(temp=="1","B",temp)
temp <- ifelse(temp=="2","B",temp)
temp <- ifelse(temp=="3","B",temp)
temp <- ifelse(temp=="4","B",temp)
temp <- ifelse(temp=="5","B",temp)
temp <- ifelse(temp=="6","B",temp)
temp <- ifelse(temp=="7","B",temp)
temp <- ifelse(temp=="8","B",temp)
temp <- ifelse(temp=="9","A",temp)
temp <- ifelse(temp=="10","X",temp)
temp <- ifelse(temp=="11","B",temp)
temp <- ifelse(temp=="12","B",temp)
temp <- ifelse(temp=="13","B",temp)
temp <- ifelse(temp=="14","B",temp)
temp <- ifelse(temp=="15","B",temp)
temp <- ifelse(temp=="16","B",temp)
temp <- ifelse(temp=="17","X",temp)
temp <- ifelse(temp=="18","X",temp)
temp <- ifelse(temp=="19","X",temp)
temp <- ifelse(temp=="20","X",temp)
temp <- ifelse(temp=="21","X",temp)
temp <- ifelse(temp=="22","X",temp)
temp <- ifelse(temp=="23","X",temp)
temp <- ifelse(temp=="24","X",temp)
temp <- ifelse(temp=="25","X",temp)
pbmc.Control.H250.185@meta.data$comparisonA <- as.factor(temp)
temp <- pbmc.Control.H250.185[,pbmc.Control.H250.185@meta.data$comparisonA!="X"]
temp.data <- GetAssayData(temp, slot = "counts")
temp.data <- as.matrix(temp.data)
temp.data <- t(temp.data)
temp <- dimnames(temp.data)[[1]]
temp <- str_replace_all(temp,"X","x")
dimnames(temp.data)[[1]] <- temp
write.csv(temp.data,"comparisonG.csv")
temp <- strsplit(temp,"x")
temp <- as.data.frame(temp)
temp <- t(temp)
dimnames(temp)[[1]] <- dimnames(temp.data)[[1]]
temp <- cbind(temp,apply(temp.data,1,sum))
dimnames(temp)[[2]] <- c("x","y","total_counts")
write.csv(temp,"comparisonG.meta.csv")
# SpatialDE
# https://protect-au.mimecast.com/s/-zOOCWLVEjtX9E82fwl5tK?domain=github.com
cd /data/johnsonko
source /data/johnsonko/conda/etc/profile.d/conda.sh
conda activate base
conda activate project2
pip install openpyxl
sinteractive --mem 500g --time 36:00:00 --gres=lscratch:500
python
import matplotlib.pyplot as plt
import pandas as pd
import NaiveDE
import SpatialDE
counts = pd.read_csv('comparisonG.csv', index_col=0)
sample_info = pd.read_csv('comparisonG.meta.csv', index_col=0)
sample_info
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_g_round1.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_g_round2.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_g_round3.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_g_round4.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_g_round5.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_g_round6.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_g_round7.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_g_round8.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_g_round9.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_g_round10.xlsx')
exit()
conda deactivate
conda deactivate
# crunch
library("openxlsx")
library("xlsx")
rnd1 <- read.xlsx("comparison_g_round1.xlsx","Sheet1",stringsAsFactors = T)
rnd2 <- read.xlsx("comparison_g_round2.xlsx","Sheet1",stringsAsFactors = T)
rnd3 <- read.xlsx("comparison_g_round3.xlsx","Sheet1",stringsAsFactors = T)
rnd4 <- read.xlsx("comparison_g_round4.xlsx","Sheet1",stringsAsFactors = T)
rnd5 <- read.xlsx("comparison_g_round5.xlsx","Sheet1",stringsAsFactors = T)
rnd6 <- read.xlsx("comparison_g_round6.xlsx","Sheet1",stringsAsFactors = T)
rnd7 <- read.xlsx("comparison_g_round7.xlsx","Sheet1",stringsAsFactors = T)
rnd8 <- read.xlsx("comparison_g_round8.xlsx","Sheet1",stringsAsFactors = T)
rnd9 <- read.xlsx("comparison_g_round9.xlsx","Sheet1",stringsAsFactors = T)
rnd10 <- read.xlsx("comparison_g_round10.xlsx","Sheet1",stringsAsFactors = T)
dimnames(rnd1)[[1]] <- rnd1$g
dimnames(rnd2)[[1]] <- rnd2$g
dimnames(rnd3)[[1]] <- rnd3$g
dimnames(rnd4)[[1]] <- rnd4$g
dimnames(rnd5)[[1]] <- rnd5$g
dimnames(rnd6)[[1]] <- rnd6$g
dimnames(rnd7)[[1]] <- rnd7$g
dimnames(rnd8)[[1]] <- rnd8$g
dimnames(rnd9)[[1]] <- rnd9$g
dimnames(rnd10)[[1]] <- rnd10$g
rnd2 <- rnd2[dimnames(rnd2)[[1]],]
rnd3 <- rnd2[dimnames(rnd3)[[1]],]
rnd4 <- rnd2[dimnames(rnd4)[[1]],]
rnd5 <- rnd2[dimnames(rnd5)[[1]],]
rnd6 <- rnd2[dimnames(rnd6)[[1]],]
rnd7 <- rnd2[dimnames(rnd7)[[1]],]
rnd8 <- rnd2[dimnames(rnd8)[[1]],]
rnd9 <- rnd2[dimnames(rnd9)[[1]],]
rnd10 <- rnd2[dimnames(rnd10)[[1]],]
rnd.cx <- cbind(rnd1$LLR,rnd2$LLR,rnd3$LLR,rnd4$LLR,rnd5$LLR,rnd6$LLR,rnd7$LLR,rnd8$LLR,rnd9$LLR,rnd10$LLR)
dimnames(rnd.cx)[[1]] <- dimnames(rnd1)[[1]]
rnd.cx.mean <- apply(rnd.cx,1,mean)
rnd.cx.sd <- apply(rnd.cx,1,sd)
rnd.cx.se <- https://protect-au.mimecast.com/s/T-okCYW8JlTp17XQcDWUHe?domain=rnd.cx.sd
rnd.cx.z <- NULL
rnd.cx.p <- NULL
for (i in 1:length(rnd.cx.mean)) {
rnd.temp1 <- (rnd.cx.mean[i]-mean(rnd.cx.mean[-c(i)]))/sd(rnd.cx.mean[-c(i)])
rnd.cx.z <- c(rnd.cx.z,rnd.temp1)
rnd.temp1 <- 1-pnorm(rnd.temp1)
rnd.cx.p <- c(rnd.cx.p,rnd.temp1)
}
rnd.cx.final <- cbind(rnd.cx.mean,rnd.cx.sd,rnd.cx.se,rnd.cx.z,rnd.cx.p)
write.table(rnd.cx.final,"comparison_g_bootstrap_stats.txt",sep="\t")
############################################################################
# Comparison H
# Group 8 vs Groups 1, 2, 3, 4, 5, 6, 7
# Cluster 5 vs clusters 0, 1, 4, 14, 2, 3, 6, 7, 11, 13, 15, 9, 16
############################################################################
temp <- as.character(unlist(pbmc.Control.H250.185$seurat_clusters))
temp <- ifelse(temp=="0","B",temp)
temp <- ifelse(temp=="1","B",temp)
temp <- ifelse(temp=="2","B",temp)
temp <- ifelse(temp=="3","B",temp)
temp <- ifelse(temp=="4","B",temp)
temp <- ifelse(temp=="5","A",temp)
temp <- ifelse(temp=="6","B",temp)
temp <- ifelse(temp=="7","B",temp)
temp <- ifelse(temp=="8","X",temp)
temp <- ifelse(temp=="9","B",temp)
temp <- ifelse(temp=="10","X",temp)
temp <- ifelse(temp=="11","B",temp)
temp <- ifelse(temp=="12","X",temp)
temp <- ifelse(temp=="13","B",temp)
temp <- ifelse(temp=="14","B",temp)
temp <- ifelse(temp=="15","B",temp)
temp <- ifelse(temp=="16","B",temp)
temp <- ifelse(temp=="17","X",temp)
temp <- ifelse(temp=="18","X",temp)
temp <- ifelse(temp=="19","X",temp)
temp <- ifelse(temp=="20","X",temp)
temp <- ifelse(temp=="21","X",temp)
temp <- ifelse(temp=="22","X",temp)
temp <- ifelse(temp=="23","X",temp)
temp <- ifelse(temp=="24","X",temp)
temp <- ifelse(temp=="25","X",temp)
pbmc.Control.H250.185@meta.data$comparisonA <- as.factor(temp)
temp <- pbmc.Control.H250.185[,pbmc.Control.H250.185@meta.data$comparisonA!="X"]
temp.data <- GetAssayData(temp, slot = "counts")
temp.data <- as.matrix(temp.data)
temp.data <- t(temp.data)
temp <- dimnames(temp.data)[[1]]
temp <- str_replace_all(temp,"X","x")
dimnames(temp.data)[[1]] <- temp
write.csv(temp.data,"comparisonH.csv")
temp <- strsplit(temp,"x")
temp <- as.data.frame(temp)
temp <- t(temp)
dimnames(temp)[[1]] <- dimnames(temp.data)[[1]]
temp <- cbind(temp,apply(temp.data,1,sum))
dimnames(temp)[[2]] <- c("x","y","total_counts")
write.csv(temp,"comparisonH.meta.csv")
# SpatialDE
# https://protect-au.mimecast.com/s/-zOOCWLVEjtX9E82fwl5tK?domain=github.com
cd /data/johnsonko
source /data/johnsonko/conda/etc/profile.d/conda.sh
conda activate base
conda activate project2
pip install openpyxl
sinteractive --mem 500g --time 36:00:00 --gres=lscratch:500
python
import matplotlib.pyplot as plt
import pandas as pd
import NaiveDE
import SpatialDE
counts = pd.read_csv('comparisonH.csv', index_col=0)
sample_info = pd.read_csv('comparisonH.meta.csv', index_col=0)
sample_info
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_h_round1.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_h_round2.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_h_round3.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_h_round4.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_h_round5.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_h_round6.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_h_round7.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_h_round8.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_h_round9.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_h_round10.xlsx')
exit()
conda deactivate
conda deactivate
# crunch
library("openxlsx")
library("xlsx")
rnd1 <- read.xlsx("comparison_h_round1.xlsx","Sheet1",stringsAsFactors = T)
rnd2 <- read.xlsx("comparison_h_round2.xlsx","Sheet1",stringsAsFactors = T)
rnd3 <- read.xlsx("comparison_h_round3.xlsx","Sheet1",stringsAsFactors = T)
rnd4 <- read.xlsx("comparison_h_round4.xlsx","Sheet1",stringsAsFactors = T)
rnd5 <- read.xlsx("comparison_h_round5.xlsx","Sheet1",stringsAsFactors = T)
rnd6 <- read.xlsx("comparison_h_round6.xlsx","Sheet1",stringsAsFactors = T)
rnd7 <- read.xlsx("comparison_h_round7.xlsx","Sheet1",stringsAsFactors = T)
rnd8 <- read.xlsx("comparison_h_round8.xlsx","Sheet1",stringsAsFactors = T)
rnd9 <- read.xlsx("comparison_h_round9.xlsx","Sheet1",stringsAsFactors = T)
rnd10 <- read.xlsx("comparison_h_round10.xlsx","Sheet1",stringsAsFactors = T)
dimnames(rnd1)[[1]] <- rnd1$g
dimnames(rnd2)[[1]] <- rnd2$g
dimnames(rnd3)[[1]] <- rnd3$g
dimnames(rnd4)[[1]] <- rnd4$g
dimnames(rnd5)[[1]] <- rnd5$g
dimnames(rnd6)[[1]] <- rnd6$g
dimnames(rnd7)[[1]] <- rnd7$g
dimnames(rnd8)[[1]] <- rnd8$g
dimnames(rnd9)[[1]] <- rnd9$g
dimnames(rnd10)[[1]] <- rnd10$g
rnd2 <- rnd2[dimnames(rnd2)[[1]],]
rnd3 <- rnd2[dimnames(rnd3)[[1]],]
rnd4 <- rnd2[dimnames(rnd4)[[1]],]
rnd5 <- rnd2[dimnames(rnd5)[[1]],]
rnd6 <- rnd2[dimnames(rnd6)[[1]],]
rnd7 <- rnd2[dimnames(rnd7)[[1]],]
rnd8 <- rnd2[dimnames(rnd8)[[1]],]
rnd9 <- rnd2[dimnames(rnd9)[[1]],]
rnd10 <- rnd2[dimnames(rnd10)[[1]],]
rnd.cx <- cbind(rnd1$LLR,rnd2$LLR,rnd3$LLR,rnd4$LLR,rnd5$LLR,rnd6$LLR,rnd7$LLR,rnd8$LLR,rnd9$LLR,rnd10$LLR)
dimnames(rnd.cx)[[1]] <- dimnames(rnd1)[[1]]
rnd.cx.mean <- apply(rnd.cx,1,mean)
rnd.cx.sd <- apply(rnd.cx,1,sd)
rnd.cx.se <- https://protect-au.mimecast.com/s/T-okCYW8JlTp17XQcDWUHe?domain=rnd.cx.sd
rnd.cx.z <- NULL
rnd.cx.p <- NULL
for (i in 1:length(rnd.cx.mean)) {
rnd.temp1 <- (rnd.cx.mean[i]-mean(rnd.cx.mean[-c(i)]))/sd(rnd.cx.mean[-c(i)])
rnd.cx.z <- c(rnd.cx.z,rnd.temp1)
rnd.temp1 <- 1-pnorm(rnd.temp1)
rnd.cx.p <- c(rnd.cx.p,rnd.temp1)
}
rnd.cx.final <- cbind(rnd.cx.mean,rnd.cx.sd,rnd.cx.se,rnd.cx.z,rnd.cx.p)
write.table(rnd.cx.final,"comparison_h_bootstrap_stats.txt",sep="\t")
############################################################################
# Comparison I
# Group 8 vs Groups 1, 2, 3, 4, 5, 6, 7
# Cluster 8 vs clusters 0, 1, 4, 14, 2, 3, 6, 7, 11, 13, 15, 9, 16
############################################################################
temp <- as.character(unlist(pbmc.Control.H250.185$seurat_clusters))
temp <- ifelse(temp=="0","B",temp)
temp <- ifelse(temp=="1","B",temp)
temp <- ifelse(temp=="2","B",temp)
temp <- ifelse(temp=="3","B",temp)
temp <- ifelse(temp=="4","B",temp)
temp <- ifelse(temp=="5","X",temp)
temp <- ifelse(temp=="6","B",temp)
temp <- ifelse(temp=="7","B",temp)
temp <- ifelse(temp=="8","A",temp)
temp <- ifelse(temp=="9","B",temp)
temp <- ifelse(temp=="10","X",temp)
temp <- ifelse(temp=="11","B",temp)
temp <- ifelse(temp=="12","X",temp)
temp <- ifelse(temp=="13","B",temp)
temp <- ifelse(temp=="14","B",temp)
temp <- ifelse(temp=="15","B",temp)
temp <- ifelse(temp=="16","B",temp)
temp <- ifelse(temp=="17","X",temp)
temp <- ifelse(temp=="18","X",temp)
temp <- ifelse(temp=="19","X",temp)
temp <- ifelse(temp=="20","X",temp)
temp <- ifelse(temp=="21","X",temp)
temp <- ifelse(temp=="22","X",temp)
temp <- ifelse(temp=="23","X",temp)
temp <- ifelse(temp=="24","X",temp)
temp <- ifelse(temp=="25","X",temp)
pbmc.Control.H250.185@meta.data$comparisonA <- as.factor(temp)
temp <- pbmc.Control.H250.185[,pbmc.Control.H250.185@meta.data$comparisonA!="X"]
temp.data <- GetAssayData(temp, slot = "counts")
temp.data <- as.matrix(temp.data)
temp.data <- t(temp.data)
temp <- dimnames(temp.data)[[1]]
temp <- str_replace_all(temp,"X","x")
dimnames(temp.data)[[1]] <- temp
write.csv(temp.data,"comparisonI.csv")
temp <- strsplit(temp,"x")
temp <- as.data.frame(temp)
temp <- t(temp)
dimnames(temp)[[1]] <- dimnames(temp.data)[[1]]
temp <- cbind(temp,apply(temp.data,1,sum))
dimnames(temp)[[2]] <- c("x","y","total_counts")
write.csv(temp,"comparisonI.meta.csv")
# SpatialDE
# https://protect-au.mimecast.com/s/-zOOCWLVEjtX9E82fwl5tK?domain=github.com
cd /data/johnsonko
source /data/johnsonko/conda/etc/profile.d/conda.sh
conda activate base
conda activate project2
pip install openpyxl
sinteractive --mem 500g --time 36:00:00 --gres=lscratch:500
python
import matplotlib.pyplot as plt
import pandas as pd
import NaiveDE
import SpatialDE
counts = pd.read_csv('comparisonI.csv', index_col=0)
sample_info = pd.read_csv('comparisonI.meta.csv', index_col=0)
sample_info
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_i_round1.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_i_round2.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_i_round3.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_i_round4.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_i_round5.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_i_round6.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_i_round7.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_i_round8.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_i_round9.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_i_round10.xlsx')
exit()
conda deactivate
conda deactivate
# crunch
library("openxlsx")
library("xlsx")
rnd1 <- read.xlsx("comparison_i_round1.xlsx","Sheet1",stringsAsFactors = T)
rnd2 <- read.xlsx("comparison_i_round2.xlsx","Sheet1",stringsAsFactors = T)
rnd3 <- read.xlsx("comparison_i_round3.xlsx","Sheet1",stringsAsFactors = T)
rnd4 <- read.xlsx("comparison_i_round4.xlsx","Sheet1",stringsAsFactors = T)
rnd5 <- read.xlsx("comparison_i_round5.xlsx","Sheet1",stringsAsFactors = T)
rnd6 <- read.xlsx("comparison_i_round6.xlsx","Sheet1",stringsAsFactors = T)
rnd7 <- read.xlsx("comparison_i_round7.xlsx","Sheet1",stringsAsFactors = T)
rnd8 <- read.xlsx("comparison_i_round8.xlsx","Sheet1",stringsAsFactors = T)
rnd9 <- read.xlsx("comparison_i_round9.xlsx","Sheet1",stringsAsFactors = T)
rnd10 <- read.xlsx("comparison_i_round10.xlsx","Sheet1",stringsAsFactors = T)
dimnames(rnd1)[[1]] <- rnd1$g
dimnames(rnd2)[[1]] <- rnd2$g
dimnames(rnd3)[[1]] <- rnd3$g
dimnames(rnd4)[[1]] <- rnd4$g
dimnames(rnd5)[[1]] <- rnd5$g
dimnames(rnd6)[[1]] <- rnd6$g
dimnames(rnd7)[[1]] <- rnd7$g
dimnames(rnd8)[[1]] <- rnd8$g
dimnames(rnd9)[[1]] <- rnd9$g
dimnames(rnd10)[[1]] <- rnd10$g
rnd2 <- rnd2[dimnames(rnd2)[[1]],]
rnd3 <- rnd2[dimnames(rnd3)[[1]],]
rnd4 <- rnd2[dimnames(rnd4)[[1]],]
rnd5 <- rnd2[dimnames(rnd5)[[1]],]
rnd6 <- rnd2[dimnames(rnd6)[[1]],]
rnd7 <- rnd2[dimnames(rnd7)[[1]],]
rnd8 <- rnd2[dimnames(rnd8)[[1]],]
rnd9 <- rnd2[dimnames(rnd9)[[1]],]
rnd10 <- rnd2[dimnames(rnd10)[[1]],]
rnd.cx <- cbind(rnd1$LLR,rnd2$LLR,rnd3$LLR,rnd4$LLR,rnd5$LLR,rnd6$LLR,rnd7$LLR,rnd8$LLR,rnd9$LLR,rnd10$LLR)
dimnames(rnd.cx)[[1]] <- dimnames(rnd1)[[1]]
rnd.cx.mean <- apply(rnd.cx,1,mean)
rnd.cx.sd <- apply(rnd.cx,1,sd)
rnd.cx.se <- https://protect-au.mimecast.com/s/T-okCYW8JlTp17XQcDWUHe?domain=rnd.cx.sd
rnd.cx.z <- NULL
rnd.cx.p <- NULL
for (i in 1:length(rnd.cx.mean)) {
rnd.temp1 <- (rnd.cx.mean[i]-mean(rnd.cx.mean[-c(i)]))/sd(rnd.cx.mean[-c(i)])
rnd.cx.z <- c(rnd.cx.z,rnd.temp1)
rnd.temp1 <- 1-pnorm(rnd.temp1)
rnd.cx.p <- c(rnd.cx.p,rnd.temp1)
}
rnd.cx.final <- cbind(rnd.cx.mean,rnd.cx.sd,rnd.cx.se,rnd.cx.z,rnd.cx.p)
write.table(rnd.cx.final,"comparison_i_bootstrap_stats.txt",sep="\t")
############################################################################
# Comparison J
# Group 8 vs Groups 1, 2, 3, 4, 5, 6, 7
# Cluster 12 vs clusters 0, 1, 4, 14, 2, 3, 6, 7, 11, 13, 15, 9, 16
############################################################################
temp <- as.character(unlist(pbmc.Control.H250.185$seurat_clusters))
temp <- ifelse(temp=="0","B",temp)
temp <- ifelse(temp=="1","B",temp)
temp <- ifelse(temp=="2","B",temp)
temp <- ifelse(temp=="3","B",temp)
temp <- ifelse(temp=="4","B",temp)
temp <- ifelse(temp=="5","X",temp)
temp <- ifelse(temp=="6","B",temp)
temp <- ifelse(temp=="7","B",temp)
temp <- ifelse(temp=="8","X",temp)
temp <- ifelse(temp=="9","B",temp)
temp <- ifelse(temp=="10","X",temp)
temp <- ifelse(temp=="11","B",temp)
temp <- ifelse(temp=="12","A",temp)
temp <- ifelse(temp=="13","B",temp)
temp <- ifelse(temp=="14","B",temp)
temp <- ifelse(temp=="15","B",temp)
temp <- ifelse(temp=="16","B",temp)
temp <- ifelse(temp=="17","X",temp)
temp <- ifelse(temp=="18","X",temp)
