| Previous changeset 11:89783b79ef25 (2016-03-16) Next changeset 13:e74888e94f96 (2016-03-16) |
|
Commit message:
Uploaded |
|
added:
Dotplot_Release/BaitCheck.pl Dotplot_Release/Normalization.R Dotplot_Release/Normalization_sigpreys.R Dotplot_Release/R_dotPlot.R Dotplot_Release/R_dotPlot_hc.R Dotplot_Release/R_dotPlot_nc.R Dotplot_Release/SOFD.pl Dotplot_Release/SaintConvert.pl Dotplot_Release/Step1_data_reformating.R Dotplot_Release/Step2_data_filtering.R Dotplot_Release/Step3_nestedcluster Dotplot_Release/Step4_biclustering.R Dotplot_Release/biclust.tar.gz Dotplot_Release/biclust_param.txt Dotplot_Release/dotplot.bash Dotplot_Release/pheatmap_j.R Dotplot_Release/test_files/SC_SAINT_list.txt |
|
removed:
prohits_dotplot_generator/test_files/SC_SAINT_list.txt |
| b |
| diff -r 89783b79ef25 -r f48b1312b6dd Dotplot_Release/BaitCheck.pl --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Dotplot_Release/BaitCheck.pl Wed Mar 16 12:09:43 2016 -0400 |
| [ |
| @@ -0,0 +1,45 @@ +#!/usr/bin/perl + +# 27/04/2014 + +if($#ARGV==0){ + print "This program checks the number of baits in a Saint Output File.\n"; + print "\nusage:\n $0\n-i [csv saint output file]]\n\n"; + die; +} +else{ + $i=0; + $cutoff=0.01; + while($i<=$#ARGV){ + if($ARGV[$i] eq '-i'){ + $i++; + $ifile=$ARGV[$i]; + } + else{ + die "\Incorrect program usage\n\n"; + } + $i++; + } +} + +$file=''; +open(IFILE,"<$ifile") || die "$ifile can't be opened: $!"; +{ local $/=undef; $file=<IFILE>; } +@lines=split /[\r\n]+/, $file; +foreach $line (@lines) { + if($line =~ /^Bait/){ + } + elsif($line =~ /^([^\t]+)/){ + if($1 ne $bait[$baitn]){ + $baitn++; + $bait[$baitn]=$1; + } + } + else{ + } +} +close(IFILE); + +print $baitn; + + |
| b |
| diff -r 89783b79ef25 -r f48b1312b6dd Dotplot_Release/Normalization.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Dotplot_Release/Normalization.R Wed Mar 16 12:09:43 2016 -0400 |
| [ |
| @@ -0,0 +1,44 @@ +#!/usr/bin/env Rscript + +args <- commandArgs(trailingOnly = TRUE) + +#this programs normalizes a saint input file based on the spectral counts of all preys + +d = read.delim(args[1], header=T, sep="\t", as.is=T) + +baitn = 1 +curr_bait <- d$Bait[1] +s <- vector() +s[1] = 0 +for(i in 1:length(d$Bait)){ + if(curr_bait != d$Bait[i]){ + baitn <- baitn + 1 + curr_bait <- d$Bait[i] + s[baitn] <- d$AvgSpec[i] + } + else{ + s[baitn] <- s[baitn] + d$AvgSpec[i] + } +} + +med.s = median(s) +s = s / med.s + +d_n <- d +baitn = 1 +curr_bait <- d_n$Bait[1] +for(i in 1:length(d_n$Bait)){ + if(curr_bait != d_n$Bait[i]){ + baitn <- baitn + 1 + curr_bait <- d_n$Bait[i] + d_n$AvgSpec[i] <- d_n$AvgSpec[i]/s[baitn] + } + else{ + d_n$AvgSpec[i] <- d_n$AvgSpec[i]/s[baitn] + } +} + +#print normalized data to file + +write.table(d_n, file = "norm_saint.txt", sep="\t", quote=F, row.names=F) + |
| b |
| diff -r 89783b79ef25 -r f48b1312b6dd Dotplot_Release/Normalization_sigpreys.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Dotplot_Release/Normalization_sigpreys.R Wed Mar 16 12:09:43 2016 -0400 |
| [ |
| @@ -0,0 +1,46 @@ +#!/usr/bin/env Rscript + +#this programs normalizes a saint input file based on the spectral counts of "signficant" preys +# that is, preys with an FDR <= the secondary cutoff as supplied to the dotplot script + +args <- commandArgs(trailingOnly = TRUE) + +d = read.delim(args[1], header=T, as.is=T) +d <- d[d$BFDR <= as.numeric(args[2]),] + +baitn = 1 +curr_bait <- d$Bait[1] +s <- vector() +s[1] = 0 +for(i in 1:length(d$Bait)){ + if(curr_bait != d$Bait[i]){ + baitn <- baitn + 1 + curr_bait <- d$Bait[i] + s[baitn] <- d$AvgSpec[i] + } + else{ + s[baitn] <- s[baitn] + d$AvgSpec[i] + } +} + +med.s = median(s) +s = s / med.s + +d_n <- d +baitn = 1 +curr_bait <- d_n$Bait[1] +for(i in 1:length(d_n$Bait)){ + if(curr_bait != d_n$Bait[i]){ + baitn <- baitn + 1 + curr_bait <- d_n$Bait[i] + d_n$AvgSpec[i] <- d_n$AvgSpec[i]/s[baitn] + } + else{ + d_n$AvgSpec[i] <- d_n$AvgSpec[i]/s[baitn] + } +} + +#print normalized data to file + +write.table(d_n, file = "norm_saint.txt", sep="\t", quote=F, row.names=F) + |
| b |
| diff -r 89783b79ef25 -r f48b1312b6dd Dotplot_Release/R_dotPlot.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Dotplot_Release/R_dotPlot.R Wed Mar 16 12:09:43 2016 -0400 |
| [ |
| @@ -0,0 +1,83 @@ +#!/usr/bin/env Rscript + +args <- commandArgs(trailingOnly = TRUE) + +library('latticeExtra') +library('colorRamps') + +data.file <- read.table("SC_data.txt", sep="\t", header=TRUE, row.names=1) ### import spectral count data +data.file2 <- read.table("FDR_data.txt", sep="\t", header=TRUE, row.names=1) ### import FDR count data +data.file3 <- read.table("clustered_matrix.txt", sep="\t", header=TRUE, row.names=1) ### import clustered matrix +data.file4 <- scan("singletons.txt", what="", sep="\n", strip.white=T) ### import singleton data + +#setting parameters + +Sfirst=as.numeric(args[1]) #first FDR cutoff +Ssecond=as.numeric(args[2]) #second FDR cutoff +maxp=as.integer(args[3]) #maximum value for a spectral count + +#calculate column and row lengths + +#determine bait and prey ordering + +bait_levels=names(data.file3) +prey_levels=c(rownames(data.file3),data.file4) + +x_ord=factor(row.names(data.file),levels=prey_levels) +y_ord=factor(names(data.file),levels=bait_levels) + +df<-data.frame(y=rep(y_ord,nrow(data.file)) + ,x=rep(x_ord, each=ncol(data.file)) + ,z1=as.vector(t(data.file)) # Circle color + ,z2=as.vector(t(data.file/apply(data.file,1,max))) # Circle size + ,z3=as.vector(t(data.file2)) # FDR +) + +df$z1[df$z1>maxp] <- maxp #maximum value for spectral count +df$z2[df$z2==0] <- NA +df$z3[df$z3>Ssecond] <- 0.05*maxp +df$z3[df$z3<=Ssecond & df$z3>Sfirst] <- 0.5*maxp +df$z3[df$z3<=Sfirst] <- 1*maxp +df$z4 <- df$z1 +df$z4[df$z4==0] <- 0 +df$z4[df$z4>0] <- 2.5 + +# The labeling for the colorkey + +labelat = c(0, maxp) +labeltext = c(0, maxp) + +# color scheme to use + +nmb.colors<-maxp +z.colors<-grey(rev(seq(0,0.9,0.9/nmb.colors))) #grayscale color scale + +#plot + +pl <- levelplot(z1~x*y, data=df + ,col.regions =z.colors #terrain.colors(100) + ,scales = list(x = list(rot = 90), y=list(cex=0.8), tck=0) # rotates X,Y labels and changes scale + ,colorkey = FALSE + ,xlab="Prey", ylab="Bait" + ,panel=function(x,y,z,...,col.regions){ + print(x) + z.c<-df$z1[ (df$x %in% as.character(x)) & (df$y %in% y)] + z.2<-df$z2[ (df$x %in% as.character(x)) & (df$y %in% y)] + z.3<-df$z3 + z.4<-df$z4 + panel.xyplot(x,y + ,as.table=TRUE + ,pch=21 # point type to use (circles in this case) + ,cex=((z.2-min(z.2,na.rm=TRUE))/(max(z.2,na.rm=TRUE)-min(z.2,na.rm=TRUE)))*3 #circle size + ,fill=z.colors[floor((z.c-min(z.c,na.rm=TRUE))*nmb.colors/(max(z.c,na.rm=TRUE)-min(z.c,na.rm=TRUE)))+1] # circle colors + ,col=z.colors[1+z.3] # border colors + ,lex=z.4 #border thickness + ) + } + #,main="Fold change" # graph main title + ) +if(ncol(data.file) > 4) ht=3.5+(0.36*((ncol(data.file)-1)-4)) else ht=3.5 +if(nrow(data.file) > 20) wd=8.25+(0.29*(nrow(data.file)-20)) else wd=5+(0.28*(nrow(data.file)-10)) +pdf("dotplot.pdf", onefile = FALSE, paper = "special", height = ht, width = wd, pointsize = 2) +print(pl) +dev.off() |
| b |
| diff -r 89783b79ef25 -r f48b1312b6dd Dotplot_Release/R_dotPlot_hc.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Dotplot_Release/R_dotPlot_hc.R Wed Mar 16 12:09:43 2016 -0400 |
| [ |
