# HG changeset patch
# User bornea
# Date 1458070244 14400
# Node ID 59e57834c36666597ab6c3bd8b78f348c9531cff
# Parent  744905986eb929c6c8dd09302d42cff4febb8073
Uploaded

diff -r 744905986eb9 -r 59e57834c366 Dotplot_Release/BaitCheck.pl
--- a/Dotplot_Release/BaitCheck.pl	Tue Mar 15 15:27:13 2016 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,45 +0,0 @@
-#!/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 $baitn;
-
-
diff -r 744905986eb9 -r 59e57834c366 Dotplot_Release/Normalization.R
--- a/Dotplot_Release/Normalization.R	Tue Mar 15 15:27:13 2016 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,44 +0,0 @@
-#!/usr/bin/env Rscript
-
-args <- commandArgs(trailingOnly = TRUE)
-
-#this programs normalizes a saint input file based on the spectral counts of all preys
-
-d = read.delim(args[1], header=T, sep="\t", as.is=T)
-
-baitn = 1
-curr_bait <- d$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$AvgSpec[i]/s[baitn]
-	}
-}
-
-#print normalized data to file
-
-write.table(d_n, file = "norm_saint.txt", sep="\t", quote=F, row.names=F)
-
diff -r 744905986eb9 -r 59e57834c366 Dotplot_Release/Normalization_sigpreys.R
--- a/Dotplot_Release/Normalization_sigpreys.R	Tue Mar 15 15:27:13 2016 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,46 +0,0 @@
-#!/usr/bin/env Rscript
-
-#this programs normalizes a saint input file based on the spectral counts of "signficant" preys
-# that is, preys with an FDR <= the secondary cutoff as supplied to the dotplot script
-
-args <- commandArgs(trailingOnly = TRUE)
-
-d = read.delim(args[1], header=T, as.is=T)
-d <- d[d$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)
-
diff -r 744905986eb9 -r 59e57834c366 Dotplot_Release/R_dotPlot.R
--- a/Dotplot_Release/R_dotPlot.R	Tue Mar 15 15:27:13 2016 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,83 +0,0 @@
-#!/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()
diff -r 744905986eb9 -r 59e57834c366 Dotplot_Release/R_dotPlot_hc.R
--- a/Dotplot_Release/R_dotPlot_hc.R	Tue Mar 15 15:27:13 2016 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,125 +0,0 @@
-#!/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()
diff -r 744905986eb9 -r 59e57834c366 Dotplot_Release/R_dotPlot_nc.R
--- a/Dotplot_Release/R_dotPlot_nc.R	Tue Mar 15 15:27:13 2016 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,140 +0,0 @@
-#!/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$z4
-		panel.xyplot(x,y
-			,as.table=TRUE
-			,pch=21 # point type to use (circles in this case)
-			,cex=((z.2-min(z.2,na.rm=TRUE))/(max(z.2,na.rm=TRUE)-min(z.2,na.rm=TRUE)))*3 #circle size
-			,fill=z.colors[floor((z.c-min(z.c,na.rm=TRUE))*nmb.colors/(max(z.c,na.rm=TRUE)-min(z.c,na.rm=TRUE)))+1] # circle colors
-			,col=z.colors[1+z.3] # border colors
-			,lex=z.4 #border thickness
-			)
-	}
-	#,main="Fold change" # graph main title
-	)
-if(ncol(data.file) > 4) ht=3.5+(0.36*((ncol(data.file)-1)-4)) else ht=3.5
-if(nrow(data.file) > 20) wd=8.25+(0.29*(nrow(data.file)-20)) else wd=5.7+(0.28*(nrow(data.file)-10))
-pdf("dotplot.pdf", onefile = FALSE, paper = "special", height = ht, width = wd, pointsize = 2)
-print(pl)
-dev.off()
-
-#plot heatmap
-
-heat_df <- acast(df, y~x, value.var="z1")
-heat_df <- apply(heat_df, 2, rev)
-
-if(ncol(data.file) > 4) ht=3.5+(0.1*((ncol(data.file)-1)-4)) else ht=3.5
-if(nrow(data.file) > 20) wd=8.25+(0.1*(nrow(data.file)-20)) else wd=5+(0.1*(nrow(data.file)-10))
-pdf("heatmap_borders.pdf", onefile = FALSE, paper = "special", height = ht, width = wd, pointsize = 2)
-pheatmap_j(heat_df, scale="none", border_color="black", border_width = 0.1, cluster_rows=FALSE, cluster_cols=FALSE, col=colorRampPalette(c("#FFFFFF", brewer.pal(9,"Blues")))(100))
-dev.off()
-
-pdf("heatmap_no_borders.pdf", onefile = FALSE, paper = "special", height = ht, width = wd, pointsize = 2)
-pheatmap_j(heat_df, scale="none", border_color=NA, cluster_rows=FALSE, cluster_cols=FALSE, col=colorRampPalette(c("#FFFFFF", brewer.pal(9,"Blues")))(100))
-dev.off()
diff -r 744905986eb9 -r 59e57834c366 Dotplot_Release/SOFD.pl
--- a/Dotplot_Release/SOFD.pl	Tue Mar 15 15:27:13 2016 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,112 +0,0 @@
-#!/usr/bin/perl
-