temp <- ifelse(temp=="19","X",temp)
temp <- ifelse(temp=="20","X",temp)
temp <- ifelse(temp=="21","X",temp)
temp <- ifelse(temp=="22","X",temp)
temp <- ifelse(temp=="23","X",temp)
temp <- ifelse(temp=="24","X",temp)
temp <- ifelse(temp=="25","X",temp)
pbmc.Control.H250.185@meta.data$comparisonA <- as.factor(temp)
temp <- pbmc.Control.H250.185[,pbmc.Control.H250.185@meta.data$comparisonA!="X"]
temp.data <- GetAssayData(temp, slot = "counts")
temp.data <- as.matrix(temp.data)
temp.data <- t(temp.data)
temp <- dimnames(temp.data)[[1]]
temp <- str_replace_all(temp,"X","x")
dimnames(temp.data)[[1]] <- temp
write.csv(temp.data,"comparisonJ.csv")
temp <- strsplit(temp,"x")
temp <- as.data.frame(temp)
temp <- t(temp)
dimnames(temp)[[1]] <- dimnames(temp.data)[[1]]
temp <- cbind(temp,apply(temp.data,1,sum))
dimnames(temp)[[2]] <- c("x","y","total_counts")
write.csv(temp,"comparisonJ.meta.csv")
# SpatialDE
# https://protect-au.mimecast.com/s/-zOOCWLVEjtX9E82fwl5tK?domain=github.com
cd /data/johnsonko
source /data/johnsonko/conda/etc/profile.d/conda.sh
conda activate base
conda activate project2
pip install openpyxl
sinteractive --mem 500g --time 36:00:00 --gres=lscratch:500
python
import matplotlib.pyplot as plt
import pandas as pd
import NaiveDE
import SpatialDE
counts = pd.read_csv('comparisonJ.csv', index_col=0)
sample_info = pd.read_csv('comparisonJ.meta.csv', index_col=0)
sample_info
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_j_round1.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_j_round2.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_j_round3.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_j_round4.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_j_round5.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_j_round6.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_j_round7.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_j_round8.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_j_round9.xlsx')
sampled_sample_info = sample_info.sample(3000)
sampled_counts = counts.loc[sampled_sample_info.index]
norm_expr = NaiveDE.stabilize(sampled_counts.T).T
resid_expr = NaiveDE.regress_out(sampled_sample_info, norm_expr.T, 'np.log(total_counts)', rcond=1e-4).T
X = sampled_sample_info[['x', 'y']]
results = ""
results = SpatialDE.run(X, resid_expr)
results.to_excel('comparison_j_round10.xlsx')
exit()
conda deactivate
conda deactivate
# crunch
library("openxlsx")
library("xlsx")
rnd1 <- read.xlsx("comparison_j_round1.xlsx","Sheet1",stringsAsFactors = T)
rnd2 <- read.xlsx("comparison_j_round2.xlsx","Sheet1",stringsAsFactors = T)
rnd3 <- read.xlsx("comparison_j_round3.xlsx","Sheet1",stringsAsFactors = T)
rnd4 <- read.xlsx("comparison_j_round4.xlsx","Sheet1",stringsAsFactors = T)
rnd5 <- read.xlsx("comparison_j_round5.xlsx","Sheet1",stringsAsFactors = T)
rnd6 <- read.xlsx("comparison_j_round6.xlsx","Sheet1",stringsAsFactors = T)
rnd7 <- read.xlsx("comparison_j_round7.xlsx","Sheet1",stringsAsFactors = T)
rnd8 <- read.xlsx("comparison_j_round8.xlsx","Sheet1",stringsAsFactors = T)
rnd9 <- read.xlsx("comparison_j_round9.xlsx","Sheet1",stringsAsFactors = T)
rnd10 <- read.xlsx("comparison_j_round10.xlsx","Sheet1",stringsAsFactors = T)
dimnames(rnd1)[[1]] <- rnd1$g
dimnames(rnd2)[[1]] <- rnd2$g
dimnames(rnd3)[[1]] <- rnd3$g
dimnames(rnd4)[[1]] <- rnd4$g
dimnames(rnd5)[[1]] <- rnd5$g
dimnames(rnd6)[[1]] <- rnd6$g
dimnames(rnd7)[[1]] <- rnd7$g
dimnames(rnd8)[[1]] <- rnd8$g
dimnames(rnd9)[[1]] <- rnd9$g
dimnames(rnd10)[[1]] <- rnd10$g
rnd2 <- rnd2[dimnames(rnd2)[[1]],]
rnd3 <- rnd2[dimnames(rnd3)[[1]],]
rnd4 <- rnd2[dimnames(rnd4)[[1]],]
rnd5 <- rnd2[dimnames(rnd5)[[1]],]
rnd6 <- rnd2[dimnames(rnd6)[[1]],]
rnd7 <- rnd2[dimnames(rnd7)[[1]],]
rnd8 <- rnd2[dimnames(rnd8)[[1]],]
rnd9 <- rnd2[dimnames(rnd9)[[1]],]
rnd10 <- rnd2[dimnames(rnd10)[[1]],]
rnd.cx <- cbind(rnd1$LLR,rnd2$LLR,rnd3$LLR,rnd4$LLR,rnd5$LLR,rnd6$LLR,rnd7$LLR,rnd8$LLR,rnd9$LLR,rnd10$LLR)
dimnames(rnd.cx)[[1]] <- dimnames(rnd1)[[1]]
rnd.cx.mean <- apply(rnd.cx,1,mean)
rnd.cx.sd <- apply(rnd.cx,1,sd)
rnd.cx.se <- https://protect-au.mimecast.com/s/T-okCYW8JlTp17XQcDWUHe?domain=rnd.cx.sd
rnd.cx.z <- NULL
rnd.cx.p <- NULL
for (i in 1:length(rnd.cx.mean)) {
rnd.temp1 <- (rnd.cx.mean[i]-mean(rnd.cx.mean[-c(i)]))/sd(rnd.cx.mean[-c(i)])
rnd.cx.z <- c(rnd.cx.z,rnd.temp1)
rnd.temp1 <- 1-pnorm(rnd.temp1)
rnd.cx.p <- c(rnd.cx.p,rnd.temp1)
}
rnd.cx.final <- cbind(rnd.cx.mean,rnd.cx.sd,rnd.cx.se,rnd.cx.z,rnd.cx.p)
write.table(rnd.cx.final,"comparison_j_bootstrap_stats.txt",sep="\t")
##############################################
##############################################
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##############################################
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##############################################
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##############################################
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##############################################
# Cross Bulb Seurat Analysis - 20201006
##############################################
##############################################
##############################################
##############################################
##############################################
##############################################
##############################################
##############################################
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# https://protect-au.mimecast.com/s/bTpdCZY1Kmh846YEsoyrGL?domain=satijalab.org
# https://protect-au.mimecast.com/s/Zpd-C1WLoBTBz4X1iwmjof?domain=rdrr.io
pbmc.merged <- merge(x=pbmc.AZ.109.146,y=c(pbmc.AZ.84.145,
pbmc.AZ.90.242,
pbmc.AZ.99.140,
pbmc.AZ.251.146,
pbmc.Control.H190.260,
pbmc.Control.H250.185,
pbmc.Control.OFB57.194,
pbmc.Control.OFB6A.107,
pbmc.PD.52.247,
pbmc.PD.56.236,
pbmc.PD.58.229,
pbmc.PD.77.197,
pbmc.PD.79.201),
add.cell.ids=c("AZ.109.146",
"AZ.84.145",
"AZ.90.242",
"AZ.99.140",
"AZ.251.146",
"Control.H190.260",
"Control.H250.185",
"Control.OFB57.194",
"Control.OFB6A.107",
"PD.52.247",
"PD.56.236",
"PD.58.229",
"PD.77.197",
"PD.79.201"))
DefaultAssay(pbmc.merged) <- "SCT"
VariableFeatures(pbmc.merged) <- sort(unique(c(VariableFeatures(pbmc.AZ.109.146),
VariableFeatures(pbmc.AZ.84.145),
VariableFeatures(pbmc.AZ.90.242),
VariableFeatures(pbmc.AZ.99.140),
VariableFeatures(pbmc.AZ.251.146),
VariableFeatures(pbmc.Control.H190.260),
VariableFeatures(pbmc.Control.H250.185),
VariableFeatures(pbmc.Control.OFB57.194),
VariableFeatures(pbmc.Control.OFB6A.107),
VariableFeatures(pbmc.PD.52.247),
VariableFeatures(pbmc.PD.56.236),
VariableFeatures(pbmc.PD.58.229),
VariableFeatures(pbmc.PD.77.197),
VariableFeatures(pbmc.PD.79.201))))
pbmc.merged <- RunPCA(pbmc.merged,verbose=FALSE,approx=FALSE)
e <- ElbowPlot(pbmc.merged, ndims=dim(pbmc.merged$pca)[2],reduction="pca")
e <- e[[1]]
e <- e[e[,2]>1,]
e <- max(e[,1])
dims2use <- e
pbmc.merged <- FindNeighbors(pbmc.merged, dims = 1:dims2use)
pbmc.merged <- FindClusters(pbmc.merged, verbose = FALSE)
pbmc.merged <- RunUMAP(pbmc.merged, dims = 1:dims2use)
temp <- dimnames(pbmc.merged@meta.data)[[1]]
temp <- strsplit(temp,"_")
temp <- as.data.frame(temp)
temp <- t(temp)
temp <- temp[,1]
temp <- as.character(unlist(temp))
pbmc.merged@meta.data$Sample <- temp
###############
# UMAP Plots
###############
tiff("Combined_Sample_UMAP_Plot_Not_Split_Out_Label_Yes_Legend_Yes.tiff")
print(DimPlot(pbmc.merged,reduction="umap",label = TRUE))
dev.off()
tiff("Combined_Sample_UMAP_Plot_Not_Split_Out_Label_No_Legend_Yes.tiff")
print(DimPlot(pbmc.merged,reduction="umap",label = FALSE) + NoLegend())
dev.off()
tiff("Combined_Sample_UMAP_Plot_Not_Split_Out_Label_No_Legend_No.tiff")
print(DimPlot(pbmc.merged,reduction="umap",label = FALSE) + NoLegend())
dev.off()
tiff("Combined_Sample_UMAP_Plot_Split_Out_Label_No_Legend_No.tiff")
print(DimPlot(pbmc.merged,reduction="umap",label = FALSE,split.by="Sample",ncol=4) + NoLegend())
dev.off()
tiff("Combined_Sample_UMAP_Plot_Split_Out_Label_By_Sample.tiff")
print(DimPlot(pbmc.merged,reduction="umap",label = FALSE,split.by="Sample",group.by="Sample",ncol=4) + NoLegend())
dev.off()
tiff("Combined_Sample_UMAP_Plot_Split_Out_Label_By_Sample_With_Legend.tiff")
print(DimPlot(pbmc.merged,reduction="umap",label = FALSE,split.by="Sample",group.by="Sample",ncol=4))
dev.off()
tiff("Combined_Sample_UMAP_Plot_Split_Out_Label_By_Sample_With_Legend.tiff")
print(DimPlot(pbmc.merged,reduction="umap",label = FALSE,split.by="seurat_clusters",group.by="Sample",ncol=4))
dev.off()
print(FeaturePlot(pbmc.merged,features="TAU",split.by="Sample",ncol=4) + NoLegend())
###############
# Feature Plots
###############
tiff("Combined_Sample_Feature_Plot_ALPHA_SYNUCLEIN.tiff")
print(FeaturePlot(pbmc.merged, features = c("ALPHA-SYNUCLEIN")))
dev.off()
tiff("Combined_Sample_Feature_Plot_BETA_AMYLOID.tiff")
print(FeaturePlot(pbmc.merged, features = c("BETA-AMYLOID")))
dev.off()
tiff("Combined_Sample_Feature_Plot_CALBINDIN.tiff")
print(FeaturePlot(pbmc.merged, features = c("CALBINDIN")))
dev.off()
tiff("Combined_Sample_Feature_Plot_CD31.tiff")
print(FeaturePlot(pbmc.merged, features = c("CD31")))
dev.off()
tiff("Combined_Sample_Feature_Plot_CH_NEUN.tiff")
print(FeaturePlot(pbmc.merged, features = c("CH-NEUN")))
dev.off()
tiff("Combined_Sample_Feature_Plot_CNPASE.tiff")
print(FeaturePlot(pbmc.merged, features = c("CNPASE")))
dev.off()
tiff("Combined_Sample_Feature_Plot_COLLAGEN_IV.tiff")
print(FeaturePlot(pbmc.merged, features = c("COLLAGEN-IV")))
dev.off()
tiff("Combined_Sample_Feature_Plot_DAPI.tiff")
print(FeaturePlot(pbmc.merged, features = c("DAPI")))
dev.off()
tiff("Combined_Sample_Feature_Plot_GFAP.tiff")
print(FeaturePlot(pbmc.merged, features = c("GFAP")))
dev.off()
tiff("Combined_Sample_Feature_Plot_HISTONES.tiff")
print(FeaturePlot(pbmc.merged, features = c("HISTONES")))
dev.off()
tiff("Combined_Sample_Feature_Plot_HLADR.tiff")
print(FeaturePlot(pbmc.merged, features = c("HLADR")))
dev.off()
tiff("Combined_Sample_Feature_Plot_IBA1.tiff")
print(FeaturePlot(pbmc.merged, features = c("IBA1")))
dev.off()
tiff("Combined_Sample_Feature_Plot_MAP2.tiff")
print(FeaturePlot(pbmc.merged, features = c("MAP2")))
dev.off()
tiff("Combined_Sample_Feature_Plot_MBP.tiff")
print(FeaturePlot(pbmc.merged, features = c("MBP")))
dev.off()
tiff("Combined_Sample_Feature_Plot_NEURO_HEAVY.tiff")
print(FeaturePlot(pbmc.merged, features = c("NEURO-HEAVY")))
dev.off()
tiff("Combined_Sample_Feature_Plot_NEURO_LIGHT.tiff")
print(FeaturePlot(pbmc.merged, features = c("NEURO-LIGHT")))
dev.off()
tiff("Combined_Sample_Feature_Plot_OMP.tiff")
print(FeaturePlot(pbmc.merged, features = c("OMP")))
dev.off()
tiff("Combined_Sample_Feature_Plot_PGP9_5.tiff")
print(FeaturePlot(pbmc.merged, features = c("PGP9-5")))
dev.off()
tiff("Combined_Sample_Feature_Plot_RB_CALRETININ.tiff")
print(FeaturePlot(pbmc.merged, features = c("RB-CALRETININ")))
dev.off()
tiff("Combined_Sample_Feature_Plot_S100.tiff")
print(FeaturePlot(pbmc.merged, features = c("S100")))
dev.off()
tiff("Combined_Sample_Feature_Plot_SYNAPTOPHYSIN.tiff")
print(FeaturePlot(pbmc.merged, features = c("SYNAPTOPHYSIN")))
dev.off()
tiff("Combined_Sample_Feature_Plot_TAU.tiff")
print(FeaturePlot(pbmc.merged, features = c("TAU")))
dev.off()
tiff("Combined_Sample_Feature_Plot_TOMATO_LECTIN.tiff")
print(FeaturePlot(pbmc.merged, features = c("TOMATO-LECTIN")))
dev.off()
tiff("Combined_Sample_Feature_Plot_TYR_HYDROXYLASE.tiff")
print(FeaturePlot(pbmc.merged, features = c("TYR-HYDROXYLASE")))
dev.off()
tiff("Combined_Sample_Feature_Plot_UEA_LECTIN.tiff")
print(FeaturePlot(pbmc.merged, features = c("UEA-LECTIN")))
dev.off()
#############
# Ridge Plots
#############
tiff("Combined_Sample_Ridge_Plot_ALPHA_SYNUCLEIN.tiff")
print(RidgePlot(pbmc.merged, features = c("ALPHA-SYNUCLEIN"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_BETA_AMYLOID.tiff")
print(RidgePlot(pbmc.merged, features = c("BETA-AMYLOID"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_CALBINDIN.tiff")
print(RidgePlot(pbmc.merged, features = c("CALBINDIN"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_CD31.tiff")
print(RidgePlot(pbmc.merged, features = c("CD31"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_CH_NEUN.tiff")
print(RidgePlot(pbmc.merged, features = c("CH-NEUN"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_CNPASE.tiff")
print(RidgePlot(pbmc.merged, features = c("CNPASE"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_COLLAGEN_IV.tiff")
print(RidgePlot(pbmc.merged, features = c("COLLAGEN-IV"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_DAPI.tiff")
print(RidgePlot(pbmc.merged, features = c("DAPI"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_GFAP.tiff")
print(RidgePlot(pbmc.merged, features = c("GFAP"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_HISTONES.tiff")
print(RidgePlot(pbmc.merged, features = c("HISTONES"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_HLADR.tiff")
print(RidgePlot(pbmc.merged, features = c("HLADR"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_IBA1.tiff")
print(RidgePlot(pbmc.merged, features = c("IBA1"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_MAP2.tiff")
print(RidgePlot(pbmc.merged, features = c("MAP2"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_MBP.tiff")
print(RidgePlot(pbmc.merged, features = c("MBP"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_NEURO_HEAVY.tiff")
print(RidgePlot(pbmc.merged, features = c("NEURO-HEAVY"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_NEURO_LIGHT.tiff")
print(RidgePlot(pbmc.merged, features = c("NEURO-LIGHT"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_OMP.tiff")
print(RidgePlot(pbmc.merged, features = c("OMP"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_PGP9_5.tiff")
print(RidgePlot(pbmc.merged, features = c("PGP9-5"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_RB_CALRETININ.tiff")
print(RidgePlot(pbmc.merged, features = c("RB-CALRETININ"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_S100.tiff")
print(RidgePlot(pbmc.merged, features = c("S100"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_SYNAPTOPHYSIN.tiff")
print(RidgePlot(pbmc.merged, features = c("SYNAPTOPHYSIN"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_TAU.tiff")
print(RidgePlot(pbmc.merged, features = c("TAU"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_TOMATO_LECTIN.tiff")
print(RidgePlot(pbmc.merged, features = c("TOMATO-LECTIN"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_TYR_HYDROXYLASE.tiff")
print(RidgePlot(pbmc.merged, features = c("TYR-HYDROXYLASE"),group.by="Sample") + NoLegend())
dev.off()
tiff("Combined_Sample_Ridge_Plot_UEA_LECTIN.tiff")
print(RidgePlot(pbmc.merged, features = c("UEA-LECTIN"),group.by="Sample") + NoLegend())
dev.off()
#############
# Violin Plots
#############
tiff("Combined_Sample_Violin_Plot_ALPHA_SYNUCLEIN.tiff")
print(VlnPlot(pbmc.merged, features = c("ALPHA-SYNUCLEIN"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_BETA_AMYLOID.tiff")
print(VlnPlot(pbmc.merged, features = c("BETA-AMYLOID"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_CALBINDIN.tiff")
print(VlnPlot(pbmc.merged, features = c("CALBINDIN"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_CD31.tiff")
print(VlnPlot(pbmc.merged, features = c("CD31"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_CH_NEUN.tiff")
print(VlnPlot(pbmc.merged, features = c("CH-NEUN"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_CNPASE.tiff")
print(VlnPlot(pbmc.merged, features = c("CNPASE"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_COLLAGEN_IV.tiff")
print(VlnPlot(pbmc.merged, features = c("COLLAGEN-IV"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_DAPI.tiff")
print(VlnPlot(pbmc.merged, features = c("DAPI"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_GFAP.tiff")
print(VlnPlot(pbmc.merged, features = c("GFAP"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_HISTONES.tiff")
print(VlnPlot(pbmc.merged, features = c("HISTONES"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_HLADR.tiff")
print(VlnPlot(pbmc.merged, features = c("HLADR"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_IBA1.tiff")