| @@ -0,0 +1,125 @@ +#!/usr/bin/env Rscript + +args <- commandArgs(trailingOnly = TRUE) + +pheatmapj_loc <- paste(args[6],"pheatmap_j.R",sep="/") +heatmap2j_loc <- paste(args[6],"heatmap_2j.R",sep="/") + +library('latticeExtra') +library('RColorBrewer') +library('grid') +library(reshape2) +library('gplots') +library('gtools') +source(pheatmapj_loc) +source(heatmap2j_loc) + +data.file <- read.table("SC_data.txt", sep="\t", header=TRUE, row.names=1) ### import spectral count data +data.file2 <- read.table("FDR_data.txt", sep="\t", header=TRUE, row.names=1) ### import FDR count data + +#setting parameters + +Sfirst=as.numeric(args[1]) #first FDR cutoff +Ssecond=as.numeric(args[2]) #second FDR cutoff +maxp=as.integer(args[3]) #maximum value for a spectral count +methd <- args[4] +dist_methd <- args[5] + +#determine bait and prey ordering + +dist_bait <- dist(as.matrix(t(data.file)), method= dist_methd) # "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski" +dist_prey <- dist(as.matrix(data.file), method= dist_methd) + +if(methd == "ward"){ + dist_bait <- dist_bait^2 #comment out this line and the next if not using Ward's method of clustering + dist_prey <- dist_prey^2 +} + +hc_bait <- hclust(dist_bait, method = methd) # method = "average", "single", "complete", "ward", "mcquitty", "median" or "centroid" +hc_prey <- hclust(dist_prey, method = methd) + +data.file = data.file[hc_prey$order, , drop = FALSE] +data.file = data.file[, hc_bait$order, drop = FALSE] +data.file2 = data.file2[hc_prey$order, , drop = FALSE] +data.file2 = data.file2[, hc_bait$order, drop = FALSE] + +x_ord=factor(row.names(data.file), levels=row.names(data.file)) +y_ord=factor(names(data.file[1,]), levels=names(data.file[1,])) + +df<-data.frame(y=rep(y_ord, nrow(data.file)) + ,x=rep(x_ord, each=ncol(data.file)) + ,z1=as.vector(t(data.file)) # Circle color + ,z2=as.vector(t(data.file/apply(data.file,1,max))) # Circle size + ,z3=as.vector(t(data.file2)) # FDR +) + +df$z1[df$z1>maxp] <- maxp #maximum value for spectral count +df$z2[df$z2==0] <- NA +df$z3[df$z3>Ssecond] <- 0.05*maxp +df$z3[df$z3<=Ssecond & df$z3>Sfirst] <- 0.5*maxp +df$z3[df$z3<=Sfirst] <- 1*maxp +df$z4 <- df$z1 +df$z4[df$z4==0] <- 0 +df$z4[df$z4>0] <- 2.5 + +# The labeling for the colorkey + +labelat = c(0, maxp) +labeltext = c(0, maxp) + +# color scheme to use + +nmb.colors<-maxp +z.colors<-grey(rev(seq(0,0.9,0.9/nmb.colors))) #grayscale color scale + +#plot dotplot + +pl <- levelplot(z1~x*y, data=df + ,col.regions =z.colors #terrain.colors(100) + ,scales = list(x = list(rot = 90), y=list(cex=0.8), tck=0) # rotates X,Y labels and changes scale + ,colorkey = FALSE + #,colorkey = list(space="bottom", width=1.5, height=0.3, labels=list(at = labelat, labels = labeltext)) #put colorkey at top with my labeling scheme + ,xlab="Prey", ylab="Bait" + ,panel=function(x,y,z,...,col.regions){ + print(x) + z.c<-df$z1[ (df$x %in% as.character(x)) & (df$y %in% y)] + z.2<-df$z2[ (df$x %in% as.character(x)) & (df$y %in% y)] + z.3<-df$z3 + z.4<-df$z4 + panel.xyplot(x,y + ,as.table=TRUE + ,pch=21 # point type to use (circles in this case) + ,cex=((z.2-min(z.2,na.rm=TRUE))/(max(z.2,na.rm=TRUE)-min(z.2,na.rm=TRUE)))*3 #circle size + ,fill=z.colors[floor((z.c-min(z.c,na.rm=TRUE))*nmb.colors/(max(z.c,na.rm=TRUE)-min(z.c,na.rm=TRUE)))+1] # circle colors + ,col=z.colors[1+z.3] # border colors + ,lex=z.4 #border thickness + ) + } + #,main="Fold change" # graph main title + ) +if(ncol(data.file) > 4) ht=3.5+(0.36*((ncol(data.file)-1)-4)) else ht=3.5 +if(nrow(data.file) > 20) wd=8.25+(0.29*(nrow(data.file) -20)) else wd=5.7+(0.28*(nrow(data.file) -10)) +pdf("dotplot.pdf", onefile = FALSE, paper = "special", height = ht, width = wd, pointsize = 2) +print(pl) +dev.off() + +#plot bait vs prey heatmap + +heat_df <- acast(df, y~x, value.var="z1") +heat_df <- apply(heat_df, 2, rev) + +if(ncol(data.file) > 4) ht=3.5+(0.1*((ncol(data.file)-1)-4)) else ht=3.5 +if(nrow(data.file) > 20) wd=8.25+(0.1*(nrow(data.file)-20)) else wd=5+(0.1*(nrow(data.file)-10)) +pdf("heatmap_borders.pdf", onefile = FALSE, paper = "special", height = ht, width = wd, pointsize = 2) +pheatmap_j(heat_df, scale="none", border_color="black", border_width = 0.1, cluster_rows=FALSE, cluster_cols=FALSE, col=colorRampPalette(c("#FFFFFF", brewer.pal(9,"Blues")))(100)) +dev.off() + +pdf("heatmap_no_borders.pdf", onefile = FALSE, paper = "special", height = ht, width = wd, pointsize = 2) +pheatmap_j(heat_df, scale="none", border_color=NA, cluster_rows=FALSE, cluster_cols=FALSE, col=colorRampPalette(c("#FFFFFF", brewer.pal(9,"Blues")))(100)) +dev.off() + +#plot bait vs bait heatmap using dist matrix +dist_bait <- dist_bait/max(dist_bait) +pdf("bait2bait.pdf", onefile = FALSE, paper = "special") +heatmap_2j(as.matrix(dist_bait), trace="none", scale="none", density.info="none", col=rev(colorRampPalette(c("#FFFFFF", brewer.pal(9,"Blues")))(100)), xMin=0, xMax=1, margins=c(1.5*max(nchar(rownames(as.matrix(dist_bait)))),1.5*max(nchar(colnames(as.matrix(dist_bait)))))) +dev.off() |
| b |
| diff -r 89783b79ef25 -r f48b1312b6dd Dotplot_Release/R_dotPlot_nc.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Dotplot_Release/R_dotPlot_nc.R Wed Mar 16 12:09:43 2016 -0400 |
| [ |
| @@ -0,0 +1,140 @@ +#!/usr/bin/env Rscript + +args <- commandArgs(trailingOnly = TRUE) + +pheatmapj_loc <- paste(args[9],"pheatmap_j.R",sep="/") + +library('latticeExtra') +library('RColorBrewer') +library('grid') +library(reshape2) +source(pheatmapj_loc) + +data.file <- read.table("SC_data.txt", sep="\t", header=TRUE, row.names=1) ### import spectral count data +data.file2 <- read.table("FDR_data.txt", sep="\t", header=TRUE, row.names=1) ### import FDR count data +bait_l <- scan(args[4], what="") ### import bait list +if(args[5] == 0) prey_l <- scan(args[6], what="") ### import prey list +methd <- args[7] +dist_methd <- args[8] + +#setting parameters + +Sfirst=as.numeric(args[1]) #first FDR cutoff +Ssecond=as.numeric(args[2]) #second FDR cutoff +maxp=as.integer(args[3]) #maximum value for a spectral count + +#extract only needed data + +if(args[5] == 0){ + remove <- vector() + remove <- prey_l[prey_l %in% row.names(data.file)] + prey_l <- prey_l[prey_l %in% remove] + remove <- bait_l[bait_l %in% names(data.file)] + bait_l <- bait_l[bait_l %in% remove] + data.file <- data.file[prey_l, bait_l] + data.file2 <- data.file2[prey_l, bait_l] +} else{ + remove <- vector() + remove <- bait_l[bait_l %in% names(data.file)] + bait_l <- bait_l[bait_l %in% remove] + data.file <- data.file[, bait_l] + data.file2 <- data.file2[, bait_l] + prey_keep = apply(data.file2, 1, function(x) sum(x<=Sfirst) >= 1) + data.file <- data.file[prey_keep,] + data.file2 <- data.file2[prey_keep,] +} + +#determine bait and prey ordering + +y_ord=factor(names(data.file[1,]),levels=bait_l) + +if(args[5] == 0){ + x_ord=factor(rownames(data.file),levels=prey_l) +} else { + + data.file <- data.file[which(rowSums(data.file) > 0),] + dist_prey <- dist(as.matrix(data.file), method= dist_methd) + + if(methd == "ward"){ + dist_prey <- dist_prey^2 + } + + hc_prey <- hclust(dist_prey, method = methd) + + data.file = data.file[hc_prey$order, , drop = FALSE] + data.file2 = data.file2[hc_prey$order, , drop = FALSE] + + x_ord=factor(row.names(data.file), levels=row.names(data.file)) +} + +df<-data.frame(y=rep(y_ord, nrow(data.file)) + ,x=rep(x_ord, each=ncol(data.file)) + ,z1=as.vector(t(data.file)) # Circle color + ,z2=as.vector(t(data.file/apply(data.file,1,max))) # Circle size + ,z3=as.vector(t(data.file2)) # FDR +) + +df$z1[df$z1>maxp] <- maxp #maximum value for spectral count +df$z2[df$z2==0] <- NA +df$z3[df$z3>Ssecond] <- 0.05*maxp +df$z3[df$z3<=Ssecond & df$z3>Sfirst] <- 0.5*maxp +df$z3[df$z3<=Sfirst] <- 1*maxp +df$z4 <- df$z1 +df$z4[df$z4==0] <- 0 +df$z4[df$z4>0] <- 2.5 + +# The labeling for the colorkey + +labelat = c(0, maxp) +labeltext = c(0, maxp) + +# color scheme to use + +nmb.colors<-maxp +z.colors<-grey(rev(seq(0,0.9,0.9/nmb.colors))) #grayscale color scale + +#plot dotplot + +pl <- levelplot(z1~x*y, data=df + ,col.regions =z.colors #terrain.colors(100) + ,scales = list(x = list(rot = 90), y=list(cex=0.8), tck=0) # rotates X,Y labels and changes scale + ,colorkey = FALSE + #,colorkey = list(space="bottom", width=1.5, height=0.3, labels=list(at = labelat, labels = labeltext)) #put colorkey at top with my labeling scheme + ,xlab="Prey", ylab="Bait" + ,panel=function(x,y,z,...,col.regions){ + print(x) + z.c<-df$z1[ (df$x %in% as.character(x)) & (df$y %in% y)] + z.2<-df$z2[ (df$x %in% as.character(x)) & (df$y %in% y)] + z.3<-df$z3 + z.4<-df$z4 + panel.xyplot(x,y + ,as.table=TRUE + ,pch=21 # point type to