-# 17/12/2013
-
-if($#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][$krem]";
-		}
-		else{
-			print SC_FILE "\t0";
-			print FDR_FILE "\t1";
-		}
-	}
-	print SC_FILE "\n";
-	print FDR_FILE "\n";
-}
-close(SC_FILE);
-close(FDR_FILE);
-
diff -r 744905986eb9 -r 59e57834c366 Dotplot_Release/SaintConvert.pl
--- a/Dotplot_Release/SaintConvert.pl	Tue Mar 15 15:27:13 2016 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,52 +0,0 @@
-#!/usr/bin/perl
-
-# 17/12/2013
-
-if($#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[$i]\t111\n";
-}
-close(OFILE);
-
diff -r 744905986eb9 -r 59e57834c366 Dotplot_Release/Step1_data_reformating.R
--- a/Dotplot_Release/Step1_data_reformating.R	Tue Mar 15 15:27:13 2016 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,41 +0,0 @@
-#!/usr/bin/env Rscript
-
-args <- commandArgs(trailingOnly = TRUE)
-
-d = read.delim(args[1], header=T, sep="\t", as.is=T)
-
-### Select Prey interactions were at least one Bait > Probability Threshold
-
-preylist=unique(c(d$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)
-
-
-
-
-
diff -r 744905986eb9 -r 59e57834c366 Dotplot_Release/Step2_data_filtering.R
--- a/Dotplot_Release/Step2_data_filtering.R	Tue Mar 15 15:27:13 2016 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,28 +0,0 @@
-#!/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)
-
diff -r 744905986eb9 -r 59e57834c366 Dotplot_Release/Step3_nestedcluster
Binary file Dotplot_Release/Step3_nestedcluster has changed
diff -r 744905986eb9 -r 59e57834c366 Dotplot_Release/Step4_biclustering.R
--- a/Dotplot_Release/Step4_biclustering.R	Tue Mar 15 15:27:13 2016 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,201 +0,0 @@
-#!/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$Bait, sep="_")
-
-###dd = cor(tmp[,-26])    ### This line is only for Chris' data (one bait has all zeros in the estimated parameters)
-dd = cor(tmp)    ### This line is only for Chris' data (one bait has all zeros in the estimated parameters)
-
-write.table(dd, "bait2bait_matrix.txt", sep="\t", quote=F)
-heatmap.2(as.matrix(dd), trace="n", breaks=seq(-1,1,by=0.1), col=(greenred(20)), cexRow=0.7, cexCol=0.7)
-dev.off()
-
-tmp = mcmc[,2]
-ymax = max(tmp)
-ymin = min(tmp)
-pdf("stats.pdf", height=12, width=12)
-
-plot(mcmc[mcmc[,4]=="G",3], type="s", xlab="Iterations", ylab="Number of Clusters", main="")
-plot(mcmc[,2], type="l", xlab="Iterations", ylab="Log-Likelihood", main="", ylim=c(ymin,ymax))
-
-dev.off()
-
diff -r 744905986eb9 -r 59e57834c366 Dotplot_Release/biclust.tar.gz
Binary file Dotplot_Release/biclust.tar.gz has changed
diff -r 744905986eb9 -r 59e57834c366 Dotplot_Release/biclust_param.txt
--- a/Dotplot_Release/biclust_param.txt	Tue Mar 15 15:27:13 2016 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,12 +0,0 @@
-np 10
-nb 100
-a 1.0
-b 1.0
-lambda 0.0
-nu 25.0
-alpha 1.0
-rho 1.0
-gamma 1.0
-nburn 5000
-niter 10000
-
diff -r 744905986eb9 -r 59e57834c366 Dotplot_Release/dotplot.bash
--- a/Dotplot_Release/dotplot.bash	Tue Mar 15 15:27:13 2016 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,252 +0,0 @@
-#!/bin/bash
-#SCRIPT=$(readlink -e $0)
-#SCRIPTPATH=`dirname $SCRIPT`
-pushd `dirname $0` > /dev/null
-SCRIPTPATH=`pwd`
-popd > /dev/null
-
-usage() { printf "Usage: $0 
-[-f <saint_file_name.txt>]
-[-i <0 for SaintExpress format, 1 for other>]
-[-c <clustering to perform. Options: b (biclustering), h (hierarchical), n (none, requires input text files for bait and prey ordering; see options -b and -p)>]
-[-n <clustering type to be performed if option -c is set to \"h\">]
-[-d <distance metric to use if option -c is set to \"h\">]
-[-b <list of bait proteins in display order (see option -c n)>]
-[-p <list of prey proteins in display order (see option -c n). Set this to \"all\" if you want to include all preys and cluster them>]
-[-s <primary FDR cutoff [0-1, recommended=0.01]>]
-[-t <secondary FDR cutoff [must be less than the primary, recommended=0.025]>
-[-x <spectral count minimum. Only preys with >= this will be used]>
-[-m <maximum spectral count>]
-[-N <normalization, 0 for no (default), 1 for yes, 2 for normalization based on significant preys counts (prey FDR <= option -t)>]