print(VlnPlot(pbmc.merged, features = c("IBA1"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_MAP2.tiff")
print(VlnPlot(pbmc.merged, features = c("MAP2"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_MBP.tiff")
print(VlnPlot(pbmc.merged, features = c("MBP"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_NEURO_HEAVY.tiff")
print(VlnPlot(pbmc.merged, features = c("NEURO-HEAVY"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_NEURO_LIGHT.tiff")
print(VlnPlot(pbmc.merged, features = c("NEURO-LIGHT"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_OMP.tiff")
print(VlnPlot(pbmc.merged, features = c("OMP"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_PGP9_5.tiff")
print(VlnPlot(pbmc.merged, features = c("PGP9-5"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_RB_CALRETININ.tiff")
print(VlnPlot(pbmc.merged, features = c("RB-CALRETININ"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_S100.tiff")
print(VlnPlot(pbmc.merged, features = c("S100"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_SYNAPTOPHYSIN.tiff")
print(VlnPlot(pbmc.merged, features = c("SYNAPTOPHYSIN"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_TAU.tiff")
print(VlnPlot(pbmc.merged, features = c("TAU"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_TOMATO_LECTIN.tiff")
print(VlnPlot(pbmc.merged, features = c("TOMATO-LECTIN"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_TYR_HYDROXYLASE.tiff")
print(VlnPlot(pbmc.merged, features = c("TYR-HYDROXYLASE"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
tiff("Combined_Sample_Violin_Plot_UEA_LECTIN.tiff")
print(VlnPlot(pbmc.merged, features = c("UEA-LECTIN"),group.by="Sample",pt.size=0) + NoLegend())
dev.off()
##########
# Dot Plot
##########
tiff("Combined_Sample_Dot_Plot_By_Sample_By_Label.tiff",width = 12, height = 6, units = "in",res=300)
print(DotPlot(pbmc.merged, features = rownames(pbmc.merged),group.by="Sample") + RotatedAxis())
dev.off()
tiff("Combined_Sample_Dot_Plot_By_Sample_By_Label.tiff",width = 12, height = 12, units = "in",res=300)
print(DotPlot(pbmc.merged, features = rownames(pbmc.merged),group.by="seurat_clusters") + RotatedAxis())
dev.off()
##########
# Class Definition
##########
temp <- dimnames(pbmc.merged@meta.data)[[1]]
temp <- strsplit(temp,"_")
temp <- as.data.frame(temp)
temptemp <- temp[1,]
temp <- as.character(unlist(temptemp))
temptemp <- strsplit(temp,".",fixed=TRUE)
temp <- as.data.frame(temptemp)
temptemp <- temp[1,]
temptemp <- as.character(unlist(temptemp))
pbmc.merged@meta.data$Class <- temptemp
##########
# Dot Plot
##########
tiff("Combined_Sample_Dot_Plot_By_Class_By_Label.tiff",width = 12, height = 6, units = "in",res=300)
print(DotPlot(pbmc.merged, features = rownames(pbmc.merged),group.by="Class") + RotatedAxis())
dev.off()
##########
# UMAP Plot
##########
tiff("UMAP_By_Class.tiff")
print(DimPlot(pbmc.merged,reduction="umap",split.by="Class",group.by="Class"))
dev.off()
###############
# Feature Plots
###############
tiff("FeaturePlot_By_Class_For_ALPHA_SYNUCLEIN.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("ALPHA-SYNUCLEIN"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_BETA_AMYLOID.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("BETA-AMYLOID"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_CALBINDIN.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("CALBINDIN"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_CD31.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("CD31"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_CH_NEUN.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("CH-NEUN"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_CNPASE.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("CNPASE"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_COLLAGEN_IV.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("COLLAGEN-IV"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_DAPI.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("DAPI"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_GFAP.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("GFAP"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_HISTONES.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("HISTONES"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_HLADR.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("HLADR"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_IBA1.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("IBA1"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_MAP2.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("MAP2"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_MBP.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("MBP"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_NEURO_HEAVY.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("NEURO-HEAVY"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_NEURO_LIGHT.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("NEURO-LIGHT"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_OMP.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("OMP"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_PGP9_5.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("PGP9-5"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_RB_CALRETININ.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("RB-CALRETININ"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_S100.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("S100"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_SYNAPTOPHYSIN.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("SYNAPTOPHYSIN"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_TAU.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("TAU"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_TOMATO_LECTIN.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("TOMATO-LECTIN"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_TYR_HYDROXYLASE.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("TYR-HYDROXYLASE"),split.by="Class"))
dev.off()
tiff("FeaturePlot_By_Class_For_UEA_LECTIN.tiff",width = 12, height = 6, units = "in",res=300)
print(FeaturePlot(pbmc.merged, features = c("UEA-LECTIN"),split.by="Class"))
dev.off()
#############
# Ridge Plots
#############
tiff("Ridge_Plot_By_Class_For_ALPHA_SYNUCLEIN.tiff")
print(RidgePlot(pbmc.merged, features = c("ALPHA-SYNUCLEIN"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_BETA_AMYLOID.tiff")
print(RidgePlot(pbmc.merged, features = c("BETA-AMYLOID"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_CALBINDIN.tiff")
print(RidgePlot(pbmc.merged, features = c("CALBINDIN"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_CD31.tiff")
print(RidgePlot(pbmc.merged, features = c("CD31"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_CH_NEUN.tiff")
print(RidgePlot(pbmc.merged, features = c("CH-NEUN"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_CNPASE.tiff")
print(RidgePlot(pbmc.merged, features = c("CNPASE"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_COLLAGEN_IV.tiff")
print(RidgePlot(pbmc.merged, features = c("COLLAGEN-IV"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_DAPI.tiff")
print(RidgePlot(pbmc.merged, features = c("DAPI"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_GFAP.tiff")
print(RidgePlot(pbmc.merged, features = c("GFAP"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_HISTONES.tiff")
print(RidgePlot(pbmc.merged, features = c("HISTONES"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_HLADR.tiff")
print(RidgePlot(pbmc.merged, features = c("HLADR"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_IBA1.tiff")
print(RidgePlot(pbmc.merged, features = c("IBA1"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_MAP2.tiff")
print(RidgePlot(pbmc.merged, features = c("MAP2"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_MBP.tiff")
print(RidgePlot(pbmc.merged, features = c("MBP"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_NEURO_HEAVY.tiff")
print(RidgePlot(pbmc.merged, features = c("NEURO-HEAVY"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_NEURO_LIGHT.tiff")
print(RidgePlot(pbmc.merged, features = c("NEURO-LIGHT"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_OMP.tiff")
print(RidgePlot(pbmc.merged, features = c("OMP"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_PGP9_5.tiff")
print(RidgePlot(pbmc.merged, features = c("PGP9-5"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_RB_CALRETININ.tiff")
print(RidgePlot(pbmc.merged, features = c("RB-CALRETININ"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_S100.tiff")
print(RidgePlot(pbmc.merged, features = c("S100"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_SYNAPTOPHYSIN.tiff")
print(RidgePlot(pbmc.merged, features = c("SYNAPTOPHYSIN"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_TAU.tiff")
print(RidgePlot(pbmc.merged, features = c("TAU"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_TOMATO_LECTIN.tiff")
print(RidgePlot(pbmc.merged, features = c("TOMATO-LECTIN"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_TYR_HYDROXYLASE.tiff")
print(RidgePlot(pbmc.merged, features = c("TYR-HYDROXYLASE"),group.by="Class") + NoLegend())
dev.off()
tiff("Ridge_Plot_By_Class_For_UEA_LECTIN.tiff")
print(RidgePlot(pbmc.merged, features = c("UEA-LECTIN"),group.by="Class") + NoLegend())
dev.off()
#############
# Violin Plots
#############
tiff("Violin_Plot_By_Class_For_ALPHA_SYNUCLEIN.tiff")
print(VlnPlot(pbmc.merged, features = c("ALPHA-SYNUCLEIN"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_BETA_AMYLOID.tiff")
print(VlnPlot(pbmc.merged, features = c("BETA-AMYLOID"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_CALBINDIN.tiff")
print(VlnPlot(pbmc.merged, features = c("CALBINDIN"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_CD31.tiff")
print(VlnPlot(pbmc.merged, features = c("CD31"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_CH_NEUN.tiff")
print(VlnPlot(pbmc.merged, features = c("CH-NEUN"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_CNPASE.tiff")
print(VlnPlot(pbmc.merged, features = c("CNPASE"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_COLLAGEN_IV.tiff")
print(VlnPlot(pbmc.merged, features = c("COLLAGEN-IV"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_DAPI.tiff")
print(VlnPlot(pbmc.merged, features = c("DAPI"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_GFAP.tiff")
print(VlnPlot(pbmc.merged, features = c("GFAP"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_HISTONES.tiff")
print(VlnPlot(pbmc.merged, features = c("HISTONES"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_HLADR.tiff")
print(VlnPlot(pbmc.merged, features = c("HLADR"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_IBA1.tiff")
print(VlnPlot(pbmc.merged, features = c("IBA1"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_MAP2.tiff")
print(VlnPlot(pbmc.merged, features = c("MAP2"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_MBP.tiff")
print(VlnPlot(pbmc.merged, features = c("MBP"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_NEURO_HEAVY.tiff")
print(VlnPlot(pbmc.merged, features = c("NEURO-HEAVY"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_NEURO_LIGHT.tiff")
print(VlnPlot(pbmc.merged, features = c("NEURO-LIGHT"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_OMP.tiff")
print(VlnPlot(pbmc.merged, features = c("OMP"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_PGP9_5.tiff")
print(VlnPlot(pbmc.merged, features = c("PGP9-5"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_RB_CALRETININ.tiff")
print(VlnPlot(pbmc.merged, features = c("RB-CALRETININ"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_S100.tiff")
print(VlnPlot(pbmc.merged, features = c("S100"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_SYNAPTOPHYSIN.tiff")
print(VlnPlot(pbmc.merged, features = c("SYNAPTOPHYSIN"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_TAU.tiff")
print(VlnPlot(pbmc.merged, features = c("TAU"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_TOMATO_LECTIN.tiff")
print(VlnPlot(pbmc.merged, features = c("TOMATO-LECTIN"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_TYR_HYDROXYLASE.tiff")
print(VlnPlot(pbmc.merged, features = c("TYR-HYDROXYLASE"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
tiff("Violin_Plot_By_Class_For_UEA_LECTIN.tiff")
print(VlnPlot(pbmc.merged, features = c("UEA-LECTIN"),group.by="Class",pt.size=0) + NoLegend())
dev.off()
##############################################
# Stacked BarChart
# https://protect-au.mimecast.com/s/gm_LC2xMp1UZlYQvuZPKBD?domain=r-graph-gallery.com
##############################################
library(ggplot2)
library(viridis)
running.stack <- NULL
temp0 <- sort(unique(pbmc.merged@meta.data$seurat_clusters))
for (i in 1:length(temp0)) {
print(i)
temp1 <- pbmc.merged@meta.data$Class[pbmc.merged@meta.data$seurat_clusters==temp0[i]]
temp2 <- table(temp1)
if(length(intersect(names(temp2),"Control"))==0) {
temp2 <- c(temp2,0)
names(temp2)[length(temp2)] <- "Control"
}
if(length(intersect(names(temp2),"AZ"))==0) {
temp2 <- c(temp2,0)
names(temp2)[length(temp2)] <- "AZ"
}
if(length(intersect(names(temp2),"PD"))==0) {
temp2 <- c(temp2,0)
names(temp2)[length(temp2)] <- "PD"
}
temp2 <- temp2[c("Control","AZ","PD")]
running.stack <- rbind(running.stack,temp2)
}
write.table(running.stack,"Number_Bins_Per_Cluster_By_Class.txt",sep="\t")
running.stack <- cbind(temp0,running.stack)
dimnames(running.stack)[[2]] <- c("Cluster","Control","AZ","PD")
running.stack <- as.data.frame(running.stack)
running.stack$Cluster <- as.factor(running.stack$Cluster)
temp3 <- running.stack[,c(1,2)]
temp3 <- data.frame(Cluster=as.factor(running.stack[,c(1)]),Sample=as.factor(c(rep("Control",dim(running.stack)[1]))),NumberCells=running.stack[,c(2)])
temp4 <- data.frame(Cluster=as.factor(running.stack[,c(1)]),Sample=as.factor(c(rep("AZ",dim(running.stack)[1]))),NumberCells=running.stack[,c(3)])
temp5 <- data.frame(Cluster=as.factor(running.stack[,c(1)]),Sample=as.factor(c(rep("PD",dim(running.stack)[1]))),NumberCells=running.stack[,c(4)])
temp6 <- rbind(temp4,temp3,temp5)
temp7 <- temp6[order(temp6$Cluster), ]
p1 <- ggplot(temp7, aes(fill=Sample, y=log2(NumberCells+2), x=Cluster)) +
geom_bar(position="stack", stat="identity") +
scale_fill_manual(values=hue_pal()(3)[c(1,2,3)]) + labs(y="Log2(Number Bins + 2)") + theme(panel.background = element_rect(fill = "white",colour = "white")) + geom_hline(yintercept=0, size=0.5)
p1
tiff("Cluster_vs_Number_Bin_Bar_Plot_Version_1.tiff",res=300,width=8, height=8,units="in")
print(p1 + RotatedAxis())
dev.off()
p2 <- ggplot(temp7, aes(fill=Sample, y=NumberCells, x=Cluster)) +
geom_bar(position="fill", stat="identity") +
scale_fill_manual(values=hue_pal()(3)[c(1,2,3)]) + labs(y="Percent Total Bins") + theme(panel.background = element_rect(fill = "white",colour = "white")) + theme(panel.border = element_rect(colour = "black", fill=NA, size=0.5)) + geom_hline(yintercept=0, size=0.5)+ geom_hline(yintercept=0, size=0.5)+ geom_vline(xintercept=0, size=0.5)
p2
tiff("Cluster_vs_Number_Bin_Bar_Plot_Version_2.tiff",res=300,width=8, height=8,units="in")
print(p2 + RotatedAxis())
dev.off()
write.table(temp7,"cluster_vs_sample_class_breakdown.txt",sep="\t")
#########################
# Sample breakdown vs cluster
#########################
running.stack <- NULL
temp0 <- sort(unique(pbmc.merged@meta.data$seurat_clusters))
for (i in 1:length(temp0)) {
print(i)
temp1 <- pbmc.merged@meta.data$Sample[pbmc.merged@meta.data$seurat_clusters==temp0[i]]
temp2 <- table(temp1)
if(length(intersect(names(temp2),"AZ.109.146"))==0) {
temp2 <- c(temp2,0)
names(temp2)[length(temp2)] <- "AZ.109.146"
}
if(length(intersect(names(temp2),"AZ.251.146"))==0) {
temp2 <- c(temp2,0)
names(temp2)[length(temp2)] <- "AZ.251.146"
}
if(length(intersect(names(temp2),"AZ.84.145"))==0) {
temp2 <- c(temp2,0)
names(temp2)[length(temp2)] <- "AZ.84.145"
}
if(length(intersect(names(temp2),"AZ.90.242"))==0) {
temp2 <- c(temp2,0)
names(temp2)[length(temp2)] <- "AZ.90.242"
}
if(length(intersect(names(temp2),"AZ.99.140"))==0) {
temp2 <- c(temp2,0)
names(temp2)[length(temp2)] <- "AZ.99.140"
}
if(length(intersect(names(temp2),"Control.H190.260"))==0) {
temp2 <- c(temp2,0)
names(temp2)[length(temp2)] <- "Control.H190.260"
}
if(length(intersect(names(temp2),"Control.H250.185"))==0) {