use (circles in this case) + ,cex=((z.2-min(z.2,na.rm=TRUE))/(max(z.2,na.rm=TRUE)-min(z.2,na.rm=TRUE)))*3 #circle size + ,fill=z.colors[floor((z.c-min(z.c,na.rm=TRUE))*nmb.colors/(max(z.c,na.rm=TRUE)-min(z.c,na.rm=TRUE)))+1] # circle colors + ,col=z.colors[1+z.3] # border colors + ,lex=z.4 #border thickness + ) + } + #,main="Fold change" # graph main title + ) +if(ncol(data.file) > 4) ht=3.5+(0.36*((ncol(data.file)-1)-4)) else ht=3.5 +if(nrow(data.file) > 20) wd=8.25+(0.29*(nrow(data.file)-20)) else wd=5.7+(0.28*(nrow(data.file)-10)) +pdf("dotplot.pdf", onefile = FALSE, paper = "special", height = ht, width = wd, pointsize = 2) +print(pl) +dev.off() + +#plot heatmap + +heat_df <- acast(df, y~x, value.var="z1") +heat_df <- apply(heat_df, 2, rev) + +if(ncol(data.file) > 4) ht=3.5+(0.1*((ncol(data.file)-1)-4)) else ht=3.5 +if(nrow(data.file) > 20) wd=8.25+(0.1*(nrow(data.file)-20)) else wd=5+(0.1*(nrow(data.file)-10)) +pdf("heatmap_borders.pdf", onefile = FALSE, paper = "special", height = ht, width = wd, pointsize = 2) +pheatmap_j(heat_df, scale="none", border_color="black", border_width = 0.1, cluster_rows=FALSE, cluster_cols=FALSE, col=colorRampPalette(c("#FFFFFF", brewer.pal(9,"Blues")))(100)) +dev.off() + +pdf("heatmap_no_borders.pdf", onefile = FALSE, paper = "special", height = ht, width = wd, pointsize = 2) +pheatmap_j(heat_df, scale="none", border_color=NA, cluster_rows=FALSE, cluster_cols=FALSE, col=colorRampPalette(c("#FFFFFF", brewer.pal(9,"Blues")))(100)) +dev.off() |
| b |
| diff -r 89783b79ef25 -r f48b1312b6dd Dotplot_Release/SOFD.pl --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Dotplot_Release/SOFD.pl Wed Mar 16 12:09:43 2016 -0400 |
| [ |
| @@ -0,0 +1,112 @@ +#!/usr/bin/perl + +# 17/12/2013 + +if($#ARGV==0){ + print "This program takes the Saint Output File and produces two matrices.\n"; + print "One has average spectral counts and the other FDR scores,\n"; + print "\nusage:\n $0\n-i [csv saint output file]\n-s [FDR cutoff, default=0.01]\n\n"; + die; +} +else{ + $i=0; + $cutoff=0.01; + $spec_cutoff=0; + while($i<=$#ARGV){ + if($ARGV[$i] eq '-i'){ + $i++; + $ifile=$ARGV[$i]; + } + elsif($ARGV[$i] eq '-s'){ + $i++; + if($ARGV[$i]>1 || $ARGV[$i]<0){ + die "\nFDR cutoff must be between 0 and 1 \n\n"; + } + $cutoff=$ARGV[$i]; + } + elsif($ARGV[$i] eq '-x'){ + $i++; + if($ARGV[$i]<0){ + die "\nAvgSpec cutoff must be > 0 \n\n"; + } + $spec_cutoff=$ARGV[$i]; + } + else{ + die "\Incorrect program usage\n\n"; + } + $i++; + } +} + +$baitn=0, $bait[0]=xxxx, $sig_preysn=0; +$file=''; +open(IFILE,"<$ifile") || die "$ifile can't be opened: $!"; +{ local $/=undef; $file=<IFILE>; } +@lines=split /[\r\n]+/, $file; +foreach $line (@lines) { + if($line =~ /^Bait/){ + } + elsif($line =~ /^([^\t]+)\t[^\t]+\t([^\t]+)\t[^\t]+\t[\d]+\t([\d\.]+)\t[\d]+\t[^\t]+\t[^\t]+\t[^\t]+\t[^\t]+\t[^\t]+\t([^\t]+)\t[^\t]+\t([^\t]+)\t/){ + if($1 ne $bait[$baitn]){ + $baitn++; + $bait[$baitn]=$1; + $preyn[$baitn]=0; + } + $preyn[$baitn]++; + $preys[$baitn][$preyn[$baitn]]=$2; + $avgspec[$baitn][$preyn[$baitn]]=$3; + $saint[$baitn][$preyn[$baitn]]=$4; + $fdr[$baitn][$preyn[$baitn]]=$5; + if($5 <= $cutoff && $3 >= $spec_cutoff){ + $check_prey=0; + for($i=1; $i<=$sig_preysn; $i++){ + if($sig_preys[$i] eq $2){ + $check_prey=1; + } + } + if($check_prey==0){ + $sig_preysn++; + $sig_preys[$sig_preysn]=$2; + } + } + } + else{ + } +} +close(IFILE); + +open(SC_FILE, ">SC_data.txt"); +open(FDR_FILE, ">FDR_data.txt"); + +for($i=1; $i<=$baitn; $i++){ + print SC_FILE "\t$bait[$i]"; + print FDR_FILE "\t$bait[$i]"; +} +print SC_FILE "\n"; +print FDR_FILE "\n"; +for($i=1; $i<=$sig_preysn; $i++){ + print SC_FILE "$sig_preys[$i]"; + print FDR_FILE "$sig_preys[$i]"; + for($j=1; $j<=$baitn; $j++){ + $krem=0; + for($k=1; $k<=$preyn[$j]; $k++){; + if($preys[$j][$k] eq $sig_preys[$i]){ + $krem=$k; + last; + } + } + if($krem != 0){ + print SC_FILE "\t$avgspec[$j][$krem]"; + print FDR_FILE "\t$fdr[$j][$krem]"; + } + else{ + print SC_FILE "\t0"; + print FDR_FILE "\t1"; + } + } + print SC_FILE "\n"; + print FDR_FILE "\n"; +} +close(SC_FILE); +close(FDR_FILE); + |
| b |
| diff -r 89783b79ef25 -r f48b1312b6dd Dotplot_Release/SaintConvert.pl --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Dotplot_Release/SaintConvert.pl Wed Mar 16 12:09:43 2016 -0400 |
| [ |
| @@ -0,0 +1,52 @@ +#!/usr/bin/perl + +# 17/12/2013 + +if($#ARGV==0){ + print "This program takes non-SaintExpress formatted data and converts it to look like it.\n"; + print "\nusage:\n $0\n-i [csv saint output file]\n\n"; + die; +} +else{ + $i=0; + while($i<=$#ARGV){ + if($ARGV[$i] eq '-i'){ + $i++; + $ifile=$ARGV[$i]; + } + else{ + die "\Incorrect program usage\n\n"; + } + $i++; + } +} + +$i=0; +$file=''; +open(IFILE,"<$ifile") || die "$ifile can't be opened: $!"; +{ local $/=undef; $file=<IFILE>; } +@lines=split /[\r\n]+/, $file; +foreach $line (@lines) { + if($line =~ /^Bait/){ + } + elsif($line =~ /^([^\t]+)\t([^\t]+)\t([^\t]+)\t([^\t]+)/){ + $bait[$i]=$1; + $prey[$i]=$2; + $spec[$i]=$3; + $fdr[$i]=$4; + $i++; + } + else{ + } +} +close(IFILE); +$line_count=$i; + +open(OFILE, ">mockSaintExpress.txt"); +print OFILE "Bait\tPrey\tPreyGene\tSpec\tSpecSum\tAvgSpec\tNumReplicates\tctrlCounts\tAvgP\tMaxP\tTopoAvgP\tTopoMaxP\tSaintScore\tFoldChange\tBFDR\tboosted_by\n"; + +for($i=0; $i<$line_count; $i++){ + print OFILE "$bait[$i]\t111\t$prey[$i]\t111\t111\t$spec[$i]\t111\t111\t111\t111\t111\t111\t111\t111\t$fdr[$i]\t111\n"; +} +close(OFILE); + |
| b |
| diff -r 89783b79ef25 -r f48b1312b6dd Dotplot_Release/Step1_data_reformating.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Dotplot_Release/Step1_data_reformating.R Wed Mar 16 12:09:43 2016 -0400 |
| [ |
| @@ -0,0 +1,41 @@ +#!/usr/bin/env Rscript + +args <- commandArgs(trailingOnly = TRUE) + +d = read.delim(args[1], header=T, sep="\t", as.is=T) + +### Select Prey interactions were at least one Bait > Probability Threshold + +preylist=unique(c(d$PreyGene[d$BFDR <= as.numeric(args[2])])) +pid = d$PreyGene %in% preylist +d = d[pid,] + +bb = unique(d$Bait) +pp = unique(d$PreyGene) + +nbait = length(bb) +nprey = length(pp) + +### Reformat the SAINToutput data into a spreadsheet +mat = matrix(0, nprey, nbait) + +n = nrow(d) +mb = match(d$Bait, bb) +mp = match(d$PreyGene, pp) + +### Using the AvgSpec for the spectral counts +for(i in 1:n) { + mat[mp[i],mb[i]] = d$AvgSpec[i] +} + +rownames(mat) = pp +colnames(mat) = bb + +outfile <- paste(c(args[3]), "matrix.txt", sep="_") +### The following file is the outcome of running this step. +write.table(mat, outfile, sep="\t", quote=F) + + + + + |
| b |
| diff -r 89783b79ef25 -r f48b1312b6dd Dotplot_Release/Step2_data_filtering.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Dotplot_Release/Step2_data_filtering.R Wed Mar 16 12:09:43 2016 -0400 |
| [ |
| @@ -0,0 +1,28 @@ +#!/usr/bin/env Rscript + +args <- commandArgs(trailingOnly = TRUE) + +d = read.delim(args[1], header=T, as.is=T) + +d2 = d +d2s = d + +ss_cutoff <- as.numeric(args[2]) +### Here I'm only going to take the preys which appeared in at least 2 baits with >args[2] counts +id = apply(d, 1, function(x) sum(x>ss_cutoff) >= 2) +id2 = apply(d, 1, function(x) sum(x>ss_cutoff) < 2) +d2 = d2[id, ] +d2s = d2s[id2, 0] +max.d2 = max(as.numeric(as.matrix(d2))) +d2 = d2 / max.d2 * 10 + +d3 = data.frame(PROT = rownames(d2), d2) + +outfile <- paste(c(args[3]), "dat", sep=".") + +### The following file is the outcome of running this step. +write.table(d3, outfile, sep="\t", quote=F, row.names=F) +### This is the final input file for nested cluster algorithm + +write.table(d2s, "singletons.txt", quote=F) + |
| b |
| diff -r 89783b79ef25 -r f48b1312b6dd Dotplot_Release/Step3_nestedcluster |
| b |
| Binary file Dotplot_Release/Step3_nestedcluster has changed |
| b |
| diff -r 89783b79ef25 -r f48b1312b6dd Dotplot_Release/Step4_biclustering.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Dotplot_Release/Step4_biclustering.R Wed Mar 16 12:09:43 2016 -0400 |
| [ |