-[-C <FDR cutoff for normalization if using option -N 2 (deafult is -t)>]\n"
-1>&2; exit 1; }
-
-N=0
-n="ward"
-d="canberra"
-x=0
-i=0
-while getopts ":f:i:s:t:x:m:c:n:d:b:p:N:C:" o; do
-    case "${o}" in
-        f)
-            f=${OPTARG}
-            ;;
-        i)
-	    i=${OPTARG}
-            ;;
-        s)
-            s=${OPTARG}
-            ;;
-	t)
-            t=${OPTARG}
-            ;;
-        x)
-	    x=${OPTARG}
-            ;;
-	m)
-            m=${OPTARG}
-            ;;
-	c)
-            c=${OPTARG}
-	    ;;
-	n)
-	    n=${OPTARG}
-	    ;;
-	d)
-	    d=${OPTARG}
-	    ;;
-	b)
-            b=${OPTARG}
-	    ;;
-	p)
-	    p=${OPTARG}
-	    ;;
-	N)
-	    N=${OPTARG}
-	    ;;
-	C)
-	    C=${OPTARG}
-	    ;;
-        *)
-            usage
-            ;;
-    esac
-done
-shift $((OPTIND-1))
-
-filename=${f%%.*}
-echo "Saint input file = ${f}"
-echo "Primary FDR cutoff = ${s}"
-echo "Secondary FDR cutoff for dotplot = ${t}"
-echo "Minimum spectral count for significant preys = ${x}"
-echo "Maximum spectral count for dot plot = ${m}"
-
-if [ -z "${f}" ] || [ -z "${s}" ] || [ -z "${t}" ] || [ -z "${m}" ] || [ -z "${c}" ]; then
-    usage
-fi
-
-if [ "${i}" == 1 ]; then
-	$SCRIPTPATH/SaintConvert.pl -i ${f}
-	f="mockSaintExpress.txt"
-fi
-
-if [ "${x}" -ge "${m}" ]; then
-	echo "spectral count minimum (${x}) cannot be greater than or equal to the maximum (${m})"
-	exit 1;
-elif [ "${x}" -lt 0 ]; then
-	echo "spectral count minimum (${x}) cannot be less than 0. Setting to 0 and continuing"
-	x=0
-fi
-
-###Check for normalization
-
-if [ "${N}" == 1 ]; then
-	printf "\nNormalization is being performed\n"
-	$SCRIPTPATH/Normalization.R ${f}
-	f="norm_saint.txt"
-elif [ "${N}" == 2 ]; then
-	printf "\nNormalization is being performed\n"
-	if [ -z "${C}" ]; then
-		C=${t}
-	fi
-	$SCRIPTPATH/Normalization_sigpreys.R ${f} ${C}
-	f="norm_saint.txt"
-fi
-
-
-###Check for clustering etc
-
-if [ "${c}" == "h" ] && [ -z "${n}" ]; then
-	printf "\nHierarchial clustering was selected (-c = h), but no clustering method (-n) was chosen.\n"
-	printf "The input parameter -n must be set to one of \"average\", \"centroid\", \"complete\", \"mcquitty\",\n"
-	printf "\"median\", \"single\" or \"ward\". \"ward\" will be selected as default.\n\n"
-	n="ward"
-elif [ "${c}" == "h" ] && [ -n "${n}" ]; then
-	if [ "${n}" == "average" ] || [ "${n}" == "centroid" ] || [ "${n}" == "complete" ] || [ "${n}" == "mcquitty" ] || [ "${n}" == "median" ] || [  "${n}" == "single" ] || [ "${n}" == "ward" ]; then
-		printf "\nHierarchical clustering (method = ${n}) will be performed\n\n"
-	else
-		printf "\n${n} is not a valid Hierarchical clustering method.\n"
-		printf "Choose one of \"average\", \"centroid\", \"complete\", \"mcquitty\", \"median\", \"single\" or \"ward\"\n\n"
-		exit 1
-	fi
-fi
-
-p_c=0
-if [ "${c}" == "h" ] && [ -z "${d}" ]; then
-	printf "\nHierarchial clustering was selected (-c = h), but no distance metric (-d) was chosen.\n"
-	printf "The input parameter -d must be set to one of  \"binary\", \"canberra\", \"euclidean\",\n"
-	printf "\"manhattan\", \"maximum\" or \"minkowski\". \"canberra\" will be selected as default.\n\n"
-	d="canberra"
-elif [ "${c}" == "h" ] && [ -n "${d}" ]; then
-	if [ "${d}" == "binary" ] || [ "${d}" == "canberra" ] || [ "${d}" == "euclidean" ] || [ "${d}" == "manhattan" ] || [ "${d}" == "maximum" ] || [  "${d}" == "minkowski" ]; then
-		printf "\nHierarchical clustering (distance metric = ${d}) will be performed\n\n"
-	else
-		printf "\n${d} is not a valid Hierarchical clustering distance metric.\n"
-		printf "Choose one of  \"binary\", \"canberra\", \"euclidean\", \"manhattan\", \"maximum\" or \"minkowski\"\n\n"
-		exit 1
-	fi
-fi
-
-if [ "${c}" == "n" ] && [ -z "${b}" ]; then
-	printf "\n\"No Clustering\" option was selected (-c = n), but no bait list was included (option -b).\n"
-	printf "Bait list must be in .txt formart.\n\n"
-	exit 1
-elif [ "${c}" == "n" ] && [ -z "${p}" ]; then
-	printf "\n\"No Clustering\" option was selected (-c = n), but no prey list was included (option -p).\n"
-	printf "Prey list must be in .txt formart.\n\n"
-	exit 1
-elif [ "${c}" == "n" ] && [ "${p}" == "all" ]; then