temp2 <- c(temp2,0)
names(temp2)[length(temp2)] <- "Control.H250.185"
}
if(length(intersect(names(temp2),"Control.OFB57.194"))==0) {
temp2 <- c(temp2,0)
names(temp2)[length(temp2)] <- "Control.OFB57.194"
}
if(length(intersect(names(temp2),"Control.OFB6A.107"))==0) {
temp2 <- c(temp2,0)
names(temp2)[length(temp2)] <- "Control.OFB6A.107"
}
if(length(intersect(names(temp2),"PD.52.247"))==0) {
temp2 <- c(temp2,0)
names(temp2)[length(temp2)] <- "PD.52.247"
}
if(length(intersect(names(temp2),"PD.56.236"))==0) {
temp2 <- c(temp2,0)
names(temp2)[length(temp2)] <- "PD.56.236"
}
if(length(intersect(names(temp2),"PD.58.229"))==0) {
temp2 <- c(temp2,0)
names(temp2)[length(temp2)] <- "PD.58.229"
}
if(length(intersect(names(temp2),"PD.77.197"))==0) {
temp2 <- c(temp2,0)
names(temp2)[length(temp2)] <- "PD.77.197"
}
if(length(intersect(names(temp2),"PD.79.201"))==0) {
temp2 <- c(temp2,0)
names(temp2)[length(temp2)] <- "PD.79.201"
}
temp2 <- temp2[c("AZ.109.146","AZ.251.146","AZ.84.145","AZ.90.242","AZ.99.140","Control.H190.260","Control.H250.185","Control.OFB57.194","Control.OFB6A.107","PD.52.247","PD.56.236","PD.58.229","PD.77.197","PD.79.201")]
running.stack <- rbind(running.stack,as.numeric(temp2))
}
running.stack <- cbind(temp0,running.stack)
dimnames(running.stack)[[2]] <- c("Cluster","AZ.109.146","AZ_251_146","AZ.84.145","AZ.90.242","AZ.99.140","Control.H190.260","Control.H250.185","Control.OFB57.194","Control.OFB6A.107","PD.52.247","PD.56.236","PD.58.229","PD.77.197","PD.79.201")
running.stack <- as.data.frame(running.stack)
running.stack$Cluster <- as.factor(running.stack$Cluster)
temp3 <- c("AZ.1","AZ.2","AZ.3","AZ.4","AZ.5","Cntrl.1","Cntrl.2","Cntrl.3","Cntrl.4","PD.1","PD.2","PD.3","PD.4","PD.5")
temp4 <- NULL
for (i in 1:length(temp3)) {
temp5 <- data.frame(Cluster=as.factor(running.stack[,c(1)]),Sample=as.factor(c(rep(temp3[i],dim(running.stack)[1]))),NumberCells=running.stack[,c(i+1)])
temp4 <- rbind(temp4,temp5)
}
p3 <- ggplot(temp4, aes(fill=Cluster, y=log2(NumberCells+2), x=Sample)) +
geom_bar(position="stack", stat="identity") +
scale_fill_manual(values=hue_pal()(length(temp0))) + labs(y="Log2(Number Bins + 2)") + theme(panel.background = element_rect(fill = "white",colour = "white")) + geom_hline(yintercept=0, size=0.5)
p3
tiff("Bar_Plot_Version_3.tiff",res=300,width=8, height=8,units="in")
p3
dev.off()
p4 <- ggplot(temp4, aes(fill=Cluster, y=NumberCells, x=Sample)) +
geom_bar(position="fill", stat="identity") +
scale_fill_manual(values=hue_pal()(length(temp0))) + labs(y="Percent Total Bins") + theme(panel.background = element_rect(fill = "white",colour = "white")) + geom_hline(yintercept=0, size=0.5) + geom_hline(yintercept=1, size=0.5) + geom_hline(yintercept=0.5, size=0.25,linetype="dashed") + geom_hline(yintercept=0.25, size=0.25,linetype="dashed")+ geom_hline(yintercept=0.75, size=0.25,linetype="dashed")
p4
tiff("Bar_Plot_Version_4.tiff",res=300,width=8, height=8,units="in")
p4
dev.off()
write.table(temp4,"temp4.txt",sep="\t")
save.image("Current_20201006.RData")
load.image("Current_20201006.RData")
temp0 <- coded.ready.to.cluster
temp0 <- coded.ready.to.cluster[,coded.ready.to.cluster[22,]>0]
temp1 <- apply(temp0,2,paste,collapse="_",sep="_")
temp2 <- rev(sort(table(temp1)))
length(temp2)
length(temp2[temp2>1])
length(temp2[temp2>2])
length(temp2[temp2>3])
length(temp2[temp2>4])
length(temp2[temp2>5])
length(temp2[temp2>6])
length(temp2[temp2>7])
length(temp2[temp2>8])
length(temp2[temp2>9])
length(temp2[temp2>10])
length(temp2[temp2>99])
length(temp2[temp2>500])
temp3 <- temp2[temp2>10]
dimnames(coded.ready.to.cluster)[[1]]
working <- CreateSeuratObject(counts = coded.ready.to.cluster)
temp <- colnames(working)
temptemp <- strsplit(temp,"_Pos_")
temptemp <- as.data.frame(temptemp)
temptemp <- t(temptemp)
dimnames(temptemp)[[1]] <- temp
temp <- as.character(temptemp[,2])
temp <- strsplit(temp,"X")
temp <- as.data.frame(temp)
temp <- t(temp)
temptemp <- cbind(temptemp,temp)
dimnames(temptemp)[[2]] <- c("Sample","PosCx","Xpos","Ypos")
working@meta.data$Sample <- temptemp[,1]
working@meta.data$PosCx <- temptemp[,2]
working@meta.data$Xpos <- temptemp[,3]
working@meta.data$Ypos <- temptemp[,4]
temp <- temptemp[,1]
temptemp <- strsplit(temp,"_")
temptemp <- as.data.frame(temptemp)
temptemp <- t(temptemp)
working@meta.data$Type <- temptemp[,2]
working <- SCTransform(working)
working <- RunPCA(working,npcs=24)
DimPlot(working,reduction="pca",label=FALSE)
e <- ElbowPlot(working, ndims=24,reduction="pca")
e
dims2use <- 7
#tiff("Primary_Analysis_Elbow_Plot.tiff",res=300,width=8, height=8,units="in")
pdf("Primary_Analysis_Elbow_Plot.pdf")
plot(e[[1]],xlab="PC",ylab="SD",main="Elbow Plot",type='b')
abline(v=dims2use,lwd=2,col=2,lty=2)
dev.off()
working <- FindNeighbors(working,dims=1:dims2use)
working <- FindClusters(working)
working <- RunUMAP(working,dims=1:dims2use,reduction="pca",do.return=TRUE)
#tiff("Primary_Analysis_UMAP_Plot.tiff",res=300,width=8, height=8,units="in")
pdf("Primary_Analysis_UMAP_Plot.pdf")
DimPlot(working,reduction="umap",label=FALSE) + NoLegend()
dev.off()
DimPlot(working,reduction="umap",label=FALSE)
DimPlot(working,reduction="umap",label=FALSE) + NoLegend()
#tiff("Primary_Analysis_UMAP_Plot_2.tiff",res=300,width=8, height=8,units="in")
#DimPlot(working,reduction="umap",group.by = "Sample")
#dev.off()
#tiff("Primary_Analysis_UMAP_Plot_3.tiff",res=300,width=8, height=8,units="in")
#DimPlot(working,reduction="umap",group.by = "Type")
#dev.off()
#tiff("Primary_Analysis_UMAP_Plot_4.tiff",res=300,width=24, height=8,units="in")
#DimPlot(working,reduction="umap",split.by="Type",group.by="Type")
#dev.off()
original.working <- working
FeaturePlot(working,"TAU")
DotPlot(working, features = row.names(working),group.by="seurat_clusters") + RotatedAxis()
##############################################
# Output Sample vs Super Cluster Breakdown
##############################################
temp1 <- working@meta.data$Sample
temp2 <- working@meta.data$seurat_clusters
temp3 <- cbind(temp1,temp2)
temp4 <- apply(temp3,1,paste,collapse="_",sep="_")
write.table(temp4,"Sample_vs_Cluster_Breakdown.txt",sep="\t")
plot(sort(table((as.numeric(temp3[,2])-1))),ylab="Number Bins",xlab="Cluster",main="Tau (Total Bins = 2812)",type='b')
#######################################
# Generate Slide Plots
#######################################
full.bulb <- c("Final_Analysis_Ready_Counts_AZ_109_146",
"Final_Analysis_Ready_Counts_AZ_251_146",
"Final_Analysis_Ready_Counts_AZ_84_145",
"Final_Analysis_Ready_Counts_AZ_90_242",
"Final_Analysis_Ready_Counts_AZ_99_140",
"Final_Analysis_Ready_Counts_Control_H190_260",
"Final_Analysis_Ready_Counts_Control_H250_185",
"Final_Analysis_Ready_Counts_Control_OFB57_194",
"Final_Analysis_Ready_Counts_Control_OFB6A_107",
"Final_Analysis_Ready_Counts_PD_52_247",
"Final_Analysis_Ready_Counts_PD_56_236",
"Final_Analysis_Ready_Counts_PD_58_229",
"Final_Analysis_Ready_Counts_PD_77_197",
"Final_Analysis_Ready_Counts_PD_79_201")
unique.samples <- sort(unique(working@meta.data$Sample))
for(s in 1:length(unique.samples)) {
print(s)
# subset sample bin data
new.x <- working@meta.data$Xpos[working@meta.data$Sample==unique.samples[s]]
new.y <- working@meta.data$Ypos[working@meta.data$Sample==unique.samples[s]]
new.clusters <- working@meta.data$seurat_clusters[working@meta.data$Sample==unique.samples[s]]
my_color_palette <- hue_pal()(length(unique(working@meta.data$seurat_clusters)))
col.2.use <- my_color_palette[as.numeric(as.character(working@meta.data$seurat_clusters))+1][working@meta.data$Sample==unique.samples[s]]
b1 <- eval(parse(text=full.bulb[s]))
b1 <- dimnames(b1)[[1]]
b1 <- strsplit(b1,"X")
b1 <- as.data.frame(b1)
b1 <- t(b1)
# generate all detected bins plot
# temp <- paste(c(unique.samples[s],"_","All_Detected_Bins_Super_Clustering_Slide_Plot.tiff"),sep="",collapse="")
# tiff(eval(temp),width = 28950, height = 6030, units = "px",res=300)
temp <- paste(c(unique.samples[s],"_","All_Detected_Bins_Super_Clustering_Slide_Plot.pdf"),sep="",collapse="")
pdf(eval(temp),width = 28950/1000, height = 6030/1000)
plot(as.numeric(b1[,1]),-1*as.numeric(b1[,2]),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8, main="Whole Bulb")
# dev.off()
plot(c(1:length(my_color_palette)),c(1:length(my_color_palette)),col=my_color_palette,pch="-",cex=4,xlab="Label",ylab="Label",main="Color Legend")
text(c(1:round(length(my_color_palette)/2))+.9,c(1:round(length(my_color_palette)/2)),as.character((c(1:round(length(my_color_palette)/2))-1)),col="black")
text(c(round(length(my_color_palette)/2):(length(my_color_palette)-1))-.4,c((round(length(my_color_palette)/2)+1):length(my_color_palette)),as.character(c(round(length(my_color_palette)/2):(length(my_color_palette)-1))),col="black")
# generate all detected bins all clusters plot
# temp <- paste(c(unique.samples[s],"_","All_Detected_Bins_All_Clusters_Super_Clustering_Slide_Plot.tiff"),sep="",collapse="")
#tiff(eval(temp),width = 28950, height = 6030, units = "px",res=300)
plot(as.numeric(b1[,1]),-1*as.numeric(b1[,2]),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8, main="Detected Bins")
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use)
# dev.off()
# generate cluster plots
for(c in 1:length(my_color_palette)) {
# temp <- paste(c(unique.samples[s],"_Super_Clustering_",c-1,"_Slide_Plot.tiff"),sep="",collapse="")
# tiff(eval(temp),width = 28950, height = 6030, units = "px",res=300)
plot(as.numeric(b1[,1]),-1*as.numeric(b1[,2]),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8, main=as.character(c-1))
points(as.numeric(new.x[col.2.use==my_color_palette[c]]),-1*as.numeric((new.y[col.2.use==my_color_palette[c]])),col=col.2.use[col.2.use==my_color_palette[c]],cex=1,pch=15)
# dev.off()
}
dev.off()
}
#######################################
# I/O
#######################################
saveRDS(original.working,"original.working.rds")
#original.working <- readRDS("original.working.rds")
#######################################
# Perform Super Clustering
#######################################
working <- original.working
s.clusters <- as.numeric(working@meta.data$seurat_clusters)
ccc <- GetAssayData(object=working,slot="counts")
ccc <- as.matrix(ccc)
ddd <- ifelse(ccc==0,NA,ccc)
running.clusters <- unique(sort(s.clusters))
running.means <- NULL
multi.mean <- 0
if (multi.mean==1) {
temptemptemp <- NULL
temp1 <- sort(table(s.clusters))
temp2 <- temp1/10000
temp3 <- ifelse(as.numeric(unlist(temp2))<1,1,temp2)
names(temp3) <- names(temp2)
temp3 <- round(temp3,0)
replication.factor <- rev(temp3)
for(i in 1:length(running.clusters)) {
print(i)
temp <- ddd[,s.clusters==running.clusters[i]]
temptemp <- dim(temp)[2]
temp <- apply(temp,1,mean,na.rm=T)
temp <- ifelse(is.na(temp),0,temp)
temp <- round(temp,0)
rock.it <- as.numeric(unlist(replication.factor[as.character(running.clusters[i])]))
for (j in 1:rock.it) {
running.means <- cbind(running.means,temp)
temptemptemp <- c(temptemptemp,running.clusters[i])
}
}
dimnames(running.means)[[2]] <- temptemptemp
}
if (multi.mean==0) {
for(i in 1:length(running.clusters)) {
print(i)
temp <- ddd[,s.clusters==running.clusters[i]]
temptemp <- dim(temp)[2]
temp <- apply(temp,1,mean,na.rm=T)
temp <- ifelse(is.na(temp),0,temp)
temp <- round(temp,0)
running.means <- cbind(running.means,temp)
}
dimnames(running.means)[[2]] <- running.clusters
}
www <- CreateSeuratObject(counts = running.means)
www <- subset(www, subset = nCount_RNA > 0 & nFeature_RNA > 0)
www@meta.data$Prior <- colnames(www)
www <- SCTransform(www)
www <- RunPCA(www,assay="SCT",npcs=25)
saveRDS(www,"www.rds")
ee <- ElbowPlot(www, ndims=25,reduction="pca")
ee
dims2use <- 5
#tiff("Super_Cluster_Elbow_Plot.tiff",res=300,width=8, height=8,units="in")
pdf("Super_Cluster_Elbow_Plot.pdf")
plot(ee[[1]],xlab="PC",ylab="SD",main="Elbow Plot",type='b')
abline(v=dims2use,lwd=2,col=2,lty=2)
dev.off()
www <- FindNeighbors(www,dims=1:dims2use,reduction="pca")
www <- FindClusters(www,resolution=0.8) # Edit Here
www <- RunUMAP(www,dims=1:dims2use,reduction="pca",do.return=TRUE)
#tiff("Super_Cluster_UMAP_Plot_1.tiff",res=300,width=8, height=8,units="in")
pdf("Mean_Cluster_UMAP_Plot_Resolution_Default.pdf") # Edit Here
DimPlot(www,reduction="umap",label=TRUE)
dev.off()
super.clusters <- www@meta.data$seurat_clusters
working@meta.data$super.clusters <- rep(0,length(colnames(working)))
for (i in 1:length(super.clusters)) {
working@meta.data$super.clusters[s.clusters==i] <- super.clusters[i]
}
working@meta.data$super.clusters <- working@meta.data$super.clusters-1
working@meta.data$all.clusters <- working@meta.data$seurat_clusters
working@meta.data$seurat_clusters <- working@meta.data$super.clusters
#tiff("Super_Cluster_DotPlot.tiff",res=300,width=8, height=8,units="in")
pdf("Super_Cluster_DotPlot_Resolution_Default.pdf") # Edit Here
DotPlot(working, features = row.names(working),group.by="seurat_clusters") + RotatedAxis()
dev.off()
www <- FindClusters(www,resolution=1) # Edit Here
www <- RunUMAP(www,dims=1:dims2use,reduction="pca",do.return=TRUE)
#tiff("Super_Cluster_UMAP_Plot_1.tiff",res=300,width=8, height=8,units="in")
pdf("Mean_Cluster_UMAP_Plot_Resolution_1.pdf") # Edit Here
DimPlot(www,reduction="umap",label=TRUE)
dev.off()
super.clusters <- www@meta.data$seurat_clusters
working@meta.data$super.clusters <- rep(0,length(colnames(working)))
for (i in 1:length(super.clusters)) {
working@meta.data$super.clusters[s.clusters==i] <- super.clusters[i]
}
working@meta.data$super.clusters <- working@meta.data$super.clusters-1
working@meta.data$all.clusters <- working@meta.data$seurat_clusters
working@meta.data$seurat_clusters <- working@meta.data$super.clusters
#tiff("Super_Cluster_DotPlot.tiff",res=300,width=8, height=8,units="in")
pdf("Super_Cluster_DotPlot_Resolution_1.pdf") # Edit Here
DotPlot(working, features = row.names(working),group.by="seurat_clusters") + RotatedAxis()
dev.off()
www <- FindClusters(www,resolution=2) # Edit Here
www <- RunUMAP(www,dims=1:dims2use,reduction="pca",do.return=TRUE)
#tiff("Super_Cluster_UMAP_Plot_1.tiff",res=300,width=8, height=8,units="in")
pdf("Mean_Cluster_UMAP_Plot_Resolution_2.pdf") # Edit Here
DimPlot(www,reduction="umap",label=TRUE)
dev.off()
super.clusters <- www@meta.data$seurat_clusters
working@meta.data$super.clusters <- rep(0,length(colnames(working)))
for (i in 1:length(super.clusters)) {
working@meta.data$super.clusters[s.clusters==i] <- super.clusters[i]
}
working@meta.data$super.clusters <- working@meta.data$super.clusters-1
working@meta.data$all.clusters <- working@meta.data$seurat_clusters
working@meta.data$seurat_clusters <- working@meta.data$super.clusters
#tiff("Super_Cluster_DotPlot.tiff",res=300,width=8, height=8,units="in")
pdf("Super_Cluster_DotPlot_Resolution_2.pdf") # Edit Here
DotPlot(working, features = row.names(working),group.by="seurat_clusters") + RotatedAxis()
dev.off()
www <- FindClusters(www,resolution=4) # Edit Here
www <- RunUMAP(www,dims=1:dims2use,reduction="pca",do.return=TRUE)
#tiff("Super_Cluster_UMAP_Plot_1.tiff",res=300,width=8, height=8,units="in")
pdf("Mean_Cluster_UMAP_Plot_Resolution_4.pdf") # Edit Here
DimPlot(www,reduction="umap",label=TRUE)
dev.off()
super.clusters <- www@meta.data$seurat_clusters
working@meta.data$super.clusters <- rep(0,length(colnames(working)))
for (i in 1:length(super.clusters)) {
working@meta.data$super.clusters[s.clusters==i] <- super.clusters[i]
}
working@meta.data$super.clusters <- working@meta.data$super.clusters-1
working@meta.data$all.clusters <- working@meta.data$seurat_clusters
working@meta.data$seurat_clusters <- working@meta.data$super.clusters
#tiff("Super_Cluster_DotPlot.tiff",res=300,width=8, height=8,units="in")
pdf("Super_Cluster_DotPlot_Resolution_4.pdf") # Edit Here
DotPlot(working, features = row.names(working),group.by="seurat_clusters") + RotatedAxis()
dev.off()
www <- FindClusters(www,resolution=6) # Edit Here
www <- RunUMAP(www,dims=1:dims2use,reduction="pca",do.return=TRUE)
#tiff("Super_Cluster_UMAP_Plot_1.tiff",res=300,width=8, height=8,units="in")
pdf("Mean_Cluster_UMAP_Plot_Resolution_6.pdf") # Edit Here
DimPlot(www,reduction="umap",label=TRUE)
dev.off()
super.clusters <- www@meta.data$seurat_clusters
working@meta.data$super.clusters <- rep(0,length(colnames(working)))
for (i in 1:length(super.clusters)) {