| @@ -0,0 +1,201 @@ +#!/usr/bin/env Rscript + +args <- commandArgs(trailingOnly = TRUE) + +d = read.delim(args[1], header=T, sep="\t", as.is=T, row.names=1) + +clusters = read.delim("Clusters", header=T, sep="\t", as.is=T)[,-1] +clusters = data.frame(Bait=colnames(clusters), Cluster=as.numeric(clusters[1,])) +nested.clusters = read.delim("NestedClusters", header=F, sep="\t", as.is=T)[1:dim(d)[1],] +nested.phi = read.delim("NestedMu", header=F, sep="\t", as.is=T)[1:dim(d)[1],] +nested.phi2 = read.delim("NestedSigma2", header=F, sep="\t", as.is=T)[1:dim(d)[1],] +mcmc = read.delim("MCMCparameters", header=F, sep="\t", as.is=T) + +### distance between bait using phi (also reorder cluster names) +### report nested clusters with positive counts only +### rearrange rows and columns of the raw data matrix according to the back-tracking algorithm + +recursivePaste = function(x) { + n = length(x) + x = x[order(x)] + y = x[1] + if(n > 1) { + for(i in 2:n) y = paste(y, x[i], sep="-") + } + y +} + +calcDist = function(x, y) { + if(length(x) != length(y)) stop("different length\n") + else res = sum(abs(x-y)) + res +} + + +#clusters, nested.clusters, nested.phi, d + +bcl = clusters +pcl = nested.clusters +phi = nested.phi +phi2 = nested.phi2 +dat = d + + +## bipartite graph +make.graphlet = function(b,p,s) { + g = NULL + g$b = b + g$p = p + g$s = as.numeric(s) + g +} + +make.hub = function(b,p) { + g = NULL + g$b = b + g$p = p + g +} + +jaccard = function(x,y) { + j = length(intersect(x,y)) / length(union(x,y)) + j +} + +merge.graphlets = function(x, y) { + g = NULL + g$b = union(x$b, y$b) + g$p = union(x$p, y$p) + g$s1 = rep(0,length(g$p)) + g$s2 = rep(0,length(g$p)) + g$s1[match(x$p, g$p)] = x$s + g$s2[match(y$p, g$p)] = y$s + g$s = apply(cbind(g$s1, g$s2), 1, max) + g +} + +summarizeDP = function(bcl, pcl, phi, phi2, dat, hub.size=0.5, ...) { + pcl = as.matrix(pcl) + phi = as.matrix(phi) + phi2 = as.matrix(phi2) + dat = as.matrix(dat) + rownames(phi) = rownames(dat) + rownames(phi2) = rownames(dat) + + ubcl = unique(as.numeric(bcl$Cluster)) + n = length(ubcl) + pcl = pcl[,ubcl] + phi = phi[,ubcl] + phi2 = phi2[,ubcl] + phi[phi < 0.05] = 0 + + bcl$Cluster = match(as.numeric(bcl$Cluster), ubcl) + colnames(pcl) = colnames(phi) = colnames(phi2) = paste("CL", 1:n, sep="") + + ## remove non-reproducible mean values + nprey = dim(dat)[1]; nbait = dim(dat)[2] + preys = rownames(dat); baits = colnames(dat) + n = length(unique(bcl$Cluster)) + for(j in 1:n) { + id = c(1:nbait)[bcl$Cluster == j] + for(k in 1:nprey) { + do.it = ifelse(mean(as.numeric(dat[k,id]) > 0) <= 0.5,TRUE,FALSE) + if(do.it) { + phi[k,j] = 0 + } + } + } + + ## create bipartite graphs (graphlets) + gr = NULL + for(j in 1:n) { + id = c(1:nbait)[bcl$Cluster == j] + id2 = c(1:nprey)[phi[,j] > 0] + gr[[j]] = make.graphlet(baits[id], preys[id2], phi[id2,j]) + } + + ## intersecting preys between graphlets + gr2 = NULL + cur = 1 + for(i in 1:n) { + for(j in 1:n) { + if(i != j) { + combine = jaccard(gr[[i]]$p, gr[[j]]$p) >= 0.75 + if(combine) { + gr2[[cur]] = merge.graphlets(gr[[i]], gr[[j]]) + cur = cur + 1 + } + } + } + } + + old.phi = phi + phi = phi[, bcl$Cluster] + phi2 = phi2[, bcl$Cluster] + ## find hub preys + proceed = apply(old.phi, 1, function(x) sum(x>0) >= 2) + h = NULL + cur = 1 + for(k in 1:nprey) { + if(proceed[k]) { + id = as.numeric(phi[k,]) > 0 + if(mean(id) >= hub.size) { + h[[cur]] = make.hub(baits[id], preys[k]) + cur = cur + 1 + } + } + } + nhub = cur - 1 + + res = list(data=dat, baitCL=bcl, phi=phi, phi2=phi2, gr = gr, gr2 = gr2, hub = h) + res +} + +res = summarizeDP(clusters, nested.clusters, nested.phi, nested.phi2, d) + +write.table(res$baitCL[order(res$baitCL$Cluster),], "baitClusters", sep="\t", quote=F, row.names=F) +write.table(res$data, "clusteredData", sep="\t", quote=F) + +##### SOFT +library(gplots) +tmpd = res$data +tmpm = res$phi +colnames(tmpm) = paste(colnames(res$data), colnames(tmpm)) + +pdf("estimated.pdf", height=25, width=8) +my.hclust<-hclust(dist(tmpd)) +my.dend<-as.dendrogram(my.hclust) +tmp.res = heatmap.2(tmpm, Rowv=my.dend, Colv=T, trace="n", col=rev(heat.colors(10)), breaks=seq(0,.5,by=0.05), margins=c(10,10), keysize=0.8, cexRow=0.4) +#tmp.res = heatmap.2(tmpm, Rowv=T, Colv=T, trace="n", col=rev(heat.colors(10)), breaks=seq(0,.5,by=0.05), margins=c(10,10), keysize=0.8, cexRow=0.4) +tmpd = tmpd[rev(tmp.res$rowInd),tmp.res$colInd] +write.table(tmpd, "clustered_matrix.txt", sep="\t", quote=F) +heatmap.2(tmpd, Rowv=F, Colv=F, trace="n", col=rev(heat.colors(10)), breaks=seq(0,.5,by=0.05), margins=c(10,10), keysize=0.8, cexRow=0.4) +dev.off() + + +### Statistical Plots +dd = dist(1-cor((res$phi), method="pearson")) +dend = as.dendrogram(hclust(dd, "ave")) +#plot(dend) + +pdf("bait2bait.pdf") +tmp = res$phi +colnames(tmp) = paste(colnames(res$phi), res$baitCL$Bait, sep="_") + +###dd = cor(tmp[,-26]) ### This line is only for Chris' data (one bait has all zeros in the estimated parameters) +dd = cor(tmp) ### This line is only for Chris' data (one bait has all zeros in the estimated parameters) + +write.table(dd, "bait2bait_matrix.txt", sep="\t", quote=F) +heatmap.2(as.matrix(dd), trace="n", breaks=seq(-1,1,by=0.1), col=(greenred(20)), cexRow=0.7, cexCol=0.7) +dev.off() + +tmp = mcmc[,2] +ymax = max(tmp) +ymin = min(tmp) +pdf("stats.pdf", height=12, width=12) + +plot(mcmc[mcmc[,4]=="G",3], type="s", xlab="Iterations", ylab="Number of Clusters", main="") +plot(mcmc[,2], type="l", xlab="Iterations", ylab="Log-Likelihood", main="", ylim=c(ymin,ymax)) + +dev.off() + |
| b |
| diff -r 89783b79ef25 -r f48b1312b6dd Dotplot_Release/biclust.tar.gz |
| b |
| Binary file Dotplot_Release/biclust.tar.gz has changed |
| b |
| diff -r 89783b79ef25 -r f48b1312b6dd Dotplot_Release/biclust_param.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Dotplot_Release/biclust_param.txt Wed Mar 16 12:09:43 2016 -0400 |
| b |
| @@ -0,0 +1,12 @@ +np 10 +nb 100 +a 1.0 +b 1.0 +lambda 0.0 +nu 25.0 +alpha 1.0 +rho 1.0 +gamma 1.0 +nburn 5000 +niter 10000 + |
| b |
| diff -r 89783b79ef25 -r f48b1312b6dd Dotplot_Release/dotplot.bash --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Dotplot_Release/dotplot.bash Wed Mar 16 12:09:43 2016 -0400 |
| [ |
| b'@@ -0,0 +1,252 @@\n+#!/bin/bash\n+#SCRIPT=$(readlink -e $0)\n+#SCRIPTPATH=`dirname $SCRIPT`\n+pushd `dirname $0` > /dev/null\n+SCRIPTPATH=`pwd`\n+popd > /dev/null\n+\n+usage() { printf "Usage: $0 \n+[-f <saint_file_name.txt>]\n+[-i <0 for SaintExpress format, 1 for other>]\n+[-c <clustering to perform. Options: b (biclustering), h (hierarchical), n (none, requires input text files for bait and prey ordering; see options -b and -p)>]\n+[-n <clustering type to be performed if option -c is set to \\"h\\">]\n+[-d <distance metric to use if option -c is set to \\"h\\">]\n+[-b <list of bait proteins in display order (see option -c n)>]\n+[-p <list of prey proteins in display order (see option -c n). Set this to \\"all\\" if you want to include all preys and cluster them>]\n+[-s <primary FDR cutoff [0-1, recommended=0.01]>]\n+[-t <secondary FDR cutoff [must be less than the primary, recommended=0.025]>\n+[-x <spectral count minimum. Only preys with >= this will be used]>\n+[-m <maximum spectral count>]\n+[-N <normalization, 0 for no (default), 1 for yes, 2 for normalization based on significant preys counts (prey FDR <= option -t)>]\n+[-C <FDR cutoff for normalization if using option -N 2 (deafult is -t)>]\\n"\n+1>&2; exit 1; }\n+\n+N=0\n+n="ward"\n+d="canberra"\n+x=0\n+i=0\n+while getopts ":f:i:s:t:x:m:c:n:d:b:p:N:C:" o; do\n+ case "${o}" in\n+ f)\n+ f=${OPTARG}\n+ ;;\n+ i)\n+\t i=${OPTARG}\n+ ;;\n+ s)\n+ s=${OPTARG}\n+ ;;\n+\tt)\n+ t=${OPTARG}\n+ ;;\n+ x)\n+\t x=${OPTARG}\n+ ;;\n+\tm)\n+ m=${OPTARG}\n+ ;;\n+\tc)\n+ c=${OPTARG}\n+\t ;;\n+\tn)\n+\t n=${OPTARG}\n+\t ;;\n+\td)\n+\t d=${OPTARG}\n+\t ;;\n+\tb)\n+ b=${OPTARG}\n+\t ;;\n+\tp)\n+\t p=${OPTARG}\n+\t ;;\n+\tN)\n+\t N=${OPTARG}\n+\t ;;\n+\tC)\n+\t C=${OPTARG}\n+\t ;;\n+ *)\n+ usage\n+ ;;\n+ esac\n+done\n+shift $((OPTIND-1))\n+\n+filename=${f%%.