-	printf "\n\"No Clustering\" option was selected (-c = n) for baits, but preys will still be clustered.\n"
-	printf "using \"ward\" and \"canberra\" as defaults or options as supplied on command line.\n\n"
-	p="empty"
-	p_c=1
-	n="ward"
-	d="canberra"
-fi
-
-
-###Check number of baits
-
-bait_n=$(perl $SCRIPTPATH/BaitCheck.pl -i ${f})
-echo "Number of baits = "$bait_n
-printf "\n\n"
-
-if [ "${c}" == "b" ] && [ $bait_n == 2 ]; then
-	printf "\nWarning only 2 baits are present. Biclustering will not performed.\n"
-	printf "Hierarchical clustering (method = ward) will be performed instead.\n\n"
-	c="h"
-	n="ward"
-fi
-
-
-###Generate plots
-
-if [ "${c}" == "b" ]; then
-	printf "\nBiclustering will be performed\n\n"
-	$SCRIPTPATH/Step1_data_reformating.R ${f} ${s} ${filename}
-	$SCRIPTPATH/Step2_data_filtering.R ${filename}_matrix.txt ${x} ${filename}
-	GSL_RNG_SEED=123  $SCRIPTPATH/Step3_nestedcluster ${filename}.dat $SCRIPTPATH/biclust_param.txt
-	$SCRIPTPATH/Step4_biclustering.R ${filename}.dat
-
-	$SCRIPTPATH/SOFD.pl -i ${f} -s ${s} -x ${x}
-	$SCRIPTPATH/R_dotPlot.R ${s} ${t} ${m}
-	mkdir Output_${filename}
-	mkdir Output_${filename}/TempData_${filename}
-	mv bait_lists Output_${filename}/TempData_${filename}
-	mv Clusters Output_${filename}/TempData_${filename}
-	mv MCMCparameters Output_${filename}/TempData_${filename}
-	mv NestedClusters Output_${filename}/TempData_${filename}
-	mv NestedMu Output_${filename}/TempData_${filename}
-	mv NestedSigma2 Output_${filename}/TempData_${filename}
-	mv OPTclusters Output_${filename}/TempData_${filename}
-	mv ${filename}_matrix.txt Output_${filename}/TempData_${filename}
-	mv ${filename}.dat Output_${filename}/TempData_${filename}
-	mv SC_data.txt Output_${filename}/TempData_${filename}
-	mv FDR_data.txt Output_${filename}/TempData_${filename}
-	mv clustered_matrix.txt Output_${filename}/TempData_${filename}
-	mv singletons.txt Output_${filename}/TempData_${filename}
-	mv bait2bait_matrix.txt Output_${filename}/TempData_${filename}
-	mv baitClusters Output_${filename}/TempData_${filename}
-	mv clusteredData Output_${filename}/TempData_${filename}
-	mv dotplot.pdf Output_${filename}
-	mv bait2bait.pdf Output_${filename} 
-	mv estimated.pdf Output_${filename} 
-	mv stats.pdf Output_${filename}
-	cp $SCRIPTPATH/legend.pdf Output_${filename}
-elif [ "${c}" == "h" ]; then
-
-	$SCRIPTPATH/SOFD.pl -i ${f} -s ${s} -x ${x}
-	$SCRIPTPATH/R_dotPlot_hc.R ${s} ${t} ${m} ${n} ${d} $SCRIPTPATH
-
-	mkdir Output_${filename}
-	mkdir Output_${filename}/TempData_${filename}
-	mv dotplot.pdf Output_${filename}
-	mv heatmap_borders.pdf Output_${filename}
-	mv heatmap_no_borders.pdf Output_${filename}
-	mv bait2bait.pdf Output_${filename}
-	mv SC_data.txt Output_${filename}/TempData_${filename}
-	mv FDR_data.txt Output_${filename}/TempData_${filename}
-	cp $SCRIPTPATH/legend.pdf Output_${filename}
-elif [ "${c}" == "n" ]; then
-	
-	$SCRIPTPATH/SOFD.pl -i ${f} -s ${s} -x ${x}
-	echo "$SCRIPTPATH/R_dotPlot_nc.R ${s} ${t} ${m} ${b} $p_c ${p} ${n} ${d} $SCRIPTPATH"
-	$SCRIPTPATH/R_dotPlot_nc.R ${s} ${t} ${m} ${b} $p_c ${p} ${n} ${d} $SCRIPTPATH
-
-	mkdir Output_${filename}
-	mkdir Output_${filename}/TempData_${filename}
-	mv dotplot.pdf Output_${filename}
-	mv heatmap_borders.pdf Output_${filename}
-	mv heatmap_no_borders.pdf Output_${filename}
-	mv SC_data.txt Output_${filename}/TempData_${filename}
-	mv FDR_data.txt Output_${filename}/TempData_${filename}
-	cp $SCRIPTPATH/legend.pdf Output_${filename}
-else
-	printf -- "-c must be one of [b, h, n]:  b (biclustering), h (hierarchical), n (none, requires input text files for bait and prey ordering>\n"
-	exit 1;
-fi
-
-if [ "${N}" == "1" ] || [ "${N}" == "2" ]; then
-	mv norm_saint.txt Output_${filename}/TempData_${filename}
-fi
-
diff -r 744905986eb9 -r 59e57834c366 Dotplot_Release/pheatmap_j.R
--- a/Dotplot_Release/pheatmap_j.R	Tue Mar 15 15:27:13 2016 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,719 +0,0 @@
-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, ...){
-	# Get height of colnames and length of rownames
-	if(!is.null(coln[1])){
-		longest_coln = which.max(strwidth(coln, units = 'in'))
-		gp = list(fontsize = fontsize_col, ...)