working@meta.data$super.clusters[s.clusters==i] <- super.clusters[i]
}
working@meta.data$super.clusters <- working@meta.data$super.clusters-1
working@meta.data$all.clusters <- working@meta.data$seurat_clusters
working@meta.data$seurat_clusters <- working@meta.data$super.clusters
#tiff("Super_Cluster_DotPlot.tiff",res=300,width=8, height=8,units="in")
pdf("Super_Cluster_DotPlot_Resolution_6.pdf") # Edit Here
DotPlot(working, features = row.names(working),group.by="seurat_clusters") + RotatedAxis()
dev.off()
www <- FindClusters(www,resolution=8) # Edit Here
www <- RunUMAP(www,dims=1:dims2use,reduction="pca",do.return=TRUE)
#tiff("Super_Cluster_UMAP_Plot_1.tiff",res=300,width=8, height=8,units="in")
pdf("Mean_Cluster_UMAP_Plot_Resolution_8.pdf") # Edit Here
DimPlot(www,reduction="umap",label=TRUE)
dev.off()
super.clusters <- www@meta.data$seurat_clusters
working@meta.data$super.clusters <- rep(0,length(colnames(working)))
for (i in 1:length(super.clusters)) {
working@meta.data$super.clusters[s.clusters==i] <- super.clusters[i]
}
working@meta.data$super.clusters <- working@meta.data$super.clusters-1
working@meta.data$all.clusters <- working@meta.data$seurat_clusters
working@meta.data$seurat_clusters <- working@meta.data$super.clusters
#tiff("Super_Cluster_DotPlot.tiff",res=300,width=8, height=8,units="in")
pdf("Super_Cluster_DotPlot_Resolution_8.pdf") # Edit Here
DotPlot(working, features = row.names(working),group.by="seurat_clusters") + RotatedAxis()
dev.off()
www <- FindClusters(www,resolution=10) # Edit Here
www <- RunUMAP(www,dims=1:dims2use,reduction="pca",do.return=TRUE)
#tiff("Super_Cluster_UMAP_Plot_1.tiff",res=300,width=8, height=8,units="in")
pdf("Mean_Cluster_UMAP_Plot_Resolution_10.pdf") # Edit Here
DimPlot(www,reduction="umap",label=TRUE)
dev.off()
super.clusters <- www@meta.data$seurat_clusters
working@meta.data$super.clusters <- rep(0,length(colnames(working)))
for (i in 1:length(super.clusters)) {
working@meta.data$super.clusters[s.clusters==i] <- super.clusters[i]
}
working@meta.data$super.clusters <- working@meta.data$super.clusters-1
working@meta.data$all.clusters <- working@meta.data$seurat_clusters
working@meta.data$seurat_clusters <- working@meta.data$super.clusters
#tiff("Super_Cluster_DotPlot.tiff",res=300,width=8, height=8,units="in")
pdf("Super_Cluster_DotPlot_Resolution_10.pdf") # Edit Here
DotPlot(working, features = row.names(working),group.by="seurat_clusters") + RotatedAxis()
dev.off()
www <- FindClusters(www,resolution=12) # Edit Here
www <- RunUMAP(www,dims=1:dims2use,reduction="pca",do.return=TRUE)
#tiff("Super_Cluster_UMAP_Plot_1.tiff",res=300,width=8, height=8,units="in")
pdf("Mean_Cluster_UMAP_Plot_Resolution_12.pdf") # Edit Here
DimPlot(www,reduction="umap",label=TRUE)
dev.off()
super.clusters <- www@meta.data$seurat_clusters
working@meta.data$super.clusters <- rep(0,length(colnames(working)))
for (i in 1:length(super.clusters)) {
working@meta.data$super.clusters[s.clusters==i] <- super.clusters[i]
}
working@meta.data$super.clusters <- working@meta.data$super.clusters-1
working@meta.data$all.clusters <- working@meta.data$seurat_clusters
working@meta.data$seurat_clusters <- working@meta.data$super.clusters
#tiff("Super_Cluster_DotPlot.tiff",res=300,width=8, height=8,units="in")
pdf("Super_Cluster_DotPlot_Resolution_12.pdf") # Edit Here
DotPlot(working, features = row.names(working),group.by="seurat_clusters") + RotatedAxis()
dev.off()
www <- FindClusters(www,resolution=14) # Edit Here
www <- RunUMAP(www,dims=1:dims2use,reduction="pca",do.return=TRUE)
#tiff("Super_Cluster_UMAP_Plot_1.tiff",res=300,width=8, height=8,units="in")
pdf("Mean_Cluster_UMAP_Plot_Resolution_14.pdf") # Edit Here
DimPlot(www,reduction="umap",label=TRUE)
dev.off()
super.clusters <- www@meta.data$seurat_clusters
working@meta.data$super.clusters <- rep(0,length(colnames(working)))
for (i in 1:length(super.clusters)) {
working@meta.data$super.clusters[s.clusters==i] <- super.clusters[i]
}
working@meta.data$super.clusters <- working@meta.data$super.clusters-1
working@meta.data$all.clusters <- working@meta.data$seurat_clusters
working@meta.data$seurat_clusters <- working@meta.data$super.clusters
#tiff("Super_Cluster_DotPlot.tiff",res=300,width=8, height=8,units="in")
pdf("Super_Cluster_DotPlot_Resolution_14.pdf") # Edit Here
DotPlot(working, features = row.names(working),group.by="seurat_clusters") + RotatedAxis()
dev.off()
www <- FindClusters(www,resolution=16) # Edit Here
www <- RunUMAP(www,dims=1:dims2use,reduction="pca",do.return=TRUE)
#tiff("Super_Cluster_UMAP_Plot_1.tiff",res=300,width=8, height=8,units="in")
pdf("Mean_Cluster_UMAP_Plot_Resolution_16.pdf") # Edit Here
DimPlot(www,reduction="umap",label=TRUE)
dev.off()
super.clusters <- www@meta.data$seurat_clusters
working@meta.data$super.clusters <- rep(0,length(colnames(working)))
for (i in 1:length(super.clusters)) {
working@meta.data$super.clusters[s.clusters==i] <- super.clusters[i]
}
working@meta.data$super.clusters <- working@meta.data$super.clusters-1
working@meta.data$all.clusters <- working@meta.data$seurat_clusters
working@meta.data$seurat_clusters <- working@meta.data$super.clusters
#tiff("Super_Cluster_DotPlot.tiff",res=300,width=8, height=8,units="in")
pdf("Super_Cluster_DotPlot_Resolution_16.pdf") # Edit Here
DotPlot(working, features = row.names(working),group.by="seurat_clusters") + RotatedAxis()
dev.off()
www <- FindClusters(www,resolution=18) # Edit Here
www <- RunUMAP(www,dims=1:dims2use,reduction="pca",do.return=TRUE)
#tiff("Super_Cluster_UMAP_Plot_1.tiff",res=300,width=8, height=8,units="in")
pdf("Mean_Cluster_UMAP_Plot_Resolution_18.pdf") # Edit Here
DimPlot(www,reduction="umap",label=TRUE)
dev.off()
super.clusters <- www@meta.data$seurat_clusters
working@meta.data$super.clusters <- rep(0,length(colnames(working)))
for (i in 1:length(super.clusters)) {
working@meta.data$super.clusters[s.clusters==i] <- super.clusters[i]
}
working@meta.data$super.clusters <- working@meta.data$super.clusters-1
working@meta.data$all.clusters <- working@meta.data$seurat_clusters
working@meta.data$seurat_clusters <- working@meta.data$super.clusters
#tiff("Super_Cluster_DotPlot.tiff",res=300,width=8, height=8,units="in")
pdf("Super_Cluster_DotPlot_Resolution_18.pdf") # Edit Here
DotPlot(working, features = row.names(working),group.by="seurat_clusters") + RotatedAxis()
dev.off()
www <- FindClusters(www,resolution=20) # Edit Here
www <- RunUMAP(www,dims=1:dims2use,reduction="pca",do.return=TRUE)
#tiff("Super_Cluster_UMAP_Plot_1.tiff",res=300,width=8, height=8,units="in")
pdf("Mean_Cluster_UMAP_Plot_Resolution_20.pdf") # Edit Here
DimPlot(www,reduction="umap",label=TRUE)
dev.off()
super.clusters <- www@meta.data$seurat_clusters
working@meta.data$super.clusters <- rep(0,length(colnames(working)))
for (i in 1:length(super.clusters)) {
working@meta.data$super.clusters[s.clusters==i] <- super.clusters[i]
}
working@meta.data$super.clusters <- working@meta.data$super.clusters-1
working@meta.data$all.clusters <- working@meta.data$seurat_clusters
working@meta.data$seurat_clusters <- working@meta.data$super.clusters
#tiff("Super_Cluster_DotPlot.tiff",res=300,width=8, height=8,units="in")
pdf("Super_Cluster_DotPlot_Resolution_20.pdf") # Edit Here
DotPlot(working, features = row.names(working),group.by="seurat_clusters") + RotatedAxis()
dev.off()
www <- FindClusters(www,resolution=25) # Edit Here
www <- RunUMAP(www,dims=1:dims2use,reduction="pca",do.return=TRUE)
#tiff("Super_Cluster_UMAP_Plot_1.tiff",res=300,width=8, height=8,units="in")
pdf("Mean_Cluster_UMAP_Plot_Resolution_40.pdf") # Edit Here
DimPlot(www,reduction="umap",label=TRUE)
dev.off()
super.clusters <- www@meta.data$seurat_clusters
working@meta.data$super.clusters <- rep(0,length(colnames(working)))
for (i in 1:length(super.clusters)) {
working@meta.data$super.clusters[s.clusters==i] <- super.clusters[i]
}
working@meta.data$super.clusters <- working@meta.data$super.clusters-1
working@meta.data$all.clusters <- working@meta.data$seurat_clusters
working@meta.data$seurat_clusters <- working@meta.data$super.clusters
#tiff("Super_Cluster_DotPlot.tiff",res=300,width=8, height=8,units="in")
pdf("Super_Cluster_DotPlot_Resolution_40.pdf") # Edit Here
DotPlot(working, features = row.names(working),group.by="seurat_clusters") + RotatedAxis()
dev.off()
##############################################
# Output Sample vs Super Cluster Breakdown
##############################################
temp1 <- working@meta.data$Sample
temp2 <- working@meta.data$seurat_clusters
temp3 <- cbind(temp1,temp2)
temp4 <- apply(temp3,1,paste,collapse="_",sep="_")
write.table(temp4,"Sample_vs_Super_Cluster_Breakdown.txt",sep="\t")
#######################################
# Generate Slide Plots
#######################################
full.bulb <- c("Final_Analysis_Ready_Counts_AZ_109_146",
"Final_Analysis_Ready_Counts_AZ_251_146",
"Final_Analysis_Ready_Counts_AZ_84_145",
"Final_Analysis_Ready_Counts_AZ_90_242",
"Final_Analysis_Ready_Counts_AZ_99_140",
"Final_Analysis_Ready_Counts_Control_H190_260",
"Final_Analysis_Ready_Counts_Control_H250_185",
"Final_Analysis_Ready_Counts_Control_OFB57_194",
"Final_Analysis_Ready_Counts_Control_OFB6A_107",
"Final_Analysis_Ready_Counts_PD_52_247",
"Final_Analysis_Ready_Counts_PD_56_236",
"Final_Analysis_Ready_Counts_PD_58_229",
"Final_Analysis_Ready_Counts_PD_77_197",
"Final_Analysis_Ready_Counts_PD_79_201")
unique.samples <- sort(unique(working@meta.data$Sample))
for(s in 1:length(unique.samples)) {
print(s)
# subset sample bin data
new.x <- working@meta.data$Xpos[working@meta.data$Sample==unique.samples[s]]
new.y <- working@meta.data$Ypos[working@meta.data$Sample==unique.samples[s]]
new.clusters <- working@meta.data$seurat_clusters[working@meta.data$Sample==unique.samples[s]]
my_color_palette <- hue_pal()(length(unique(working@meta.data$seurat_clusters)))
col.2.use <- my_color_palette[as.numeric(as.character(working@meta.data$seurat_clusters))+1][working@meta.data$Sample==unique.samples[s]]
b1 <- eval(parse(text=full.bulb[s]))
b1 <- dimnames(b1)[[1]]
b1 <- strsplit(b1,"X")
b1 <- as.data.frame(b1)
b1 <- t(b1)
# generate all detected bins plot
# temp <- paste(c(unique.samples[s],"_","All_Detected_Bins_Super_Clustering_Slide_Plot.tiff"),sep="",collapse="")
# tiff(eval(temp),width = 28950, height = 6030, units = "px",res=300)
temp <- paste(c(unique.samples[s],"_","All_Detected_Bins_Super_Clustering_Slide_Plot.pdf"),sep="",collapse="")
pdf(eval(temp),width = 28950/1000, height = 6030/1000)
plot(as.numeric(b1[,1]),-1*as.numeric(b1[,2]),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8, main="Whole Bulb")
# dev.off()
plot(c(1:27),c(1:27),col=my_color_palette,pch="-",cex=4,xlab="Label",ylab="Label",main="Color Legend")
text(c(1:13)+.5,c(1:13),as.character(c(0:12)),col="black")
text(c(14:27)-.5,c(14:27),as.character(c(13:26)),col="black")
# generate all detected bins all clusters plot
# temp <- paste(c(unique.samples[s],"_","All_Detected_Bins_All_Clusters_Super_Clustering_Slide_Plot.tiff"),sep="",collapse="")
#tiff(eval(temp),width = 28950, height = 6030, units = "px",res=300)
plot(as.numeric(b1[,1]),-1*as.numeric(b1[,2]),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8, main="Detected Bins")
points(as.numeric(new.x),-1*as.numeric(new.y),cex=1,pch=15,col=col.2.use)
# dev.off()
# generate cluster plots
for(c in 1:length(my_color_palette)) {
# temp <- paste(c(unique.samples[s],"_Super_Clustering_",c-1,"_Slide_Plot.tiff"),sep="",collapse="")
# tiff(eval(temp),width = 28950, height = 6030, units = "px",res=300)
plot(as.numeric(b1[,1]),-1*as.numeric(b1[,2]),cex=1,pch=15,axes=FALSE,xlab="",ylab="",col=8, main=as.character(c-1))
points(as.numeric(new.x[col.2.use==my_color_palette[c]]),-1*as.numeric((new.y[col.2.use==my_color_palette[c]])),col=col.2.use[col.2.use==my_color_palette[c]],cex=1,pch=15)
# dev.off()
}
dev.off()
}
##############################################
# Generate Violin Plots per Label vs Cluster
##############################################
tiff("Violin_Plot_ALPHA_SYNUCLEIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[1],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_BETA_AMYLOID_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[2],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_CALBINDIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[3],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_CD31_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[4],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_CH_NEUN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[5],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_CNPASE_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[6],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_COLLAGEN_IV_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[7],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_DAPI_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[8],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_GFAP_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[9],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_HISTONES_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[10],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_HLADR_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[11],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_IBA1_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[12],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_MAP2_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[13],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_MBP_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[14],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_NEURO_HEAVY_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[15],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_NEURO_LIGHT_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[16],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_OMP_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[17],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_PGP9_5_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[18],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_RB_CALRETININ_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[19],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_S100_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[20],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_SYNAPTOPHYSIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[21],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_TAU_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[22],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_TOMATO_LECTIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[23],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_TYR_HYDROXYLASE_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[24],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Violin_Plot_UEA_LECTIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[25],group.by="seurat_clusters",pt.size=0)
dev.off()
# Special
tiff("Violin_Plot_TAU_vs_Cluster_vs_Type.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[22],group.by="seurat_clusters",pt.size=0,split.by="Type",col=hue_pal()(3))
dev.off()
##############################################
# Generate Ridge Plots per Label vs Cluster
##############################################