*}\n+echo "Saint input file = ${f}"\n+echo "Primary FDR cutoff = ${s}"\n+echo "Secondary FDR cutoff for dotplot = ${t}"\n+echo "Minimum spectral count for significant preys = ${x}"\n+echo "Maximum spectral count for dot plot = ${m}"\n+\n+if [ -z "${f}" ] || [ -z "${s}" ] || [ -z "${t}" ] || [ -z "${m}" ] || [ -z "${c}" ]; then\n+ usage\n+fi\n+\n+if [ "${i}" == 1 ]; then\n+\t$SCRIPTPATH/SaintConvert.pl -i ${f}\n+\tf="mockSaintExpress.txt"\n+fi\n+\n+if [ "${x}" -ge "${m}" ]; then\n+\techo "spectral count minimum (${x}) cannot be greater than or equal to the maximum (${m})"\n+\texit 1;\n+elif [ "${x}" -lt 0 ]; then\n+\techo "spectral count minimum (${x}) cannot be less than 0. Setting to 0 and continuing"\n+\tx=0\n+fi\n+\n+###Check for normalization\n+\n+if [ "${N}" == 1 ]; then\n+\tprintf "\\nNormalization is being performed\\n"\n+\t$SCRIPTPATH/Normalization.R ${f}\n+\tf="norm_saint.txt"\n+elif [ "${N}" == 2 ]; then\n+\tprintf "\\nNormalization is being performed\\n"\n+\tif [ -z "${C}" ]; then\n+\t\tC=${t}\n+\tfi\n+\t$SCRIPTPATH/Normalization_sigpreys.R ${f} ${C}\n+\tf="norm_saint.txt"\n+fi\n+\n+\n+###Check for clustering etc\n+\n+if [ "${c}" == "h" ] && [ -z "${n}" ]; then\n+\tprintf "\\nHierarchial clustering was selected (-c = h), but no clustering method (-n) was chosen.\\n"\n+\tprintf "The input parameter -n must be set to one of \\"average\\", \\"centroid\\", \\"complete\\", \\"mcquitty\\",\\n"\n+\tprintf "\\"median\\", \\"single\\" or \\"ward\\". \\"ward\\" will be selected as default.\\n\\n"\n+\tn="ward"\n+elif [ "${c}" == "h" ] && [ -n "${n}" ]; then\n+\tif [ "${n}" == "average" ] || [ "${n}" == "centroid" ] || [ "${n}" == "complete" ] || [ "${n}" == "mcquitty" ] || [ "${n}" == "median" ] || [ "${n}" == "single" ] || [ "${n}" == "ward" ]; then\n+\t\tprintf "\\nHierarchical clustering (method = ${n}) will be performed\\n\\n"\n+\telse\n+\t\tprintf "\\n${n} is not a valid Hierarchical clustering method.\\n"\n+\t\tprintf "Choose one of \\"average\\", \\"centroid\\", \\"complete\\", \\"mcquitty\\", \\"median\\", \\"single\\" or \\"ward\\"\\n\\n"\n+\t\texit 1\n+\tfi\n+fi\n+\n+p_c=0\n+if [ "${c}" == "h" ] && [ -z "${d}" ]; then\n+\tprintf "'..b'${p}" ]; then\n+\tprintf "\\n\\"No Clustering\\" option was selected (-c = n), but no prey list was included (option -p).\\n"\n+\tprintf "Prey list must be in .txt formart.\\n\\n"\n+\texit 1\n+elif [ "${c}" == "n" ] && [ "${p}" == "all" ]; then\n+\tprintf "\\n\\"No Clustering\\" option was selected (-c = n) for baits, but preys will still be clustered.\\n"\n+\tprintf "using \\"ward\\" and \\"canberra\\" as defaults or options as supplied on command line.\\n\\n"\n+\tp="empty"\n+\tp_c=1\n+\tn="ward"\n+\td="canberra"\n+fi\n+\n+\n+###Check number of baits\n+\n+bait_n=$(perl $SCRIPTPATH/BaitCheck.pl -i ${f})\n+echo "Number of baits = "$bait_n\n+printf "\\n\\n"\n+\n+if [ "${c}" == "b" ] && [ $bait_n == 2 ]; then\n+\tprintf "\\nWarning only 2 baits are present. Biclustering will not performed.\\n"\n+\tprintf "Hierarchical clustering (method = ward) will be performed instead.\\n\\n"\n+\tc="h"\n+\tn="ward"\n+fi\n+\n+\n+###Generate plots\n+\n+if [ "${c}" == "b" ]; then\n+\tprintf "\\nBiclustering will be performed\\n\\n"\n+\t$SCRIPTPATH/Step1_data_reformating.R ${f} ${s} ${filename}\n+\t$SCRIPTPATH/Step2_data_filtering.R ${filename}_matrix.txt ${x} ${filename}\n+\tGSL_RNG_SEED=123 $SCRIPTPATH/Step3_nestedcluster ${filename}.dat $SCRIPTPATH/biclust_param.txt\n+\t$SCRIPTPATH/Step4_biclustering.R ${filename}.dat\n+\n+\t$SCRIPTPATH/SOFD.pl -i ${f} -s ${s} -x ${x}\n+\t$SCRIPTPATH/R_dotPlot.R ${s} ${t} ${m}\n+\tmkdir Output_${filename}\n+\tmkdir Output_${filename}/TempData_${filename}\n+\tmv bait_lists Output_${filename}/TempData_${filename}\n+\tmv Clusters Output_${filename}/TempData_${filename}\n+\tmv MCMCparameters Output_${filename}/TempData_${filename}\n+\tmv NestedClusters Output_${filename}/TempData_${filename}\n+\tmv NestedMu Output_${filename}/TempData_${filename}\n+\tmv NestedSigma2 Output_${filename}/TempData_${filename}\n+\tmv OPTclusters Output_${filename}/TempData_${filename}\n+\tmv ${filename}_matrix.txt Output_${filename}/TempData_${filename}\n+\tmv ${filename}.dat Output_${filename}/TempData_${filename}\n+\tmv SC_data.txt Output_${filename}/TempData_${filename}\n+\tmv FDR_data.txt Output_${filename}/TempData_${filename}\n+\tmv clustered_matrix.txt Output_${filename}/TempData_${filename}\n+\tmv singletons.txt Output_${filename}/TempData_${filename}\n+\tmv bait2bait_matrix.txt Output_${filename}/TempData_${filename}\n+\tmv baitClusters Output_${filename}/TempData_${filename}\n+\tmv clusteredData Output_${filename}/TempData_${filename}\n+\tmv dotplot.pdf Output_${filename}\n+\tmv bait2bait.pdf Output_${filename} \n+\tmv estimated.pdf Output_${filename} \n+\tmv stats.pdf Output_${filename}\n+\tcp $SCRIPTPATH/legend.pdf Output_${filename}\n+elif [ "${c}" == "h" ]; then\n+\n+\t$SCRIPTPATH/SOFD.pl -i ${f} -s ${s} -x ${x}\n+\t$SCRIPTPATH/R_dotPlot_hc.R ${s} ${t} ${m} ${n} ${d} $SCRIPTPATH\n+\n+\tmkdir Output_${filename}\n+\tmkdir Output_${filename}/TempData_${filename}\n+\tmv dotplot.pdf Output_${filename}\n+\tmv heatmap_borders.pdf Output_${filename}\n+\tmv heatmap_no_borders.pdf Output_${filename}\n+\tmv bait2bait.pdf Output_${filename}\n+\tmv SC_data.txt Output_${filename}/TempData_${filename}\n+\tmv FDR_data.txt Output_${filename}/TempData_${filename}\n+\tcp $SCRIPTPATH/legend.pdf Output_${filename}\n+elif [ "${c}" == "n" ]; then\n+\t\n+\t$SCRIPTPATH/SOFD.pl -i ${f} -s ${s} -x ${x}\n+\techo "$SCRIPTPATH/R_dotPlot_nc.R ${s} ${t} ${m} ${b} $p_c ${p} ${n} ${d} $SCRIPTPATH"\n+\t$SCRIPTPATH/R_dotPlot_nc.R ${s} ${t} ${m} ${b} $p_c ${p} ${n} ${d} $SCRIPTPATH\n+\n+\tmkdir Output_${filename}\n+\tmkdir Output_${filename}/TempData_${filename}\n+\tmv dotplot.pdf Output_${filename}\n+\tmv heatmap_borders.pdf Output_${filename}\n+\tmv heatmap_no_borders.pdf Output_${filename}\n+\tmv SC_data.txt Output_${filename}/TempData_${filename}\n+\tmv FDR_data.txt Output_${filename}/TempData_${filename}\n+\tcp $SCRIPTPATH/legend.pdf Output_${filename}\n+else\n+\tprintf -- "-c must be one of [b, h, n]: b (biclustering), h (hierarchical), n (none, requires input text files for bait and prey ordering>\\n"\n+\texit 1;\n+fi\n+\n+if [ "${N}" == "1" ] || [ "${N}" == "2" ]; then\n+\tmv norm_saint.txt Output_${filename}/TempData_${filename}\n+fi\n+\n' |
| b |
| diff -r 89783b79ef25 -r f48b1312b6dd Dotplot_Release/pheatmap_j.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Dotplot_Release/pheatmap_j.R Wed Mar 16 12:09:43 2016 -0400 |
| [ |
| b'@@ -0,0 +1,719 @@\n+lo = function(rown, coln, nrow, ncol, cellheight = NA, cellwidth = NA, treeheight_col, treeheight_row, legend, annotation, annotation_colors, annotation_legend, main, fontsize, fontsize_row, fontsize_col, ...){\n+\t# Get height of colnames and length of rownames\n+\tif(!is.null(coln[1])){\n+\t\tlongest_coln = which.max(strwidth(coln, units = \'in\'))\n+\t\tgp = list(fontsize = fontsize_col, ...)\n+\t\tcoln_height = unit(1, "grobheight", textGrob(coln[longest_coln], rot = 90, gp = do.call(gpar, gp))) + unit(5, "bigpts")\n+\t}\n+\telse{\n+\t\tcoln_height = unit(5, "bigpts")\n+\t}\n+\t\n+\tif(!is.null(rown[1])){\n+\t\tlongest_rown = which.max(strwidth(rown, units = \'in\'))\n+\t\tgp = list(fontsize = fontsize_row, ...)\n+\t\trown_width = unit(1, "grobwidth", textGrob(rown[longest_rown], gp = do.call(gpar, gp))) + unit(10, "bigpts")\n+\t}\n+\telse{\n+\t\trown_width = unit(5, "bigpts")\n+\t}\n+\t\n+\tgp = list(fontsize = fontsize, ...)\n+\t# Legend position\n+\tif(!is.na(legend[1])){\n+\t\tlongest_break = which.max(nchar(names(legend)))\n+\t\tlongest_break = unit(1.1, "grobwidth", textGrob(as.character(names(legend))[longest_break], gp = do.call(gpar, gp)))\n+\t\ttitle_length = unit(1.1, "grobwidth", textGrob("Scale", gp = gpar(fontface = "bold", ...)))