-		coln_height = unit(1, "grobheight", textGrob(coln[longest_coln], rot = 90, gp = do.call(gpar, gp))) + unit(5, "bigpts")
-	}
-	else{
-		coln_height = unit(5, "bigpts")
-	}
-	
-	if(!is.null(rown[1])){
-		longest_rown = which.max(strwidth(rown, units = 'in'))
-		gp = list(fontsize = fontsize_row, ...)
-		rown_width = unit(1, "grobwidth", textGrob(rown[longest_rown], gp = do.call(gpar, gp))) + unit(10, "bigpts")
-	}
-	else{
-		rown_width = unit(5, "bigpts")
-	}
-	
-	gp = list(fontsize = fontsize, ...)
-	# Legend position
-	if(!is.na(legend[1])){
-		longest_break = which.max(nchar(names(legend)))
-		longest_break = unit(1.1, "grobwidth", textGrob(as.character(names(legend))[longest_break], gp = do.call(gpar, gp)))
-		title_length = unit(1.1, "grobwidth", textGrob("Scale", gp = gpar(fontface = "bold", ...)))
-		legend_width = unit(12, "bigpts") + longest_break * 1.2
-		legend_width = max(title_length, legend_width)
-	}
-	else{
-		legend_width = unit(0, "bigpts")
-	}
-	
-	# Set main title height
-	if(is.na(main)){
-		main_height = unit(0, "npc")
-	}
-	else{
-		main_height = unit(1.5, "grobheight", textGrob(main, gp = gpar(fontsize = 1.3 * fontsize, ...)))
-	}
-	
-	# Column annotations
-	if(!is.na(annotation[[1]][1])){
-		# Column annotation height 
-		annot_height = unit(ncol(annotation) * (8 + 2) + 2, "bigpts")
-		# Width of the correponding legend
-		longest_ann = which.max(nchar(as.matrix(annotation)))
-		annot_legend_width = unit(1.2, "grobwidth", textGrob(as.matrix(annotation)[longest_ann], gp = gpar(...))) + unit(12, "bigpts")
-		if(!annotation_legend){
-			annot_legend_width = unit(0, "npc")
-		}
-	}
-	else{
-		annot_height = unit(0, "bigpts")
-		annot_legend_width = unit(0, "bigpts")
-	}
-	
-	# Tree height
-	treeheight_col = unit(treeheight_col, "bigpts") + unit(5, "bigpts")
-	treeheight_row = unit(treeheight_row, "bigpts") + unit(5, "bigpts") 
-	
-	# Set cell sizes
-	if(is.na(cellwidth)){
-		matwidth = unit(1, "npc") - rown_width - legend_width - treeheight_row - annot_legend_width
-	}
-	else{
-		matwidth = unit(cellwidth * ncol, "bigpts")
-	}
-	
-	if(is.na(cellheight)){
-		matheight = unit(1, "npc") - main_height - coln_height - treeheight_col - annot_height
-	}
-	else{
-		matheight = unit(cellheight * nrow, "bigpts")
-	}	
-	
-	
-	# Produce layout()
-	pushViewport(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)))
-	
-	# Get cell dimensions
-	pushViewport(vplayout(4, 2))
-	cellwidth = convertWidth(unit(0:1, "npc"), "bigpts", valueOnly = T)[2] / ncol
-	cellheight = convertHeight(unit(0:1, "npc"), "bigpts", valueOnly = T)[2] / nrow
-	upViewport()
-	
-	# Return minimal cell dimension in bigpts to decide if borders are drawn
-	mindim = min(cellwidth, cellheight) 
-	return(mindim)
-}
-
-draw_dendrogram = function(hc, horizontal = T){
-	h = hc$height / max(hc$height) / 1.05
-	m = hc$merge
-	o = hc$order
-	n = length(o)
-
-	m[m > 0] = n + m[m > 0] 
-	m[m < 0] = abs(m[m < 0])
-
-	dist = matrix(0, nrow = 2 * n - 1, ncol = 2, dimnames = list(NULL, c("x", "y"))) 
-	dist[1:n, 1] = 1 / n / 2 + (1 / n) * (match(1:n, o) - 1)
-
-	for(i in 1:nrow(m)){
-		dist[n + i, 1] = (dist[m[i, 1], 1] + dist[m[i, 2], 1]) / 2
-		dist[n + i, 2] = h[i]
-	}
-	
-	draw_connection = function(x1, x2, y1, y2, y){
-		grid.lines(x = c(x1, x1), y = c(y1, y))
-		grid.lines(x = c(x2, x2), y = c(y2, y))
-		grid.lines(x = c(x1, x2), y = c(y, y))
-	}
-	
-	if(horizontal){
-		for(i in 1:nrow(m)){
-			draw_connection(dist[m[i, 1], 1], dist[m[i, 2], 1], dist[m[i, 1], 2], dist[m[i, 2], 2], h[i])
-		}
-	}
-	
-	else{
-		gr = rectGrob()
-		pushViewport(viewport(height = unit(1, "grobwidth", gr), width = unit(1, "grobheight", gr), angle = 90))
-		dist[, 1] = 1 - dist[, 1] 
-		for(i in 1:nrow(m)){
-			draw_connection(dist[m[i, 1], 1], dist[m[i, 2], 1], dist[m[i, 1], 2], dist[m[i, 2], 2], h[i])
-		}
-		upViewport()
-	}
-}
-
-draw_matrix = function(matrix, border_color, border_width, fmat, fontsize_number){
-	n = nrow(matrix)
-	m = ncol(matrix)
-	x = (1:m)/m - 1/2/m
-	y = 1 - ((1:n)/n - 1/2/n)
-	for(i in 1:m){
-		grid.rect(x = x[i], y = y[1:n], width = 1/m, height = 1/n, gp = gpar(fill = matrix[,i], col = border_color, lwd = border_width))
-		if(attr(fmat, "draw")){
-			grid.text(x = x[i], y = y[1:n], label = fmat[, i], gp = gpar(col = "grey30", fontsize = fontsize_number))
-		}
-	}
-}
-
-draw_colnames = function(coln, ...){
-	m = length(coln)
-	x = (1:m)/m - 1/2/m
-	grid.text(coln, x = x, y = unit(0.96, "npc"), just="right", rot = 90, gp = gpar(...))