tiff("Ridge_Plot_ALPHA_SYNUCLEIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[1],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_BETA_AMYLOID_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[2],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_CALBINDIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[3],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_CD31_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[4],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_CH_NEUN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[5],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_CNPASE_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[6],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_COLLAGEN_IV_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[7],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_DAPI_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[8],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_GFAP_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[9],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_HISTONES_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[10],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_HLADR_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[11],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_IBA1_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[12],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_MAP2_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[13],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_MBP_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[14],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_NEURO_HEAVY_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[15],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_NEURO_LIGHT_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[16],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_OMP_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[17],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_PGP9_5_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[18],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_RB_CALRETININ_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[19],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_S100_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[20],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_SYNAPTOPHYSIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[21],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_TAU_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[22],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_TOMATO_LECTIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[23],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_TYR_HYDROXYLASE_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[24],group.by="seurat_clusters")
dev.off()
tiff("Ridge_Plot_UEA_LECTIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[25],group.by="seurat_clusters")
dev.off()
##############################################
# Generate Feature Plots per Label vs Cluster
##############################################
tiff("UMAP_Plot_ALPHA_SYNUCLEIN_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[1])
dev.off()
tiff("UMAP_Plot_BETA_AMYLOID_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[2])
dev.off()
tiff("UMAP_Plot_CALBINDIN_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[3])
dev.off()
tiff("UMAP_Plot_CD31_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[4])
dev.off()
tiff("UMAP_Plot_CH_NEUN_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[5])
dev.off()
tiff("UMAP_Plot_CNPASE_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[6])
dev.off()
tiff("UMAP_Plot_COLLAGEN_IV_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[7])
dev.off()
tiff("UMAP_Plot_DAPI_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[8])
dev.off()
tiff("UMAP_Plot_GFAP_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[9])
dev.off()
tiff("UMAP_Plot_HISTONES_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[10])
dev.off()
tiff("UMAP_Plot_HLADR_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[11])
dev.off()
tiff("UMAP_Plot_IBA1_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[12])
dev.off()
tiff("UMAP_Plot_MAP2_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[13])
dev.off()
tiff("UMAP_Plot_MBP_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[14])
dev.off()
tiff("UMAP_Plot_NEURO_HEAVY_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[15])
dev.off()
tiff("UMAP_Plot_NEURO_LIGHT_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[16])
dev.off()
tiff("UMAP_Plot_OMP_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[17])
dev.off()
tiff("UMAP_Plot_PGP9_5_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[18])
dev.off()
tiff("UMAP_Plot_RB_CALRETININ_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[19])
dev.off()
tiff("UMAP_Plot_S100_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[20])
dev.off()
tiff("UMAP_Plot_SYNAPTOPHYSIN_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[21])
dev.off()
tiff("UMAP_Plot_TAU_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[22])
dev.off()
tiff("UMAP_Plot_TOMATO_LECTIN_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[23])
dev.off()
tiff("UMAP_Plot_TYR_HYDROXYLASE_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[24])
dev.off()
tiff("UMAP_Plot_UEA_LECTIN_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[25])
dev.off()
tiff("UMAP_Plot_TAU_vs_Cluster_vs_Type.tiff",width = 24, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[22],split.by="Type")
dev.off()
##############################################
# Stacked BarChart
# https://protect-au.mimecast.com/s/gm_LC2xMp1UZlYQvuZPKBD?domain=r-graph-gallery.com
##############################################
library(ggplot2)
library(viridis)
running.stack <- NULL
temp0 <- sort(unique(working@meta.data$super.clusters))
for (i in 1:length(temp0)) {
print(i)
temp1 <- working@meta.data$Type[working@meta.data$super.clusters==temp0[i]]
temp2 <- table(temp1)
temp2 <- temp2[c("Control","AZ","PD")]
running.stack <- rbind(running.stack,as.numeric(temp2))
}
running.stack <- cbind(temp0,running.stack)
dimnames(running.stack)[[2]] <- c("Cluster","Control","AZ","PD")
running.stack <- as.data.frame(running.stack)
running.stack$Cluster <- as.factor(running.stack$Cluster)
temp3 <- running.stack[,c(1,2)]
temp3 <- data.frame(Cluster=as.factor(running.stack[,c(1)]),Sample=as.factor(c(rep("Control",dim(running.stack)[1]))),NumberCells=running.stack[,c(2)])
temp4 <- data.frame(Cluster=as.factor(running.stack[,c(1)]),Sample=as.factor(c(rep("AZ",dim(running.stack)[1]))),NumberCells=running.stack[,c(3)])
temp5 <- data.frame(Cluster=as.factor(running.stack[,c(1)]),Sample=as.factor(c(rep("PD",dim(running.stack)[1]))),NumberCells=running.stack[,c(4)])
temp6 <- rbind(temp4,temp3,temp5)
temp7 <- temp6[order(temp6$Cluster), ]
p1 <- ggplot(temp7, aes(fill=Sample, y=log2(NumberCells+2), x=Cluster)) +
geom_bar(position="stack", stat="identity") +
scale_fill_manual(values=hue_pal()(3)[c(1,2,3)]) + labs(y="Log2(Number Bins + 2)") + theme(panel.background = element_rect(fill = "white",colour = "white")) + geom_hline(yintercept=0, size=0.5)
p1
tiff("Bar_Plot_Version_1.tiff",res=300,width=8, height=8,units="in")
p1
dev.off()
p2 <- ggplot(temp7, aes(fill=Sample, y=NumberCells, x=Cluster)) +
geom_bar(position="fill", stat="identity") +
scale_fill_manual(values=hue_pal()(3)[c(1,2,3)]) + labs(y="Percent Total Bins") + theme(panel.background = element_rect(fill = "white",colour = "white")) + theme(panel.border = element_rect(colour = "black", fill=NA, size=0.5)) + geom_hline(yintercept=0, size=0.5)+ geom_hline(yintercept=0, size=0.5)+ geom_vline(xintercept=0, size=0.5)
p2
tiff("Bar_Plot_Version_2.tiff",res=300,width=8, height=8,units="in")
p2
dev.off()
write.table(temp7,"cluster_vs_sample_class_breakdown.txt",sep="\t")
#########################
# Sample breakdown vs cluster
#########################
running.stack <- NULL
temp0 <- sort(unique(working@meta.data$seurat_clusters))
for (i in 1:length(temp0)) {
print(i)
temp1 <- working@meta.data$Sample[working@meta.data$seurat_clusters==temp0[i]]
temp2 <- table(temp1)
temp2 <- temp2[c("AZ_109_146","AZ_251_146","AZ_84_145","AZ_90_242","AZ_99_140","Control_H190_260","Control_H246_174","Control_H250_185","Control_OFB57_194","Control_OFB6A_107","PD_52_247","PD_56_236","PD_58_229","PD_77_197","PD_79_201")]
temp2 <- as.numeric(temp2)
temp2 <- ifelse(is.na(temp2),0,temp2)
running.stack <- rbind(running.stack,as.numeric(temp2))
}
running.stack <- cbind(temp0,running.stack)
dimnames(running.stack)[[2]] <- c("Cluster","AZ_109_146","AZ_251_146","AZ_84_145","AZ_90_242","AZ_99_140","Control_H190_260","Control_H246_174","Control_H250_185","Control_OFB57_194","Control_OFB6A_107","PD_52_247","PD_56_236","PD_58_229","PD_77_197","PD_79_201")
running.stack <- as.data.frame(running.stack)
running.stack$Cluster <- as.factor(running.stack$Cluster)
temp3 <- c("AZ.1","AZ.2","AZ.3","AZ.4","AZ.5","Cntrl.1","Cntrl.2","Cntrl.3","Cntrl.4","Cntrl.5","PD.1","PD.2","PD.3","PD.4","PD.5")
temp4 <- NULL
for (i in 1:length(temp3)) {
temp5 <- data.frame(Cluster=as.factor(running.stack[,c(1)]),Sample=as.factor(c(rep(temp3[i],dim(running.stack)[1]))),NumberCells=running.stack[,c(i+1)])
temp4 <- rbind(temp4,temp5)
}
p3 <- ggplot(temp4, aes(fill=Cluster, y=log2(NumberCells+2), x=Sample)) +
geom_bar(position="stack", stat="identity") +
scale_fill_manual(values=hue_pal()(length(temp0))) + labs(y="Log2(Number Bins + 2)") + theme(panel.background = element_rect(fill = "white",colour = "white")) + geom_hline(yintercept=0, size=0.5)
tiff("Bar_Plot_Version_3.tiff",res=300,width=8, height=8,units="in")
p3
dev.off()
p4 <- ggplot(temp4, aes(fill=Cluster, y=NumberCells, x=Sample)) +
geom_bar(position="fill", stat="identity") +
scale_fill_manual(values=hue_pal()(length(temp0))) + labs(y="Percent Total Bins") + theme(panel.background = element_rect(fill = "white",colour = "white")) + geom_hline(yintercept=0, size=0.5) + geom_hline(yintercept=1, size=0.5) + geom_hline(yintercept=0.5, size=0.25,linetype="dashed") + geom_hline(yintercept=0.25, size=0.25,linetype="dashed")+ geom_hline(yintercept=0.75, size=0.25,linetype="dashed")
p4
tiff("Bar_Plot_Version_4.tiff",res=300,width=8, height=8,units="in")
p4
dev.off()
###########################################
# Stacked Violin Plot
# https://protect-au.mimecast.com/s/3oc1C3QNqKI9AkvwhRw2Eu?domain=divingintogeneticsandgenomics.rbind.io
# https://protect-au.mimecast.com/s/QczfC4QOrYI90Vxyhywan6?domain=github.com
###########################################
library(patchwork)
modify_vlnplot<- function(obj,
feature,
pt.size = 0,
plot.margin = unit(c(-0.75, 0, -0.75, 0), "cm"),
...) {
p<- VlnPlot(obj, features = feature, pt.size = pt.size, ... ) +
xlab("") + ylab(feature) + ggtitle("") +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_text(size = rel(1), angle = 0),
axis.text.y = element_text(size = rel(1)),
plot.margin = plot.margin )
return(p)
}
extract_max<- function(p){
ymax<- max(ggplot_build(p)$layout$panel_scales_y[[1]]$range$range)
return(ceiling(ymax))
}
StackedVlnPlot<- function(obj, features,
pt.size = 0,
plot.margin = unit(c(-0.75, 0, -0.75, 0), "cm"),
...) {
plot_list<- purrr::map(features, function(x) modify_vlnplot(obj = obj,feature = x, ...))
# Add back x-axis title to bottom plot. patchwork is going to support this?
plot_list[[length(plot_list)]]<- plot_list[[length(plot_list)]] +
theme(axis.text.x=element_text(), axis.ticks.x = element_line())
# change the y-axis tick to only max value
ymaxs<- purrr::map_dbl(plot_list, extract_max)
plot_list<- purrr::map2(plot_list, ymaxs, function(x,y) x +
scale_y_continuous(breaks = c(y)) +
expand_limits(y = y))
p<- patchwork::wrap_plots(plotlist = plot_list, ncol = 1)
return(p)
}
working2 <- working
working2@meta.data$seurat_clusters <- factor(working2@meta.data$seurat_clusters, levels = as.character(c(0:20)))
levels(working2@meta.data$seurat_clusters)
tiff("Stacked_Violin_Plot.tiff",res=300,width=12, height=12,units="in")
StackedVlnPlot(working2, features=rownames(working),group.by="seurat_clusters")
dev.off()
##############################################
# Copy output objects to Windows FileShare
##############################################
scp -vr *.tiff johnsonko@nindsdirbis7.ninds.nih.gov:/shares/PI/Alan_Koretsky/Helen_Murray/Spatial_Transcriptomics/20200508
#######################################
# I/O
#######################################
saveRDS(working,"final.working.rds")
original.working <- readRDS("original.working.rds")
final.working <- readRDS("final.working.rds")
#######################################
#######################################
#######################################
#######################################
#######################################
# Subset out cluster Zero
#######################################
#######################################
#######################################
#######################################
#######################################
##############################################
# Generate Dot Plot
##############################################
tiff("Super_Cluster_DotPlot.tiff",res=300,width=8, height=8,units="in")
DotPlot(working, features = row.names(working),group.by="seurat_clusters") + RotatedAxis()
dev.off()
##############################################
# Remove Cluster Zero
##############################################
zeroless <- subset(working, subset = seurat_clusters != 0)
zero.only <- subset(working, subset = seurat_clusters == 0)
#############################################################
# Perform Clustering
#############################################################
zero.only <- SCTransform(zero.only)
zero.only <- RunPCA(zero.only,assay="SCT",npcs=25)
e <- ElbowPlot(zero.only, ndims=24,reduction="pca")
sd.cut.2.use <- 2
dims2use <- sum(e[[1]][,2]>sd.cut.2.use)
tiff("Zero_Cluster_Only_Primary_Analysis_Elbow_Plot.tiff",res=300,width=8, height=8,units="in")
plot(e[[1]],xlab="PC",ylab="SD",main="Elbow Plot",type='b')
abline(h=sd.cut.2.use,lwd=2,col=2,lty=2)
abline(v=dims2use,lwd=2,col=2,lty=2)
dev.off()
zero.only <- FindNeighbors(zero.only,dims=1:dims2use,reduction="pca")
zero.only <- FindClusters(zero.only)
zero.only <- RunUMAP(zero.only,dims=1:dims2use,reduction="pca",do.return=TRUE)
tiff("Zero_Cluster_Only_Primary_Analysis_UMAP_Plot_1.tiff",res=300,width=8, height=8,units="in")
DimPlot(zero.only,reduction="umap",label=FALSE) + NoLegend()
dev.off()
tiff("Zero_Cluster_Only_Primary_Analysis_UMAP_Plot_2.tiff",res=300,width=8, height=8,units="in")
DimPlot(zero.only,reduction="umap",group.by = "Sample")
dev.off()
tiff("Zero_Cluster_Only_Primary_Analysis_UMAP_Plot_3.tiff",res=300,width=8, height=8,units="in")
DimPlot(zero.only,reduction="umap",group.by = "Type")
dev.off()
tiff("Zero_Cluster_Only_Primary_Analysis_UMAP_Plot_4.tiff",res=300,width=24, height=8,units="in")
DimPlot(zero.only,reduction="umap",split.by="Type",group.by="Type")
dev.off()
original.zero.only <- zero.only
saveRDS(original.zero.only,"original.zero.only.rds")
#######################################
# Perform Super Clustering
#######################################
working <- original.zero.only
s.clusters <- as.numeric(working@meta.data$seurat_clusters)
ccc <- GetAssayData(object=working,slot="counts")
ccc <- as.matrix(ccc)
ddd <- ifelse(ccc==0,NA,ccc)
running.clusters <- unique(sort(s.clusters))
running.means <- NULL
multi.mean <- 0
if (multi.mean==1) {
temptemptemp <- NULL
temp1 <- sort(table(s.clusters))
temp2 <- temp1/10000
temp3 <- ifelse(as.numeric(unlist(temp2))<1,1,temp2)
names(temp3) <- names(temp2)
temp3 <- round(temp3,0)
replication.factor <- rev(temp3)
for(i in 1:length(running.clusters)) {
print(i)
temp <- ddd[,s.clusters==running.clusters[i]]
temptemp <- dim(temp)[2]
temp <- apply(temp,1,mean,na.rm=T)
temp <- ifelse(is.na(temp),0,temp)
temp <- round(temp,0)
rock.it <- as.numeric(unlist(replication.factor[as.character(running.clusters[i])]))
for (j in 1:rock.it) {
running.means <- cbind(running.means,temp)
temptemptemp <- c(temptemptemp,running.clusters[i])
}
}
dimnames(running.means)[[2]] <- temptemptemp
}
if (multi.mean==0) {
for(i in 1:length(running.clusters)) {
print(i)
temp <- ddd[,s.clusters==running.clusters[i]]
temptemp <- dim(temp)[2]
temp <- apply(temp,1,mean,na.rm=T)
temp <- ifelse(is.na(temp),0,temp)
temp <- round(temp,0)
running.means <- cbind(running.means,temp)
}
dimnames(running.means)[[2]] <- running.clusters
}
www <- CreateSeuratObject(counts = running.means)
www <- subset(www, subset = nCount_RNA > 0 & nFeature_RNA > 0)
www@meta.data$Prior <- colnames(www)
www <- SCTransform(www)
www <- RunPCA(www,assay="SCT",npcs=25)
e <- ElbowPlot(www, ndims=24,reduction="pca")
sd.cut.2.use <- 1
dims2use <- sum(e[[1]][,2]>sd.cut.2.use)
tiff("Zero_Cluster_Only_Super_Cluster_Elbow_Plot.tiff",res=300,width=8, height=8,units="in")
plot(e[[1]],xlab="PC",ylab="SD",main="Elbow Plot",type='b')
abline(h=sd.cut.2.use,lwd=2,col=2,lty=2)
abline(v=dims2use,lwd=2,col=2,lty=2)
dev.off()
www <- FindNeighbors(www,dims=1:dims2use,reduction="pca")
www <- FindClusters(www,resolution = 2) # <====== Can Edit Here !!!!!!!