\n+\t\tlegend_width = unit(12, "bigpts") + longest_break * 1.2\n+\t\tlegend_width = max(title_length, legend_width)\n+\t}\n+\telse{\n+\t\tlegend_width = unit(0, "bigpts")\n+\t}\n+\t\n+\t# Set main title height\n+\tif(is.na(main)){\n+\t\tmain_height = unit(0, "npc")\n+\t}\n+\telse{\n+\t\tmain_height = unit(1.5, "grobheight", textGrob(main, gp = gpar(fontsize = 1.3 * fontsize, ...)))\n+\t}\n+\t\n+\t# Column annotations\n+\tif(!is.na(annotation[[1]][1])){\n+\t\t# Column annotation height \n+\t\tannot_height = unit(ncol(annotation) * (8 + 2) + 2, "bigpts")\n+\t\t# Width of the correponding legend\n+\t\tlongest_ann = which.max(nchar(as.matrix(annotation)))\n+\t\tannot_legend_width = unit(1.2, "grobwidth", textGrob(as.matrix(annotation)[longest_ann], gp = gpar(...))) + unit(12, "bigpts")\n+\t\tif(!annotation_legend){\n+\t\t\tannot_legend_width = unit(0, "npc")\n+\t\t}\n+\t}\n+\telse{\n+\t\tannot_height = unit(0, "bigpts")\n+\t\tannot_legend_width = unit(0, "bigpts")\n+\t}\n+\t\n+\t# Tree height\n+\ttreeheight_col = unit(treeheight_col, "bigpts") + unit(5, "bigpts")\n+\ttreeheight_row = unit(treeheight_row, "bigpts") + unit(5, "bigpts") \n+\t\n+\t# Set cell sizes\n+\tif(is.na(cellwidth)){\n+\t\tmatwidth = unit(1, "npc") - rown_width - legend_width - treeheight_row - annot_legend_width\n+\t}\n+\telse{\n+\t\tmatwidth = unit(cellwidth * ncol, "bigpts")\n+\t}\n+\t\n+\tif(is.na(cellheight)){\n+\t\tmatheight = unit(1, "npc") - main_height - coln_height - treeheight_col - annot_height\n+\t}\n+\telse{\n+\t\tmatheight = unit(cellheight * nrow, "bigpts")\n+\t}\t\n+\t\n+\t\n+\t# Produce layout()\n+\tpushViewport(viewport(layout = grid.layout(nrow = 5, ncol = 5, widths = unit.c(treeheight_row, matwidth, rown_width, legend_width, annot_legend_width), heights = unit.c(main_height, treeheight_col, annot_height, matheight, coln_height)), gp = do.call(gpar, gp)))\n+\t\n+\t# Get cell dimensions\n+\tpushViewport(vplayout(4, 2))\n+\tcellwidth = convertWidth(unit(0:1, "npc"), "bigpts", valueOnly = T)[2] / ncol\n+\tcellheight = convertHeight(unit(0:1, "npc"), "bigpts", valueOnly = T)[2] / nrow\n+\tupViewport()\n+\t\n+\t# Return minimal cell dimension in bigpts to decide if borders are drawn\n+\tmindim = min(cellwidth, cellheight) \n+\treturn(mindim)\n+}\n+\n+draw_dendrogram = function(hc, horizontal = T){\n+\th = hc$height / max(hc$height) / 1.05\n+\tm = hc$merge\n+\to = hc$order\n+\tn = length(o)\n+\n+\tm[m > 0] = n + m[m > 0] \n+\tm[m < 0] = abs(m[m < 0])\n+\n+\tdist = matrix(0, nrow = 2 * n - 1, ncol = 2, dimnames = list(NULL, c("x", "y"))) \n+\tdist[1:n, 1] = 1 / n / 2 + (1 / n) * (match(1:n, o) - 1)\n+\n+\tfor(i in 1:nrow(m)){\n+\t\tdist[n + i, 1] = (dist[m[i, 1], 1] + dist[m[i, 2], 1]) / 2\n+\t\tdist[n + i, 2] = h[i]\n+\t}\n+\t\n+\tdraw_connection = function(x1, x2, y1, y2, y){\n+\t\tgrid.lines(x = c(x1, x1), y = c(y1, y))\n+\t\tgrid.lines(x = c(x2, x2), y = c(y2, y))\n+\t\tgrid.lines(x = c(x1, x2), y = c(y, y))\n+\t}\n+\t\n+\tif(horizontal){\n+'..b'ing_distance_cols = dcols)\n+#\'\n+#\' @export\n+pheatmap_j = function(mat, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100), kmeans_k = NA, breaks = NA, border_color = "grey60", border_width = 1, cellwidth = NA, cellheight = NA, scale = "none", cluster_rows = TRUE, cluster_cols = TRUE, clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean", clustering_method = "complete", treeheight_row = ifelse(cluster_rows, 50, 0), treeheight_col = ifelse(cluster_cols, 50, 0), legend = TRUE, legend_breaks = NA, legend_labels = NA, annotation = NA, annotation_colors = NA, annotation_legend = TRUE, drop_levels = TRUE, show_rownames = T, show_colnames = T, main = NA, fontsize = 10, fontsize_row = fontsize, fontsize_col = fontsize, display_numbers = F, number_format = "%.2f", fontsize_number = 0.8 * fontsize, filename = NA, width = NA, height = NA, ...){\n+\t\n+\t# Preprocess matrix\n+\tmat = as.matrix(mat)\n+\tif(scale != "none"){\n+\t\tmat = scale_mat(mat, scale)\n+\t\tif(is.na(breaks)){\n+\t\t\tbreaks = generate_breaks(mat, length(color), center = T)\n+\t\t}\n+\t}\n+\t\n+\t\n+\t# Kmeans\n+\tif(!is.na(kmeans_k)){\n+\t\t# Cluster data\n+\t\tkm = kmeans(mat, kmeans_k, iter.max = 100)\n+\t\tmat = km$centers\n+\n+\t\t# Compose rownames\n+\t\tt = table(km$cluster)\n+\t\trownames(mat) = sprintf("cl%s_size_%d", names(t), t)\n+\t}\n+\telse{\n+\t\tkm = NA\n+\t}\n+\t\n+\t# Do clustering\n+\tif(cluster_rows){\n+\t\ttree_row = cluster_mat(mat, distance = clustering_distance_rows, method = clustering_method)\n+\t\tmat = mat[tree_row$order, , drop = FALSE]\n+\t}\n+\telse{\n+\t\ttree_row = NA\n+\t\ttreeheight_row = 0\n+\t}\n+\t\n+\tif(cluster_cols){\n+\t\ttree_col = cluster_mat(t(mat), distance = clustering_distance_cols, method = clustering_method)\n+\t\tmat = mat[, tree_col$order, drop = FALSE]\n+\t}\n+\telse{\n+\t\ttree_col = NA\n+\t\ttreeheight_col = 0\n+\t}\n+\t\n+\t# Format numbers to be displayed in cells \n+\tif(display_numbers){\n+\t\tfmat = matrix(sprintf(number_format, mat), nrow = nrow(mat), ncol = ncol(mat))\n+\t\tattr(fmat, "draw") = TRUE\n+\t}\n+\telse{\n+\t\tfmat = matrix(NA, nrow = nrow(mat), ncol = ncol(mat))\n+\t\tattr(fmat, "draw") = FALSE\n+\t}\n+\t\n+\t\n+\t# Colors and scales\n+\tif(!is.na(legend_breaks[1]) & !is.na(legend_labels[1])){\n+\t\tif(length(legend_breaks) != length(legend_labels)){\n+\t\t\tstop("Lengths of legend_breaks and legend_labels must be the same")\n+\t\t}\n+\t}\n+\t\n+\t\n+\tif(is.na(breaks[1])){\n+ breaks = generate_breaks(as.vector(mat), length(color))\n+ }\n+ if (legend & is.na(legend_breaks[1])) {\n+ legend = grid.pretty(range(as.vector(breaks)))\n+\t\t\tnames(legend) = legend\n+ }\n+\telse if(legend & !is.na(legend_breaks[1])){\n+\t\tlegend = legend_breaks[legend_breaks >= min(breaks) & legend_breaks <= max(breaks)]\n+\t\t\n+\t\tif(!is.na(legend_labels[1])){\n+\t\t\tlegend_labels = legend_labels[legend_breaks >= min(breaks) & legend_breaks <= max(breaks)]\n+\t\t\tnames(legend) = legend_labels\n+\t\t}\n+\t\telse{\n+\t\t\tnames(legend) = legend\n+\t\t}\n+\t}\n+ else {\n+ legend = NA\n+ }\n+\tmat = scale_colours(mat, col = color, breaks = breaks)\n+\t\n+\t# Preparing annotation colors\n+\tif(!is.na(annotation[[1]][1])){\n+\t\tannotation = annotation[colnames(mat), , drop = F]\n+\t\tannotation_colors = generate_annotation_colours(annotation, annotation_colors, drop = drop_levels)\n+\t}\n+\t\n+\tif(!show_rownames){\n+\t\trownames(mat) = NULL\n+\t}\n+\t\n+\tif(!show_colnames){\n+\t\tcolnames(mat) = NULL\n+\t}\n+\t\n+\t# Draw heatmap\n+\theatmap_motor(mat, border_color = border_color, border_width = border_width, cellwidth = cellwidth, cellheight = cellheight, treeheight_col = treeheight_col, treeheight_row = treeheight_row, tree_col = tree_col, tree_row = tree_row, filename = filename, width = width, height = height, breaks = breaks, color = color, legend = legend, annotation = annotation, annotation_colors = annotation_colors, annotation_legend = annotation_legend, main = main, fontsize = fontsize, fontsize_row = fontsize_row, fontsize_col = fontsize_col, fmat = fmat, fontsize_number = fontsize_number, ...)\n+\t\n+\tinvisible(list(tree_row = tree_row, tree_col = tree_col, kmeans = km))\n+}\n+\n+\n' |
| b |
| diff -r 89783b79ef25 -r f48b1312b6dd Dotplot_Release/test_files/SC_SAINT_list.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Dotplot_Release/test_files/SC_SAINT_list.txt Wed Mar 16 12:09:43 2016 -0400 |
| b |