-}
-
-draw_rownames = function(rown, ...){
-	n = length(rown)
-	y = 1 - ((1:n)/n - 1/2/n)
-	grid.text(rown, x = unit(0.04, "npc"), y = y, vjust = 0.5, hjust = 0, gp = gpar(...))	
-}
-
-draw_legend = function(color, breaks, legend, ...){
-	height = min(unit(1, "npc"), unit(150, "bigpts"))
-	pushViewport(viewport(x = 0, y = unit(1, "npc"), just = c(0, 1), height = height))
-	legend_pos = (legend - min(breaks)) / (max(breaks) - min(breaks))
-	breaks = (breaks - min(breaks)) / (max(breaks) - min(breaks))
-	h = breaks[-1] - breaks[-length(breaks)]
-	grid.rect(x = 0, y = breaks[-length(breaks)], width = unit(10, "bigpts"), height = h, hjust = 0, vjust = 0, gp = gpar(fill = color, col = "#FFFFFF00"))
-	grid.text(names(legend), x = unit(12, "bigpts"), y = legend_pos, hjust = 0, gp = gpar(...))
-	upViewport()
-}
-
-convert_annotations = function(annotation, annotation_colors){
-	new = annotation
-	for(i in 1:ncol(annotation)){
-		a = annotation[, i]
-		b = annotation_colors[[colnames(annotation)[i]]]
-		if(is.character(a) | is.factor(a)){
-			a = as.character(a)
-			if(length(setdiff(a, names(b))) > 0){
-				stop(sprintf("Factor levels on variable %s do not match with annotation_colors", colnames(annotation)[i]))
-			}
-			new[, i] = b[a]
-		}
-		else{
-			a = cut(a, breaks = 100)
-			new[, i] = colorRampPalette(b)(100)[a]
-		}
-	}
-	return(as.matrix(new))
-}
-
-draw_annotations = function(converted_annotations, border_color, border_width){
-	n = ncol(converted_annotations)
-	m = nrow(converted_annotations)
-	x = (1:m)/m - 1/2/m
-	y = cumsum(rep(8, n)) - 4 + cumsum(rep(2, n))
-	for(i in 1:m){
-		grid.rect(x = x[i], unit(y[1:n], "bigpts"), width = 1/m, height = unit(8, "bigpts"), gp = gpar(fill = converted_annotations[i, ], col = border_color, lwd = border_width))
-	}
-}
-
-draw_annotation_legend = function(annotation, annotation_colors, border_color, border_width, ...){
-	y = unit(1, "npc")
-	text_height = unit(1, "grobheight", textGrob("FGH", gp = gpar(...)))
-	for(i in names(annotation_colors)){
-		grid.text(i, x = 0, y = y, vjust = 1, hjust = 0, gp = gpar(fontface = "bold", ...))
-		y = y - 1.5 * text_height
-		if(is.character(annotation[, i]) | is.factor(annotation[, i])){
-			for(j in 1:length(annotation_colors[[i]])){
-				grid.rect(x = unit(0, "npc"), y = y, hjust = 0, vjust = 1, height = text_height, width = text_height, gp = gpar(col = border_color, lwd = border_width, fill = annotation_colors[[i]][j]))
-				grid.text(names(annotation_colors[[i]])[j], x = text_height * 1.3, y = y, hjust = 0, vjust = 1, gp = gpar(...))
-				y = y - 1.5 * text_height
-			}
-		}
-		else{
-			yy = y - 4 * text_height + seq(0, 1, 0.02) * 4 * text_height
-			h = 4 * text_height * 0.02
-			grid.rect(x = unit(0, "npc"), y = yy, hjust = 0, vjust = 1, height = h, width = text_height, gp = gpar(col = "#FFFFFF00", fill = colorRampPalette(annotation_colors[[i]])(50)))
-			txt = rev(range(grid.pretty(range(annotation[, i], na.rm = TRUE))))
-			yy = y - c(0, 3) * text_height
-			grid.text(txt, x = text_height * 1.3, y = yy, hjust = 0, vjust = 1, gp = gpar(...))
-			y = y - 4.5 * text_height
-		}
-		y = y - 1.5 * text_height
-	}
-}
-
-draw_main = function(text, ...){
-	grid.text(text, gp = gpar(fontface = "bold", ...))