#www <- FindClusters(www)
www <- RunUMAP(www,dims=1:dims2use,reduction="pca",do.return=TRUE)
tiff("Zero_Cluster_Only_Super_Cluster_UMAP_Plot_1.tiff",res=300,width=8, height=8,units="in")
DimPlot(www,reduction="umap",label=TRUE)
dev.off()
super.clusters <- www@meta.data$seurat_clusters
working@meta.data$super.clusters <- rep(0,length(colnames(working)))
for (i in 1:length(super.clusters)) {
working@meta.data$super.clusters[s.clusters==i] <- super.clusters[i]
}
working@meta.data$super.clusters <- working@meta.data$super.clusters-1
working@meta.data$all.clusters <- working@meta.data$seurat_clusters
working@meta.data$seurat_clusters <- working@meta.data$super.clusters
tiff("Zero_Cluster_Only_Super_Cluster_UMAP_Plot_2.tiff",res=300,width=8, height=8,units="in")
DimPlot(working,reduction="umap",group.by="seurat_clusters",label=TRUE)
dev.off()
tiff("Zero_Cluster_Only_Super_Cluster_UMAP_Plot_3.tiff",res=300,width=24, height=8,units="in")
DimPlot(working,reduction="umap",split.by="Type",group.by="seurat_clusters",label=TRUE)
dev.off()
tiff("Zero_Cluster_Only_Super_Cluster_UMAP_Plot_4.tiff",res=300,width=40, height=24,units="in")
DimPlot(working,reduction="umap",split.by="Sample",group.by="seurat_clusters",label=TRUE,ncol=5)
dev.off()
##############################################
# Output Sample vs Super Cluster Breakdown
##############################################
temp1 <- working@meta.data$Sample
temp2 <- working@meta.data$seurat_clusters
temp3 <- cbind(temp1,temp2)
temp4 <- apply(temp3,1,paste,collapse="_",sep="_")
write.table(temp4,"Zero_Cluster_Only_Sample_vs_Super_Cluster_Breakdown.txt",sep="\t")
##############################################
# Generate Dot Plot
##############################################
tiff("Zero_Cluster_Only_Super_Cluster_DotPlot.tiff",res=300,width=8, height=8,units="in")
DotPlot(working, features = row.names(working),group.by="seurat_clusters") + RotatedAxis()
dev.off()
#######################################
# Generate Slide Plots
#######################################
unique.samples <- sort(unique(working@meta.data$Sample))
for(s in 1:length(unique.samples)) {
print(s)
# subset sample bin data
new.x <- working@meta.data$Xpos[working@meta.data$Sample==unique.samples[s]]
new.y <- working@meta.data$Ypos[working@meta.data$Sample==unique.samples[s]]
new.clusters <- working@meta.data$seurat_clusters[working@meta.data$Sample==unique.samples[s]]
my_color_palette <- hue_pal()(length(unique(working@meta.data$seurat_clusters)))
col.2.use <- my_color_palette[as.numeric(as.character(working@meta.data$seurat_clusters))+1][working@meta.data$Sample==unique.samples[s]]
# generate all detected bins plot
temp <- paste(c(unique.samples[s],"_","Zero_Cluster_Only_All_Detected_Bins_Super_Clustering_Slide_Plot.tiff"),sep="",collapse="")
tiff(eval(temp),width = 28950, height = 6030, units = "px",res=300)
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=sqrt(31),pch=15,axes=FALSE,xlab="",ylab="",col=8)
dev.off()
# generate all detected bins all clusters plot
temp <- paste(c(unique.samples[s],"_","Zero_Cluster_Only_All_Detected_Bins_All_Clusters_Super_Clustering_Slide_Plot.tiff"),sep="",collapse="")
tiff(eval(temp),width = 28950, height = 6030, units = "px",res=300)
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=sqrt(31),pch=15,axes=FALSE,xlab="",ylab="",col=col.2.use)
dev.off()
# generate cluster plots
for(c in 1:length(my_color_palette)) {
temp <- paste(c(unique.samples[s],"_Zero_Cluster_Only_Super_Clustering_",c-1,"_Slide_Plot.tiff"),sep="",collapse="")
tiff(eval(temp),width = 28950, height = 6030, units = "px",res=300)
plot(as.numeric(new.x),-1*as.numeric(new.y),col=8,pch=15,cex=sqrt(31),axes=FALSE,xlab="",ylab="")
points(as.numeric(new.x[col.2.use==my_color_palette[c]]),-1*as.numeric((new.y[col.2.use==my_color_palette[c]])),col=col.2.use[col.2.use==my_color_palette[c]],cex=sqrt(31),pch=15)
dev.off()
}
}
##############################################
# Generate Violin Plots per Label vs Cluster
##############################################
tiff("Zero_Cluster_Only_Violin_Plot_ALPHA_SYNUCLEIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[1],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_BETA_AMYLOID_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[2],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_CALBINDIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[3],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_CD31_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[4],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_CH_NEUN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[5],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_CNPASE_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[6],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_COLLAGEN_IV_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[7],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_DAPI_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[8],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_GFAP_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[9],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_HISTONES_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[10],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_HLADR_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[11],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_IBA1_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[12],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_MAP2_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[13],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_MBP_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[14],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_NEURO_HEAVY_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[15],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_NEURO_LIGHT_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[16],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_OMP_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[17],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_PGP9_5_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[18],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_RB_CALRETININ_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[19],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_S100_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[20],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_SYNAPTOPHYSIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[21],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_TAU_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[22],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_TOMATO_LECTIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[23],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_TYR_HYDROXYLASE_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[24],group.by="seurat_clusters",pt.size=0)
dev.off()
tiff("Zero_Cluster_Only_Violin_Plot_UEA_LECTIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[25],group.by="seurat_clusters",pt.size=0)
dev.off()
# Special
tiff("Zero_Cluster_Only_Violin_Plot_TAU_vs_Cluster_vs_Type.tiff",width = 12, height = 8, units = "in",res=300)
VlnPlot(working, features = rownames(working)[22],group.by="seurat_clusters",pt.size=0,split.by="Type",col=hue_pal()(3))
dev.off()
##############################################
# Generate Ridge Plots per Label vs Cluster
##############################################
tiff("Zero_Cluster_Only_Ridge_Plot_ALPHA_SYNUCLEIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[1],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_BETA_AMYLOID_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[2],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_CALBINDIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[3],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_CD31_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[4],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_CH_NEUN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[5],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_CNPASE_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[6],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_COLLAGEN_IV_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[7],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_DAPI_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[8],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_GFAP_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[9],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_HISTONES_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[10],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_HLADR_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[11],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_IBA1_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[12],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_MAP2_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[13],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_MBP_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[14],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_NEURO_HEAVY_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[15],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_NEURO_LIGHT_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[16],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_OMP_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[17],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_PGP9_5_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[18],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_RB_CALRETININ_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[19],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_S100_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[20],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_SYNAPTOPHYSIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[21],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_TAU_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[22],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_TOMATO_LECTIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[23],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_TYR_HYDROXYLASE_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[24],group.by="seurat_clusters")
dev.off()
tiff("Zero_Cluster_Only_Ridge_Plot_UEA_LECTIN_vs_Cluster.tiff",width = 12, height = 8, units = "in",res=300)
RidgePlot(working, features = rownames(working)[25],group.by="seurat_clusters")
dev.off()
##############################################
# Generate Feature Plots per Label vs Cluster
##############################################
tiff("Zero_Cluster_Only_UMAP_Plot_ALPHA_SYNUCLEIN_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[1])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_BETA_AMYLOID_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[2])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_CALBINDIN_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[3])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_CD31_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[4])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_CH_NEUN_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[5])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_CNPASE_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[6])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_COLLAGEN_IV_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[7])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_DAPI_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[8])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_GFAP_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[9])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_HISTONES_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[10])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_HLADR_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[11])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_IBA1_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[12])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_MAP2_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[13])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_MBP_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[14])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_NEURO_HEAVY_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[15])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_NEURO_LIGHT_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[16])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_OMP_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[17])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_PGP9_5_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[18])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_RB_CALRETININ_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[19])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_S100_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[20])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_SYNAPTOPHYSIN_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[21])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_TAU_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[22])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_TOMATO_LECTIN_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[23])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_TYR_HYDROXYLASE_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[24])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_UEA_LECTIN_vs_Cluster.tiff",width = 8, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[25])
dev.off()
tiff("Zero_Cluster_Only_UMAP_Plot_TAU_vs_Cluster_vs_Type.tiff",width = 24, height = 8, units = "in",res=300)
FeaturePlot(working, features = rownames(working)[22],split.by="Type")
dev.off()
##############################################
# Stacked BarChart
# https://protect-au.mimecast.com/s/gm_LC2xMp1UZlYQvuZPKBD?domain=r-graph-gallery.com
##############################################
library(ggplot2)
library(viridis)
running.stack <- NULL
temp0 <- sort(unique(working@meta.data$super.clusters))
for (i in 1:length(temp0)) {
print(i)
temp1 <- working@meta.data$Type[working@meta.data$super.clusters==temp0[i]]
temp2 <- table(temp1)
temp2 <- temp2[c("Control","AZ","PD")]
running.stack <- rbind(running.stack,as.numeric(temp2))
}
running.stack <- cbind(temp0,running.stack)
dimnames(running.stack)[[2]] <- c("Cluster","Control","AZ","PD")
running.stack <- as.data.frame(running.stack)
running.stack$Cluster <- as.factor(running.stack$Cluster)
temp3 <- running.stack[,c(1,2)]
temp3 <- data.frame(Cluster=as.factor(running.stack[,c(1)]),Sample=as.factor(c(rep("Control",dim(running.stack)[1]))),NumberCells=running.stack[,c(2)])
temp4 <- data.frame(Cluster=as.factor(running.stack[,c(1)]),Sample=as.factor(c(rep("AZ",dim(running.stack)[1]))),NumberCells=running.stack[,c(3)])
temp5 <- data.frame(Cluster=as.factor(running.stack[,c(1)]),Sample=as.factor(c(rep("PD",dim(running.stack)[1]))),NumberCells=running.stack[,c(4)])
temp6 <- rbind(temp4,temp3,temp5)
temp7 <- temp6[order(temp6$Cluster), ]
p1 <- ggplot(temp7, aes(fill=Sample, y=log2(NumberCells+2), x=Cluster)) +
geom_bar(position="stack", stat="identity") +
scale_fill_manual(values=hue_pal()(3)[c(1,2,3)]) + labs(y="Log2(Number Bins + 2)") + theme(panel.background = element_rect(fill = "white",colour = "white")) + geom_hline(yintercept=0, size=0.5)
p1
tiff("Zero_Cluster_Only_Bar_Plot_Version_1.tiff",res=300,width=8, height=8,units="in")
p1
dev.off()
p2 <- ggplot(temp7, aes(fill=Sample, y=NumberCells, x=Cluster)) +
geom_bar(position="fill", stat="identity") +
scale_fill_manual(values=hue_pal()(3)[c(1,2,3)]) + labs(y="Percent Total Bins") + theme(panel.background = element_rect(fill = "white",colour = "white")) + theme(panel.border = element_rect(colour = "black", fill=NA, size=0.5)) + geom_hline(yintercept=0, size=0.5)+ geom_hline(yintercept=0, size=0.5)+ geom_vline(xintercept=0, size=0.5)
p2
tiff("Zero_Cluster_Only_Bar_Plot_Version_2.tiff",res=300,width=8, height=8,units="in")
p2
dev.off()
write.table(temp7,"Zero_Cluster_Only_cluster_vs_sample_class_breakdown.txt",sep="\t")
#########################
# Sample breakdown vs cluster
#########################
running.stack <- NULL
temp0 <- sort(unique(working@meta.data$seurat_clusters))
for (i in 1:length(temp0)) {
print(i)
temp1 <- working@meta.data$Sample[working@meta.data$seurat_clusters==temp0[i]]
temp2 <- table(temp1)
temp2 <- temp2[c("AZ_109_146","AZ_251_146","AZ_84_145","AZ_90_242","AZ_99_140","Control_H190_260","Control_H246_174","Control_H250_185","Control_OFB57_194","Control_OFB6A_107","PD_52_247","PD_56_236","PD_58_229","PD_77_197","PD_79_201")]
temp2 <- as.numeric(temp2)
temp2 <- ifelse(is.na(temp2),0,temp2)
running.stack <- rbind(running.stack,as.numeric(temp2))
}
running.stack <- cbind(temp0,running.stack)
dimnames(running.stack)[[2]] <- c("Cluster","AZ_109_146","AZ_251_146","AZ_84_145","AZ_90_242","AZ_99_140","Control_H190_260","Control_H246_174","Control_H250_185","Control_OFB57_194","Control_OFB6A_107","PD_52_247","PD_56_236","PD_58_229","PD_77_197","PD_79_201")
running.stack <- as.data.frame(running.stack)
running.stack$Cluster <- as.factor(running.stack$Cluster)
temp3 <- c("AZ.1","AZ.2","AZ.3","AZ.4","AZ.5","Cntrl.1","Cntrl.2","Cntrl.3","Cntrl.4","Cntrl.5","PD.1","PD.2","PD.3","PD.4","PD.5")
temp4 <- NULL
for (i in 1:length(temp3)) {
temp5 <- data.frame(Cluster=as.factor(running.stack[,c(1)]),Sample=as.factor(c(rep(temp3[i],dim(running.stack)[1]))),NumberCells=running.stack[,c(i+1)])
temp4 <- rbind(temp4,temp5)
}
p3 <- ggplot(temp4, aes(fill=Cluster, y=log2(NumberCells+2), x=Sample)) +
geom_bar(position="stack", stat="identity") +
scale_fill_manual(values=hue_pal()(length(temp0))) + labs(y="Log2(Number Bins + 2)") + theme(panel.background = element_rect(fill = "white",colour = "white")) + geom_hline(yintercept=0, size=0.5)
tiff("Zero_Cluster_Only_Bar_Plot_Version_3.tiff",res=300,width=8, height=8,units="in")
p3
dev.off()
p4 <- ggplot(temp4, aes(fill=Cluster, y=NumberCells, x=Sample)) +
geom_bar(position="fill", stat="identity") +
scale_fill_manual(values=hue_pal()(length(temp0))) + labs(y="Percent Total Bins") + theme(panel.background = element_rect(fill = "white",colour = "white")) + geom_hline(yintercept=0, size=0.5) + geom_hline(yintercept=1, size=0.5) + geom_hline(yintercept=0.5, size=0.25,linetype="dashed") + geom_hline(yintercept=0.25, size=0.25,linetype="dashed")+ geom_hline(yintercept=0.75, size=0.25,linetype="dashed")
p4
tiff("Zero_Cluster_Only_Bar_Plot_Version_4.tiff",res=300,width=8, height=8,units="in")
p4
dev.off()
###########################################
# Stacked Violin Plot
# https://protect-au.mimecast.com/s/3oc1C3QNqKI9AkvwhRw2Eu?domain=divingintogeneticsandgenomics.rbind.io/
# https://protect-au.mimecast.com/s/QczfC4QOrYI90Vxyhywan6?domain=github.com
###########################################
library(patchwork)
modify_vlnplot<- function(obj,
feature,
pt.size = 0,
plot.margin = unit(c(-0.75, 0, -0.75, 0), "cm"),
...) {
p<- VlnPlot(obj, features = feature, pt.size = pt.size, ... ) +
xlab("") + ylab(feature) + ggtitle("") +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_text(size = rel(1), angle = 0),
axis.text.y = element_text(size = rel(1)),
plot.margin = plot.margin )
return(p)
}
extract_max<- function(p){
ymax<- max(ggplot_build(p)$layout$panel_scales_y[[1]]$range$range)
return(ceiling(ymax))
}
StackedVlnPlot<- function(obj, features,
pt.size = 0,
plot.margin = unit(c(-0.75, 0, -0.75, 0), "cm"),
...) {
plot_list<- purrr::map(features, function(x) modify_vlnplot(obj = obj,feature = x, ...))