| b'@@ -0,0 +1,473 @@\n+Bait\tPrey\tPreyGene\tSpec\tSpecSum\tAvgSpec\tNumReplicates\tctrlCounts\tAvgP\tMaxP\tTopoAvgP\tTopoMaxP\tSaintScore\tlogOddsScore\tFoldChange\tBFDR\tboosted_by\n+WT_EGFR\tEGFR_HUMAN\tEGFR\t67|52|45|55\t219\t54.75\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t68.87\t547.50\t0.00\t\n+WT_EGFR\tERBB2_HUMAN\tERBB2\t4|5|3|5\t17\t4.25\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t6.28\t42.50\t0.00\t\n+WT_EGFR\tP85B_HUMAN\tPIK3R2\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+WT_EGFR\tP55G_HUMAN\tPIK3R3\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+WT_EGFR\tP85A_HUMAN\tPIK3R1\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+WT_EGFR\tK2C1_HUMAN\tKRT1\t3|4|6|5\t18\t4.50\t4\t10|10|7|7\t0.00\t0.00\t0.00\t0.00\t0.00\t-27.06\t0.53\t0.51\t\n+WT_EGFR\tK1C10_HUMAN\tKRT10\t2|0|3|3\t8\t2.00\t4\t1|2|4|3\t0.01\t0.02\t0.01\t0.02\t0.01\t-9.56\t0.80\t0.49\t\n+WT_EGFR\tK1C16_HUMAN\tKRT16\t0|0|2|1\t3\t0.75\t4\t1|1|2|2\t0.03\t0.12\t0.03\t0.12\t0.03\t-5.35\t0.50\t0.47\t\n+WT_EGFR\tGRB2_HUMAN\tGRB2\t2|0|2|2\t6\t1.50\t4\t0|0|0|0\t0.74\t0.99\t0.74\t0.99\t0.74\t-0.11\t15.00\t0.05\t\n+WT_EGFR\tGRP78_HUMAN\tHSPA5\t16|14|11|12\t53\t13.25\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t19.16\t132.50\t0.00\t\n+WT_EGFR\tK22E_HUMAN\tKRT2\t2|3|6|4\t15\t3.75\t4\t4|4|1|1\t0.15\t0.50\t0.15\t0.50\t0.15\t-5.61\t1.50\t0.44\t\n+WT_EGFR\tK2C8_HUMAN\tKRT8\t1|1|2|2\t6\t1.50\t4\t0|0|0|0\t0.49\t0.99\t0.49\t0.99\t0.49\t2.40\t15.00\t0.19\t\n+WT_EGFR\tK2C5_HUMAN\tKRT5\t1|1|3|2\t7\t1.75\t4\t1|1|2|0\t0.29\t0.74\t0.29\t0.74\t0.29\t-1.74\t1.75\t0.26\t\n+WT_EGFR\tK2C4_HUMAN\tKRT4\t0|0|0|0\t0\t0.00\t4\t0|1|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.65\t0.00\t0.51\t\n+WT_EGFR\tK2C6B_HUMAN\tKRT6B\t1|2|4|4\t11\t2.75\t4\t2|2|2|1\t0.25\t0.47\t0.25\t0.47\t0.25\t-4.44\t1.57\t0.42\t\n+WT_EGFR\tK2C7_HUMAN\tKRT7\t0|0|0|2\t2\t0.50\t4\t0|0|0|0\t0.25\t0.99\t0.25\t0.99\t0.25\t-0.11\t5.00\t0.28\t\n+WT_EGFR\tHSP7C_HUMAN\tHSPA8\t15|10|12|11\t48\t12.00\t4\t0|0|1|1\t1.00\t1.00\t1.00\t1.00\t1.00\t12.83\t24.00\t0.00\t\n+WT_EGFR\tPK3CA_HUMAN\tPIK3CA\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+WT_EGFR\tERBB3_HUMAN\tERBB3\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+WT_EGFR\tADT2_HUMAN\tSLC25A5\t5|3|5|5\t18\t4.50\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t6.28\t45.00\t0.00\t\n+WT_EGFR\tADT3_HUMAN\tSLC25A6\t5|4|5|5\t19\t4.75\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t8.02\t47.50\t0.00\t\n+WT_EGFR\tK1C9_HUMAN\tKRT9\t1|1|1|0\t3\t0.75\t4\t0|0|1|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.65\t3.00\t0.51\t\n+WT_EGFR\tTBA1A_HUMAN\tTUBA1A\t5|3|5|3\t16\t4.00\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t6.28\t40.00\t0.00\t\n+WT_EGFR\tTBA4A_HUMAN\tTUBA4A\t3|2|3|1\t9\t2.25\t4\t0|0|0|0\t0.75\t1.00\t0.75\t1.00\t0.75\t2.40\t22.50\t0.02\t\n+WT_EGFR\tARHG5_HUMAN\tARHGEF5\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+WT_EGFR\tPK3CB_HUMAN\tPIK3CB\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+WT_EGFR\tIRS1_HUMAN\tIRS1\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+WT_EGFR\tTBB5_HUMAN\tTUBB\t8|6|5|2\t21\t5.25\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t4.43\t52.50\t0.00\t\n+WT_EGFR\tTBB3_HUMAN\tTUBB3\t2|2|3|1\t8\t2.00\t4\t0|0|0|0\t0.74\t1.00\t0.74\t1.00\t0.74\t2.40\t20.00\t0.05\t\n+WT_EGFR\tHS90A_HUMAN\tHSP90AA1\t3|3|1|2\t9\t2.25\t4\t0|0|0|0\t0.75\t1.00\t0.75\t1.00\t0.75\t2.40\t22.50\t0.02\t\n+WT_EGFR\tHS90B_HUMAN\tHSP90AB1\t4|2|1|2\t9\t2.25\t4\t0|0|0|0\t0.74\t1.00\t0.74\t1.00\t0.74\t2.40\t22.50\t0.04\t\n+WT_EGFR\t1433E_HUMAN\tYWHAE\t1|0|0|0\t1\t0.25\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t2.50\t0.51\t\n+WT_EGFR\t1433T_HUMAN\tYWHAQ\t1|0|0|0\t1\t0.25\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t2.50\t0.51\t\n+WT_EGFR\tLPPRC_HUMAN\tLRPPRC\t1|3|3|3\t10\t2.50\t4\t0|0|0|0\t0.75\t1.00\t0.75\t1.00\t0.75\t2.40\t25.00\t0.00\t\n+WT_EGFR\tERRFI_HUMAN\tERRFI1\t1|2|1|1\t5\t1.25\t4\t0|0|0|0\t0.25\t0.99\t0.25\t0.99\t0.25\t2.40\t12.50\t0.28\t\n+WT_EGFR\tAP2A1_HUMAN\tAP2A1\t1|1|1|1\t4\t1.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t2.40\t10.00\t0.51\t\n+WT_EGFR\tGRB7_HUMAN\tGRB7\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+WT_EGFR\tSTAT3_HUMAN\tSTAT3\t4|5|3|3\t15\t3.75\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t6.28\t37.50\t0.00\t\n+WT_EGFR\tANXA2_HUMAN\tANXA2\t3|4|0|1\t8\t2.00\t4\t0|0|0|0\t0.50\t1.00\t0.50\t1.00\t0.50\t-0.11\t20.00\t0.10\t\n+WT_EGFR\tPTPRA_HUMAN\tPTPRA\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+WT_EGFR\tC1QBP_HUMAN\tC1QBP\t0|0|0|0\t0\t0.00\t4\t7|'..b'\t0|0|1|1\t0.00\t0.00\t0.00\t0.00\t0.00\t-1.55\t1.00\t0.51\t\n+ER_P85B\tPK3CA_HUMAN\tPIK3CA\t3|3|6|6\t18\t4.50\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t6.28\t45.00\t0.00\t\n+ER_P85B\tERBB3_HUMAN\tERBB3\t1|0|2|1\t4\t1.00\t4\t0|0|0|0\t0.25\t0.99\t0.25\t0.99\t0.25\t-0.11\t10.00\t0.28\t\n+ER_P85B\tADT2_HUMAN\tSLC25A5\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tADT3_HUMAN\tSLC25A6\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tK1C9_HUMAN\tKRT9\t1|0|1|0\t2\t0.50\t4\t0|0|1|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.65\t2.00\t0.51\t\n+ER_P85B\tTBA1A_HUMAN\tTUBA1A\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tTBA4A_HUMAN\tTUBA4A\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tARHG5_HUMAN\tARHGEF5\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tPK3CB_HUMAN\tPIK3CB\t1|1|4|2\t8\t2.00\t4\t0|0|0|0\t0.50\t1.00\t0.50\t1.00\t0.50\t2.40\t20.00\t0.14\t\n+ER_P85B\tIRS1_HUMAN\tIRS1\t2|3|3|4\t12\t3.00\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t4.43\t30.00\t0.00\t\n+ER_P85B\tTBB5_HUMAN\tTUBB\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tTBB3_HUMAN\tTUBB3\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tHS90A_HUMAN\tHSP90AA1\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tHS90B_HUMAN\tHSP90AB1\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\t1433E_HUMAN\tYWHAE\t0|0|1|1\t2\t0.50\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t5.00\t0.51\t\n+ER_P85B\t1433T_HUMAN\tYWHAQ\t0|0|1|1\t2\t0.50\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t5.00\t0.51\t\n+ER_P85B\tLPPRC_HUMAN\tLRPPRC\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tERRFI_HUMAN\tERRFI1\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tAP2A1_HUMAN\tAP2A1\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tGRB7_HUMAN\tGRB7\t1|1|1|0\t3\t0.75\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t7.50\t0.51\t\n+ER_P85B\tSTAT3_HUMAN\tSTAT3\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tANXA2_HUMAN\tANXA2\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tPTPRA_HUMAN\tPTPRA\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tC1QBP_HUMAN\tC1QBP\t0|0|0|0\t0\t0.00\t4\t7|5|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-11.03\t0.00\t0.51\t\n+ER_P85B\tSHC1_HUMAN\tSHC1\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tPTN11_HUMAN\tPTPN11\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tPON2_HUMAN\tPON2\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tMET_HUMAN\tMET\t0|0|1|1\t2\t0.50\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t5.00\t0.51\t\n+ER_P85B\tCRKL_HUMAN\tCRKL\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tCBL_HUMAN\tCBL\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tPHLA2_HUMAN\tPHLDA2\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tRT27_HUMAN\tMRPS27\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tM2OM_HUMAN\tSLC25A11\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tSTAT1_HUMAN\tSTAT1\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tSLIRP_HUMAN\tSLIRP\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tPK3CD_HUMAN\tPIK3CD\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tGRP75_HUMAN\tHSPA9\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tAIFM1_HUMAN\tAIFM1\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tPHB_HUMAN\tPHB\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tHNRPK_HUMAN\tHNRNPK\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tTMM33_HUMAN\tTMEM33\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n+ER_P85B\tRT34_HUMAN\tMRPS34\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n' |
| b |