-}
-
-vplayout = function(x, y){
-	return(viewport(layout.pos.row = x, layout.pos.col = y))
-}
-
-heatmap_motor = function(matrix, border_color, border_width, cellwidth, cellheight, tree_col, tree_row, treeheight_col, treeheight_row, filename, width, height, breaks, color, legend, annotation, annotation_colors, annotation_legend, main, fontsize, fontsize_row, fontsize_col, fmat, fontsize_number, ...){
-	grid.newpage()
-	
-	# Set layout
-	mindim = lo(coln = colnames(matrix), rown = rownames(matrix), nrow = nrow(matrix), ncol = ncol(matrix), cellwidth = cellwidth, cellheight = cellheight, treeheight_col = treeheight_col, treeheight_row = treeheight_row, legend = legend, annotation = annotation, annotation_colors = annotation_colors, annotation_legend = annotation_legend, main = main, fontsize = fontsize, fontsize_row = fontsize_row, fontsize_col = fontsize_col,  ...)
-	
-	if(!is.na(filename)){
-		pushViewport(vplayout(1:5, 1:5))
-		
-		if(is.na(height)){
-			height = convertHeight(unit(0:1, "npc"), "inches", valueOnly = T)[2]
-		}
-		if(is.na(width)){
-			width = convertWidth(unit(0:1, "npc"), "inches", valueOnly = T)[2]
-		}
-		
-		# Get file type
-		r = regexpr("\\.[a-zA-Z]*$", filename)
-		if(r == -1) stop("Improper filename")
-		ending = substr(filename, r + 1, r + attr(r, "match.length"))
-
-		f = switch(ending,
-			pdf = function(x, ...) pdf(x, ...),
-			png = function(x, ...) png(x, units = "in", res = 300, ...),
-			jpeg = function(x, ...) jpeg(x, units = "in", res = 300, ...),
-			jpg = function(x, ...) jpeg(x, units = "in", res = 300, ...),
-			tiff = function(x, ...) tiff(x, units = "in", res = 300, compression = "lzw", ...),
-			bmp = function(x, ...) bmp(x, units = "in", res = 300, ...),
-			stop("File type should be: pdf, png, bmp, jpg, tiff")
-		)
-		
-		# print(sprintf("height:%f width:%f", height, width))
-		f(filename, height = height, width = width)
-		heatmap_motor(matrix, cellwidth = cellwidth, cellheight = cellheight, border_color = border_color, border_width = border_width, tree_col = tree_col, tree_row = tree_row, treeheight_col = treeheight_col, treeheight_row = treeheight_row, breaks = breaks, color = color, legend = legend, annotation = annotation, annotation_colors = annotation_colors, annotation_legend = annotation_legend, filename = NA, main = main, fontsize = fontsize, fontsize_row = fontsize_row, fontsize_col = fontsize_col, fmat = fmat, fontsize_number =  fontsize_number, ...)
-		dev.off()
-		upViewport()
-		return()
-	}
-	
-	# Omit border color if cell size is too small 
-	if(mindim < 3) border_color = NA
-	
-	# Draw title
-	if(!is.na(main)){
-		pushViewport(vplayout(1, 2))
-		draw_main(main, fontsize = 1.3 * fontsize, ...)
-		upViewport()
-	}
-	
-	# Draw tree for the columns
-	if(!is.na(tree_col[[1]][1]) & treeheight_col != 0){
-		pushViewport(vplayout(2, 2))
-		draw_dendrogram(tree_col, horizontal = T)
-		upViewport()
-	}
-	
-	# Draw tree for the rows
-	if(!is.na(tree_row[[1]][1]) & treeheight_row != 0){
-		pushViewport(vplayout(4, 1))
-		draw_dendrogram(tree_row, horizontal = F)
-		upViewport()
-	}
-	
-	# Draw matrix
-	pushViewport(vplayout(4, 2))
-	draw_matrix(matrix, border_color, border_width, fmat, fontsize_number)
-	upViewport()
-	
-	# Draw colnames
-	if(length(colnames(matrix)) != 0){
-		pushViewport(vplayout(5, 2))
-		pars = list(colnames(matrix), fontsize = fontsize_col, ...)
-		do.call(draw_colnames, pars)
-		upViewport()
-	}
-	
-	# Draw rownames
-	if(length(rownames(matrix)) != 0){
-		pushViewport(vplayout(4, 3))
-		pars = list(rownames(matrix), fontsize = fontsize_row, ...)
-		do.call(draw_rownames, pars)
-		upViewport()
-	}
-	
-	# Draw annotation tracks
-	if(!is.na(annotation[[1]][1])){
-		pushViewport(vplayout(3, 2))
-		converted_annotation = convert_annotations(annotation, annotation_colors)
-		draw_annotations(converted_annotation, border_color, border_width)
-		upViewport()
-	}
-	
-	# Draw annotation legend
-	if(!is.na(annotation[[1]][1]) & annotation_legend){
-		if(length(rownames(matrix)) != 0){
-			pushViewport(vplayout(4:5, 5))
-		}
-		else{
-			pushViewport(vplayout(3:5, 5))
-		}
-		draw_annotation_legend(annotation, annotation_colors, border_color, border_width, fontsize = fontsize, ...)
-		upViewport()
-	}
-	
-	# Draw legend
-	if(!is.na(legend[1])){
-		length(colnames(matrix))
-		if(length(rownames(matrix)) != 0){
-			pushViewport(vplayout(4:5, 4))
-		}
-		else{
-			pushViewport(vplayout(3:5, 4))
-		}
-		draw_legend(color, breaks, legend, fontsize = fontsize, ...)