# Add back x-axis title to bottom plot. patchwork is going to support this?
plot_list[[length(plot_list)]]<- plot_list[[length(plot_list)]] +
theme(axis.text.x=element_text(), axis.ticks.x = element_line())
# change the y-axis tick to only max value
ymaxs<- purrr::map_dbl(plot_list, extract_max)
plot_list<- purrr::map2(plot_list, ymaxs, function(x,y) x +
scale_y_continuous(breaks = c(y)) +
expand_limits(y = y))
p<- patchwork::wrap_plots(plotlist = plot_list, ncol = 1)
return(p)
}
working2 <- working
working2@meta.data$seurat_clusters <- factor(working2@meta.data$seurat_clusters, levels = as.character(c(0:20)))
levels(working2@meta.data$seurat_clusters)
tiff("Zero_Cluster_Only_Stacked_Violin_Plot.tiff",res=300,width=12, height=12,units="in")
StackedVlnPlot(working2, features=rownames(working),group.by="seurat_clusters")
dev.off()
#######################################
# I/O
#######################################
saveRDS(working,"zero.only.final.working.rds")
original.working <- readRDS("original.working.rds")
final.working <- readRDS("final.working.rds")
#############################################################
# Repeat Primary Clustering Without Super Cluster 0
#############################################################
ffff <- subset(working, subset = nCount_RNA > 0 & nFeature_RNA > 0 & super.clusters!=1)
# ffff <- SCTransform(ffff)
# ffff <- RunPCA(ffff,assay="SCT",npcs=100)
# e <- ElbowPlot(ffff, ndims=24,reduction="pca")
# sd.cut.2.use <- 2
# dims2use <- sum(e[[1]][,2]>sd.cut.2.use)
# tiff("Repeat_Primary_Analysis_Elbow_Plot.tiff",res=300,width=8, height=8,units="in")
# plot(e[[1]],xlab="PC",ylab="SD",main="Elbow Plot",type='b')
# abline(h=sd.cut.2.use,lwd=2,col=2,lty=2)
# abline(v=dims2use,lwd=2,col=2,lty=2)
# dev.off()
# ffff <- FindNeighbors(ffff,dims=1:dims2use,reduction="pca")
# ffff <- FindClusters(ffff)
# ffff <- RunUMAP(ffff,dims=1:dims2use,reduction="pca",do.return=TRUE)
tiff("Repeat_Primary_Analysis_UMAP_Plot_1.tiff",res=72,width=8, height=8,units="in")
DimPlot(ffff,reduction="umap",label=FALSE) + NoLegend()
dev.off()
tiff("Repeat_Primary_Analysis_UMAP_Plot_2.tiff",res=72,width=8, height=8,units="in")
DimPlot(ffff,reduction="umap",group.by = "Sample")
dev.off()
tiff("Repeat_Primary_Analysis_UMAP_Plot_3.tiff",res=72,width=8, height=8,units="in")
DimPlot(ffff,reduction="umap",group.by = "Type")
dev.off()
tiff("Repeat_Primary_Analysis_UMAP_Plot_4.tiff",res=72,width=24, height=8,units="in")
DimPlot(ffff,reduction="umap",split.by="Type",group.by="Type")
dev.off()
#######################################
# Repeat Secondary Super Clustering
#######################################
s.clusters <- as.numeric(ffff@meta.data$seurat_clusters)
ccc <- GetAssayData(object=ffff,slot="counts")
ccc <- as.matrix(ccc)
ddd <- ifelse(ccc==0,NA,ccc)
running.clusters <- unique(sort(s.clusters))
running.means <- NULL
for(i in 1:length(running.clusters)) {
print(i)
temp <- ddd[,s.clusters==running.clusters[i]]
temp <- apply(temp,1,mean,na.rm=T)
temp <- ifelse(is.na(temp),0,temp)
temp <- round(temp,0)
running.means <- cbind(running.means,temp)
}
dimnames(running.means)[[2]] <- running.clusters
xxxx <- CreateSeuratObject(counts = running.means)
xxxx <- subset(xxxx, subset = nCount_RNA > 0 & nFeature_RNA > 0)
xxxx <- SCTransform(xxxx)
xxxx <- RunPCA(xxxx,assay="SCT",npcs=100)
e <- ElbowPlot(xxxx, ndims=24,reduction="pca")
sd.cut.2.use <- 1
dims2use <- sum(e[[1]][,2]>sd.cut.2.use)
tiff("Repeat_Super_Cluster_Elbow_Plot.tiff",res=300,width=8, height=8,units="in")
plot(e[[1]],xlab="PC",ylab="SD",main="Elbow Plot",type='b')
abline(h=sd.cut.2.use,lwd=2,col=2,lty=2)
abline(v=dims2use,lwd=2,col=2,lty=2)
dev.off()
xxxx <- FindNeighbors(xxxx,dims=1:dims2use,reduction="pca")
xxxx <- FindClusters(xxxx)
xxxx <- RunUMAP(xxxx,dims=1:dims2use,reduction="pca",do.return=TRUE)
tiff("Repeat_Super_Cluster_UMAP_Plot_1.tiff",res=300,width=8, height=8,units="in")
DimPlot(xxxx,reduction="umap",label=TRUE)
dev.off()
super.clusters <- xxxx@meta.data$seurat_clusters
ffff@meta.data$super.clusters <- rep(0,length(colnames(ffff)))
for (i in 1:length(super.clusters)) {
ffff@meta.data$super.clusters[s.clusters==i] <- super.clusters[i]
}
ffff@meta.data$super.clusters <- ffff@meta.data$super.clusters-1
ffff@meta.data$all.clusters <- ffff@meta.data$seurat_clusters
ffff@meta.data$seurat_clusters <- ffff@meta.data$super.clusters
tiff("Repeat_Super_Cluster_UMAP_Plot_2.tiff",res=300,width=8, height=8,units="in")
DimPlot(ffff,reduction="umap",group.by="seurat_clusters",label=TRUE)
dev.off()
tiff("Repeat_Super_Cluster_UMAP_Plot_3.tiff",res=300,width=24, height=8,units="in")
DimPlot(ffff,reduction="umap",split.by="Type",group.by="seurat_clusters",label=TRUE)
dev.off()
##############################################
# Save Relevant R Objects
##############################################
saveRDS(working,"working.rds")
saveRDS(ffff,"ffff.rds")
saveRDS(xxxx,"xxxx.rds")
working <- readRDS("working.rds")
www <- readRDS("www.rds")
#######################################
# Generate Slide Plots
#######################################
unique.samples <- sort(unique(ffff@meta.data$Sample))
for(s in 1:length(unique.samples)) {
print(s)
# subset sample bin data
new.x <- ffff@meta.data$Xpos[ffff@meta.data$Sample==unique.samples[s]]
new.y <- ffff@meta.data$Ypos[ffff@meta.data$Sample==unique.samples[s]]
new.clusters <- ffff@meta.data$seurat_clusters[ffff@meta.data$Sample==unique.samples[s]]
my_color_palette <- hue_pal()(length(unique(ffff@meta.data$seurat_clusters)))
col.2.use <- my_color_palette[as.numeric(as.character(ffff@meta.data$seurat_clusters))+1][ffff@meta.data$Sample==unique.samples[s]]
# generate all detected bins plot
temp <- paste(c(unique.samples[s],"_","All_Detected_Bins_Repeat_Super_Clustering_Slide_Plot.tiff"),sep="",collapse="")
tiff(eval(temp),width = 28950, height = 6030, units = "px",res=300)
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=sqrt(31),pch=15,axes=FALSE,xlab="",ylab="",col=8)
dev.off()
# generate all detected bins all clusters plot
temp <- paste(c(unique.samples[s],"_","All_Detected_Bins_All_Clusters_Repeat_Super_Clustering_Slide_Plot.tiff"),sep="",collapse="")
tiff(eval(temp),width = 28950, height = 6030, units = "px",res=300)
plot(as.numeric(new.x),-1*as.numeric(new.y),cex=sqrt(31),pch=15,axes=FALSE,xlab="",ylab="",col=col.2.use)
dev.off()
# generate cluster plots
for(c in 1:length(my_color_palette)) {
temp <- paste(c(unique.samples[s],"_Repeat_Super_Clustering_",c-1,"_Slide_Plot.tiff"),sep="",collapse="")
tiff(eval(temp),width = 28950, height = 6030, units = "px",res=300)
plot(as.numeric(new.x),-1*as.numeric(new.y),col=8,pch=15,cex=sqrt(31),axes=FALSE,xlab="",ylab="")
points(as.numeric(new.x[col.2.use==my_color_palette[c]]),-1*as.numeric((new.y[col.2.use==my_color_palette[c]])),col=col.2.use[col.2.use==my_color_palette[c]],cex=sqrt(31),pch=15)
dev.off()
}
}
##############################################
# Generate Dot Plots
##############################################
tiff("Repeat_Super_Cluster_DotPlot.tiff",res=300,width=8, height=8,units="in")
DotPlot(ffff, features = row.names(ffff),group.by="seurat_clusters") + RotatedAxis()
dev.off()
##############################################
# Copy output objects to Windows FileShare
##############################################
# scp -vr *.tiff johnsonko@nindsdirbis7.ninds.nih.gov:/shares/PI/Alan_Koretsky/Helen_Murray/Spatial_Transcriptomics/20200501
##############################################
# Save Relevant R Objects
##############################################
saveRDS(working,"working.rds")
saveRDS(www,"www.rds")
saveRDS(ffff,"ffff.rds")
saveRDS(xxxx,"xxxx.rds")
##############################################
# Stacked BarChart
##############################################
library(ggplot2)
library(viridis)
https://protect-au.mimecast.com/s/gm_LC2xMp1UZlYQvuZPKBD?domain=r-graph-gallery.com
ffff@meta.data$Type
ffff <- working
running.stack <- NULL
temp0 <- sort(unique(ffff@meta.data$super.clusters))
for (i in 1:length(temp0)) {
print(i)
temp1 <- ffff@meta.data$Type[ffff@meta.data$super.clusters==temp0[i]]
temp2 <- table(temp1)
temp2 <- temp2[c("Control","AZ","PD")]
running.stack <- rbind(running.stack,as.numeric(temp2))
}
running.stack <- cbind(temp0,running.stack)
dimnames(running.stack)[[2]] <- c("Cluster","Control","AZ","PD")
running.stack <- as.data.frame(running.stack)
running.stack$Cluster <- as.factor(running.stack$Cluster)
temp3 <- running.stack[,c(1,2)]
temp3 <- data.frame(Cluster=as.factor(running.stack[,c(1)]),Sample=as.factor(c(rep("Control",dim(running.stack)[1]))),NumberCells=running.stack[,c(2)])
temp4 <- data.frame(Cluster=as.factor(running.stack[,c(1)]),Sample=as.factor(c(rep("AZ",dim(running.stack)[1]))),NumberCells=running.stack[,c(3)])
temp5 <- data.frame(Cluster=as.factor(running.stack[,c(1)]),Sample=as.factor(c(rep("PD",dim(running.stack)[1]))),NumberCells=running.stack[,c(4)])
temp6 <- rbind(temp4,temp3,temp5)
temp7 <- temp6[order(temp6$Cluster), ]
p1 <- ggplot(temp7, aes(fill=Sample, y=NumberCells, x=Cluster)) +
geom_bar(position="stack", stat="identity") +
scale_fill_manual(values=hue_pal()(3)[c(1,2,3)]) + labs(y="Number Bins") + theme(panel.background = element_rect(fill = "white",colour = "white"))+ geom_hline(yintercept=0, size=0.5)+ geom_hline(yintercept=80000, size=0.5,linetype="dashed")+ geom_hline(yintercept=60000, size=0.5,linetype="dashed")+ geom_hline(yintercept=40000, size=0.5,linetype="dashed")+ geom_hline(yintercept=20000, size=0.5,linetype="dashed")
p1 <- ggplot(temp7, aes(fill=Sample, y=log2(NumberCells+2), x=Cluster)) +
geom_bar(position="stack", stat="identity") +
scale_fill_manual(values=hue_pal()(3)[c(1,2,3)]) + labs(y="Log2(Number Bins + 2)") + theme(panel.background = element_rect(fill = "white",colour = "white")) + geom_hline(yintercept=0, size=0.5)
p1
tiff("Bar_Plot_Version_1.tiff",res=300,width=8, height=8,units="in")
p1
dev.off()
+ geom_hline(yintercept=0, size=0.5)+ geom_hline(yintercept=80000, size=0.5,linetype="dashed")+ geom_hline(yintercept=60000, size=0.5,linetype="dashed")+ geom_hline(yintercept=40000, size=0.5,linetype="dashed")+ geom_hline(yintercept=20000, size=0.5,linetype="dashed")
p2 <- ggplot(temp7, aes(fill=Sample, y=NumberCells, x=Cluster)) +
geom_bar(position="fill", stat="identity") +
scale_fill_manual(values=hue_pal()(3)[c(1,2,3)]) + labs(y="Percent Total Bins") + theme(panel.background = element_rect(fill = "white",colour = "white")) + theme(panel.border = element_rect(colour = "black", fill=NA, size=0.5)) + geom_hline(yintercept=0, size=0.5)+ geom_hline(yintercept=0, size=0.5)+ geom_vline(xintercept=0, size=0.5)
p2 <- ggplot(temp7, aes(fill=Sample, y=NumberCells, x=Cluster)) +
geom_bar(position="fill", stat="identity") +
scale_fill_manual(values=hue_pal()(3)[c(1,2,3)]) + labs(y="Percent Total Bins") + theme(panel.background = element_rect(fill = "white",colour = "white")) + geom_hline(yintercept=0, size=0.5) + geom_hline(yintercept=1, size=0.5) + geom_hline(yintercept=0.5, size=0.25,linetype="dashed") + geom_hline(yintercept=0.25, size=0.25,linetype="dashed")+ geom_hline(yintercept=0.75, size=0.25,linetype="dashed")
tiff("Bar_Plot_Version_2.tiff",res=300,width=8, height=8,units="in")
p2
dev.off()
write.table(temp7,"cluster_vs_sample_class.txt",sep="\t")
#########################
# Sample breakdown vs cluster
#########################
ffff <- working
running.stack <- NULL
temp0 <- sort(unique(ffff@meta.data$seurat_clusters))
for (i in 1:length(temp0)) {
print(i)
temp1 <- ffff@meta.data$Sample[ffff@meta.data$seurat_clusters==temp0[i]]
temp2 <- table(temp1)
temp2 <- temp2[c("AZ_109_146","AZ_251_146","AZ_84_145","AZ_90_242","AZ_99_140","Control_H190_260","Control_H246_174","Control_H250_185","Control_OFB57_194","Control_OFB6A_107","PD_52_247","PD_56_236","PD_58_229","PD_77_197","PD_79_201")]
temp2 <- as.numeric(temp2)
temp2 <- ifelse(is.na(temp2),0,temp2)
running.stack <- rbind(running.stack,as.numeric(temp2))
}
running.stack <- cbind(temp0,running.stack)
dimnames(running.stack)[[2]] <- c("Cluster","AZ_109_146","AZ_251_146","AZ_84_145","AZ_90_242","AZ_99_140","Control_H190_260","Control_H246_174","Control_H250_185","Control_OFB57_194","Control_OFB6A_107","PD_52_247","PD_56_236","PD_58_229","PD_77_197","PD_79_201")
running.stack <- as.data.frame(running.stack)
running.stack$Cluster <- as.factor(running.stack$Cluster)
temp3 <- c("AZ.1","AZ.2","AZ.3","AZ.4","AZ.5","Cntrl.1","Cntrl.2","Cntrl.3","Cntrl.4","Cntrl.5","PD.1","PD.2","PD.3","PD.4","PD.5")
temp4 <- NULL
for (i in 1:length(temp3)) {
temp5 <- data.frame(Cluster=as.factor(running.stack[,c(1)]),Sample=as.factor(c(rep(temp3[i],dim(running.stack)[1]))),NumberCells=running.stack[,c(i+1)])
temp4 <- rbind(temp4,temp5)
}
p3 <- ggplot(temp4, aes(fill=Cluster, y=log2(NumberCells+2), x=Sample)) +
geom_bar(position="stack", stat="identity") +
scale_fill_manual(values=hue_pal()(length(temp0))) + labs(y="Log2(Number Bins + 2)") + theme(panel.background = element_rect(fill = "white",colour = "white")) + geom_hline(yintercept=0, size=0.5)
tiff("Bar_Plot_Version_3.tiff",res=300,width=8, height=8,units="in")
p3
dev.off()
p4 <- ggplot(temp4, aes(fill=Cluster, y=NumberCells, x=Sample)) +
geom_bar(position="fill", stat="identity") +
scale_fill_manual(values=hue_pal()(length(temp0))) + labs(y="Percent Total Bins") + theme(panel.background = element_rect(fill = "white",colour = "white")) + geom_hline(yintercept=0, size=0.5) + geom_hline(yintercept=1, size=0.5) + geom_hline(yintercept=0.5, size=0.25,linetype="dashed") + geom_hline(yintercept=0.25, size=0.25,linetype="dashed")+ geom_hline(yintercept=0.75, size=0.25,linetype="dashed")
tiff("Bar_Plot_Version_4.tiff",res=300,width=8, height=8,units="in")
p4
dev.off()
###########################################
###########################################
###########################################
###########################################
https://protect-au.mimecast.com/s/3oc1C3QNqKI9AkvwhRw2Eu?domain=divingintogeneticsandgenomics.rbind.io/
https://protect-au.mimecast.com/s/QczfC4QOrYI90Vxyhywan6?domain=github.com
library(Seurat)
library(patchwork)
library(ggplot2)
## remove the x-axis text and tick
## plot.margin to adjust the white space between each plot.
## ... pass any arguments to VlnPlot in Seurat
modify_vlnplot<- function(obj,
feature,
pt.size = 0,
plot.margin = unit(c(-0.75, 0, -0.75, 0), "cm"),
...) {
p<- VlnPlot(obj, features = feature, pt.size = pt.size, ... ) +
xlab("") + ylab(feature) + ggtitle("") +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_text(size = rel(1), angle = 0),
axis.text.y = element_text(size = rel(1)),
plot.margin = plot.margin )
return(p)
}
## extract the max value of the y axis
extract_max<- function(p){
ymax<- max(ggplot_build(p)$layout$panel_scales_y[[1]]$range$range)
return(ceiling(ymax))
}
## main function
StackedVlnPlot<- function(obj, features,
pt.size = 0,
plot.margin = unit(c(-0.75, 0, -0.75, 0), "cm"),
...) {
plot_list<- purrr::map(features, function(x) modify_vlnplot(obj = obj,feature = x, ...))
# Add back x-axis title to bottom plot. patchwork is going to support this?
plot_list[[length(plot_list)]]<- plot_list[[length(plot_list)]] +
theme(axis.text.x=element_text(), axis.ticks.x = element_line())
# change the y-axis tick to only max value
ymaxs<- purrr::map_dbl(plot_list, extract_max)
plot_list<- purrr::map2(plot_list, ymaxs, function(x,y) x +
scale_y_continuous(breaks = c(y)) +
expand_limits(y = y))
p<- patchwork::wrap_plots(plotlist = plot_list, ncol = 1)
return(p)
}
working2 <- working
working2@meta.data$all.clusters <- working2@meta.data$seurat_clusters
working2@meta.data$SCT_snn_res.0.8 <- working2@meta.data$seurat_clusters
Idents(working2) <- working2@meta.data$seurat_clusters
StackedVlnPlot(working, features=rownames(working),group.by="seurat_clusters")
levels(working2@meta.data$seurat_clusters)
working2 <- working
working2@meta.data$seurat_clusters <- factor(working2@meta.data$seurat_clusters, levels = as.character(c(0:17)))
levels(working2@meta.data$seurat_clusters)
tiff("Stacked_Violin_Plot.tiff",res=300,width=12, height=12,units="in")
StackedVlnPlot(working, features=rownames(working),group.by="seurat_clusters")
dev.off()