| diff -r 89783b79ef25 -r f48b1312b6dd prohits_dotplot_generator/test_files/SC_SAINT_list.txt --- a/prohits_dotplot_generator/test_files/SC_SAINT_list.txt Wed Mar 16 12:08:41 2016 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| b'@@ -1,473 +0,0 @@\n-Bait\tPrey\tPreyGene\tSpec\tSpecSum\tAvgSpec\tNumReplicates\tctrlCounts\tAvgP\tMaxP\tTopoAvgP\tTopoMaxP\tSaintScore\tlogOddsScore\tFoldChange\tBFDR\tboosted_by\n-WT_EGFR\tEGFR_HUMAN\tEGFR\t67|52|45|55\t219\t54.75\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t68.87\t547.50\t0.00\t\n-WT_EGFR\tERBB2_HUMAN\tERBB2\t4|5|3|5\t17\t4.25\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t6.28\t42.50\t0.00\t\n-WT_EGFR\tP85B_HUMAN\tPIK3R2\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-WT_EGFR\tP55G_HUMAN\tPIK3R3\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-WT_EGFR\tP85A_HUMAN\tPIK3R1\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-WT_EGFR\tK2C1_HUMAN\tKRT1\t3|4|6|5\t18\t4.50\t4\t10|10|7|7\t0.00\t0.00\t0.00\t0.00\t0.00\t-27.06\t0.53\t0.51\t\n-WT_EGFR\tK1C10_HUMAN\tKRT10\t2|0|3|3\t8\t2.00\t4\t1|2|4|3\t0.01\t0.02\t0.01\t0.02\t0.01\t-9.56\t0.80\t0.49\t\n-WT_EGFR\tK1C16_HUMAN\tKRT16\t0|0|2|1\t3\t0.75\t4\t1|1|2|2\t0.03\t0.12\t0.03\t0.12\t0.03\t-5.35\t0.50\t0.47\t\n-WT_EGFR\tGRB2_HUMAN\tGRB2\t2|0|2|2\t6\t1.50\t4\t0|0|0|0\t0.74\t0.99\t0.74\t0.99\t0.74\t-0.11\t15.00\t0.05\t\n-WT_EGFR\tGRP78_HUMAN\tHSPA5\t16|14|11|12\t53\t13.25\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t19.16\t132.50\t0.00\t\n-WT_EGFR\tK22E_HUMAN\tKRT2\t2|3|6|4\t15\t3.75\t4\t4|4|1|1\t0.15\t0.50\t0.15\t0.50\t0.15\t-5.61\t1.50\t0.44\t\n-WT_EGFR\tK2C8_HUMAN\tKRT8\t1|1|2|2\t6\t1.50\t4\t0|0|0|0\t0.49\t0.99\t0.49\t0.99\t0.49\t2.40\t15.00\t0.19\t\n-WT_EGFR\tK2C5_HUMAN\tKRT5\t1|1|3|2\t7\t1.75\t4\t1|1|2|0\t0.29\t0.74\t0.29\t0.74\t0.29\t-1.74\t1.75\t0.26\t\n-WT_EGFR\tK2C4_HUMAN\tKRT4\t0|0|0|0\t0\t0.00\t4\t0|1|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.65\t0.00\t0.51\t\n-WT_EGFR\tK2C6B_HUMAN\tKRT6B\t1|2|4|4\t11\t2.75\t4\t2|2|2|1\t0.25\t0.47\t0.25\t0.47\t0.25\t-4.44\t1.57\t0.42\t\n-WT_EGFR\tK2C7_HUMAN\tKRT7\t0|0|0|2\t2\t0.50\t4\t0|0|0|0\t0.25\t0.99\t0.25\t0.99\t0.25\t-0.11\t5.00\t0.28\t\n-WT_EGFR\tHSP7C_HUMAN\tHSPA8\t15|10|12|11\t48\t12.00\t4\t0|0|1|1\t1.00\t1.00\t1.00\t1.00\t1.00\t12.83\t24.00\t0.00\t\n-WT_EGFR\tPK3CA_HUMAN\tPIK3CA\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-WT_EGFR\tERBB3_HUMAN\tERBB3\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-WT_EGFR\tADT2_HUMAN\tSLC25A5\t5|3|5|5\t18\t4.50\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t6.28\t45.00\t0.00\t\n-WT_EGFR\tADT3_HUMAN\tSLC25A6\t5|4|5|5\t19\t4.75\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t8.02\t47.50\t0.00\t\n-WT_EGFR\tK1C9_HUMAN\tKRT9\t1|1|1|0\t3\t0.75\t4\t0|0|1|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.65\t3.00\t0.51\t\n-WT_EGFR\tTBA1A_HUMAN\tTUBA1A\t5|3|5|3\t16\t4.00\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t6.28\t40.00\t0.00\t\n-WT_EGFR\tTBA4A_HUMAN\tTUBA4A\t3|2|3|1\t9\t2.25\t4\t0|0|0|0\t0.75\t1.00\t0.75\t1.00\t0.75\t2.40\t22.50\t0.02\t\n-WT_EGFR\tARHG5_HUMAN\tARHGEF5\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-WT_EGFR\tPK3CB_HUMAN\tPIK3CB\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-WT_EGFR\tIRS1_HUMAN\tIRS1\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-WT_EGFR\tTBB5_HUMAN\tTUBB\t8|6|5|2\t21\t5.25\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t4.43\t52.50\t0.00\t\n-WT_EGFR\tTBB3_HUMAN\tTUBB3\t2|2|3|1\t8\t2.00\t4\t0|0|0|0\t0.74\t1.00\t0.74\t1.00\t0.74\t2.40\t20.00\t0.05\t\n-WT_EGFR\tHS90A_HUMAN\tHSP90AA1\t3|3|1|2\t9\t2.25\t4\t0|0|0|0\t0.75\t1.00\t0.75\t1.00\t0.75\t2.40\t22.50\t0.02\t\n-WT_EGFR\tHS90B_HUMAN\tHSP90AB1\t4|2|1|2\t9\t2.25\t4\t0|0|0|0\t0.74\t1.00\t0.74\t1.00\t0.74\t2.40\t22.50\t0.04\t\n-WT_EGFR\t1433E_HUMAN\tYWHAE\t1|0|0|0\t1\t0.25\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t2.50\t0.51\t\n-WT_EGFR\t1433T_HUMAN\tYWHAQ\t1|0|0|0\t1\t0.25\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t2.50\t0.51\t\n-WT_EGFR\tLPPRC_HUMAN\tLRPPRC\t1|3|3|3\t10\t2.50\t4\t0|0|0|0\t0.75\t1.00\t0.75\t1.00\t0.75\t2.40\t25.00\t0.00\t\n-WT_EGFR\tERRFI_HUMAN\tERRFI1\t1|2|1|1\t5\t1.25\t4\t0|0|0|0\t0.25\t0.99\t0.25\t0.99\t0.25\t2.40\t12.50\t0.28\t\n-WT_EGFR\tAP2A1_HUMAN\tAP2A1\t1|1|1|1\t4\t1.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t2.40\t10.00\t0.51\t\n-WT_EGFR\tGRB7_HUMAN\tGRB7\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-WT_EGFR\tSTAT3_HUMAN\tSTAT3\t4|5|3|3\t15\t3.75\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t6.28\t37.50\t0.00\t\n-WT_EGFR\tANXA2_HUMAN\tANXA2\t3|4|0|1\t8\t2.00\t4\t0|0|0|0\t0.50\t1.00\t0.50\t1.00\t0.50\t-0.11\t20.00\t0.10\t\n-WT_EGFR\tPTPRA_HUMAN\tPTPRA\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-WT_EGFR\tC1QBP_HUMAN\tC1QBP\t0|0|0|0\t0\t0.00\t4\t7|'..b'\t0|0|1|1\t0.00\t0.00\t0.00\t0.00\t0.00\t-1.55\t1.00\t0.51\t\n-ER_P85B\tPK3CA_HUMAN\tPIK3CA\t3|3|6|6\t18\t4.50\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t6.28\t45.00\t0.00\t\n-ER_P85B\tERBB3_HUMAN\tERBB3\t1|0|2|1\t4\t1.00\t4\t0|0|0|0\t0.25\t0.99\t0.25\t0.99\t0.25\t-0.11\t10.00\t0.28\t\n-ER_P85B\tADT2_HUMAN\tSLC25A5\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tADT3_HUMAN\tSLC25A6\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tK1C9_HUMAN\tKRT9\t1|0|1|0\t2\t0.50\t4\t0|0|1|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.65\t2.00\t0.51\t\n-ER_P85B\tTBA1A_HUMAN\tTUBA1A\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tTBA4A_HUMAN\tTUBA4A\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tARHG5_HUMAN\tARHGEF5\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tPK3CB_HUMAN\tPIK3CB\t1|1|4|2\t8\t2.00\t4\t0|0|0|0\t0.50\t1.00\t0.50\t1.00\t0.50\t2.40\t20.00\t0.14\t\n-ER_P85B\tIRS1_HUMAN\tIRS1\t2|3|3|4\t12\t3.00\t4\t0|0|0|0\t1.00\t1.00\t1.00\t1.00\t1.00\t4.43\t30.00\t0.00\t\n-ER_P85B\tTBB5_HUMAN\tTUBB\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tTBB3_HUMAN\tTUBB3\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tHS90A_HUMAN\tHSP90AA1\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tHS90B_HUMAN\tHSP90AB1\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\t1433E_HUMAN\tYWHAE\t0|0|1|1\t2\t0.50\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t5.00\t0.51\t\n-ER_P85B\t1433T_HUMAN\tYWHAQ\t0|0|1|1\t2\t0.50\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t5.00\t0.51\t\n-ER_P85B\tLPPRC_HUMAN\tLRPPRC\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tERRFI_HUMAN\tERRFI1\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tAP2A1_HUMAN\tAP2A1\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tGRB7_HUMAN\tGRB7\t1|1|1|0\t3\t0.75\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t7.50\t0.51\t\n-ER_P85B\tSTAT3_HUMAN\tSTAT3\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tANXA2_HUMAN\tANXA2\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tPTPRA_HUMAN\tPTPRA\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tC1QBP_HUMAN\tC1QBP\t0|0|0|0\t0\t0.00\t4\t7|5|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-11.03\t0.00\t0.51\t\n-ER_P85B\tSHC1_HUMAN\tSHC1\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tPTN11_HUMAN\tPTPN11\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tPON2_HUMAN\tPON2\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tMET_HUMAN\tMET\t0|0|1|1\t2\t0.50\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t5.00\t0.51\t\n-ER_P85B\tCRKL_HUMAN\tCRKL\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tCBL_HUMAN\tCBL\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tPHLA2_HUMAN\tPHLDA2\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tRT27_HUMAN\tMRPS27\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tM2OM_HUMAN\tSLC25A11\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tSTAT1_HUMAN\tSTAT1\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tSLIRP_HUMAN\tSLIRP\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tPK3CD_HUMAN\tPIK3CD\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tGRP75_HUMAN\tHSPA9\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tAIFM1_HUMAN\tAIFM1\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tPHB_HUMAN\tPHB\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tHNRPK_HUMAN\tHNRNPK\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tTMM33_HUMAN\tTMEM33\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n-ER_P85B\tRT34_HUMAN\tMRPS34\t0|0|0|0\t0\t0.00\t4\t0|0|0|0\t0.00\t0.00\t0.00\t0.00\t0.00\t-0.11\t0.00\t0.51\t\n' |