-		upViewport()
-	}
-	
-	
-}
-
-generate_breaks = function(x, n, center = F){
-	if(center){
-		m = max(abs(c(min(x, na.rm = T), max(x, na.rm = T))))
-		res = seq(-m, m, length.out = n + 1)
-	}
-	else{
-		res = seq(min(x, na.rm = T), max(x, na.rm = T), length.out = n + 1)
-	}
-	
-	return(res)
-}
-
-scale_vec_colours = function(x, col = rainbow(10), breaks = NA){
-	return(col[as.numeric(cut(x, breaks = breaks, include.lowest = T))])
-}
-
-scale_colours = function(mat, col = rainbow(10), breaks = NA){
-	mat = as.matrix(mat)
-	return(matrix(scale_vec_colours(as.vector(mat), col = col, breaks = breaks), nrow(mat), ncol(mat), dimnames = list(rownames(mat), colnames(mat))))
-}
-
-cluster_mat = function(mat, distance, method){
-	if(!(method %in% c("ward", "single", "complete", "average", "mcquitty", "median", "centroid"))){
-		stop("clustering method has to one form the list: 'ward', 'single', 'complete', 'average', 'mcquitty', 'median' or 'centroid'.")
-	}
-	if(!(distance[1] %in% c("correlation", "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski")) & class(distance) != "dist"){
-		print(!(distance[1] %in% c("correlation", "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski")) | class(distance) != "dist")
-		stop("distance has to be a dissimilarity structure as produced by dist or one measure  form the list: 'correlation', 'euclidean', 'maximum', 'manhattan', 'canberra', 'binary', 'minkowski'")
-	}
-	if(distance[1] == "correlation"){
-		d = as.dist(1 - cor(t(mat)))
-	}
-	else{
-		if(class(distance) == "dist"){
-			d = distance
-		}
-		else{
-			d = dist(mat, method = distance)
-		}
-	}
-	
-	return(hclust(d, method = method))
-}
-
-scale_rows = function(x){
-	m = apply(x, 1, mean, na.rm = T)
-	s = apply(x, 1, sd, na.rm = T)
-	return((x - m) / s)
-}
-
-scale_mat = function(mat, scale){
-	if(!(scale %in% c("none", "row", "column"))){
-		stop("scale argument shoud take values: 'none', 'row' or 'column'")
-	}
-	mat = switch(scale, none = mat, row = scale_rows(mat), column = t(scale_rows(t(mat))))
-	return(mat)
-}
-
-generate_annotation_colours = function(annotation, annotation_colors, drop){
-	if(is.na(annotation_colors)[[1]][1]){
-		annotation_colors = list()
-	}
-	count = 0
-	for(i in 1:ncol(annotation)){
-		if(is.character(annotation[, i]) | is.factor(annotation[, i])){
-			if (is.factor(annotation[, i]) & !drop){
-				count = count + length(levels(annotation[, i]))
-			}
-			else{
-				count = count + length(unique(annotation[, i]))
-			}
-		}
-	}
-	
-	factor_colors = hsv((seq(0, 1, length.out = count + 1)[-1] + 
-      0.2)%%1, 0.7, 0.95)
-	
-	set.seed(3453)
-	
-	for(i in 1:ncol(annotation)){
-		if(!(colnames(annotation)[i] %in% names(annotation_colors))){
-			if(is.character(annotation[, i]) | is.factor(annotation[, i])){
-				n = length(unique(annotation[, i]))
-				if (is.factor(annotation[, i]) & !drop){
-					n = length(levels(annotation[, i]))
-				}
-				ind = sample(1:length(factor_colors), n)
-				annotation_colors[[colnames(annotation)[i]]] = factor_colors[ind]
-				l = levels(as.factor(annotation[, i]))
-				l = l[l %in% unique(annotation[, i])]
-				if (is.factor(annotation[, i]) & !drop){
-					l = levels(annotation[, i])
-				}
-				names(annotation_colors[[colnames(annotation)[i]]]) = l
-				factor_colors = factor_colors[-ind]
-			}
-			else{
-				r = runif(1)
-				annotation_colors[[colnames(annotation)[i]]] = hsv(r, c(0.1, 1), 1)
-			}
-		}
-	}
-	return(annotation_colors)
-}
-
-kmeans_pheatmap = function(mat, k = min(nrow(mat), 150), sd_limit = NA, ...){
-	# Filter data
-	if(!is.na(sd_limit)){
-		s = apply(mat, 1, sd)
-		mat = mat[s > sd_limit, ]	
-	}
-	
-	# Cluster data
-	set.seed(1245678)
-	km = kmeans(mat, k, iter.max = 100)
-	mat2 = km$centers
-	
-	# Compose rownames
-	t = table(km$cluster)
-	rownames(mat2) = sprintf("cl%s_size_%d", names(t), t)
-	
-	# Draw heatmap
-	pheatmap(mat2, ...)
-}
- 
-#' A function to draw clustered heatmaps.
-#' 
-#' A function to draw clustered heatmaps where one has better control over some graphical 
-#' parameters such as cell size, etc. 
-#' 
-#' The function also allows to aggregate the rows using kmeans clustering. This is 
-#' advisable if number of rows is so big that R cannot handle their hierarchical 
-#' clustering anymore, roughly more than 1000. Instead of showing all the rows 
-#' separately one can cluster the rows in advance and show only the cluster centers. 
-#' The number of clusters can be tuned with parameter kmeans_k.
-#'
-#' @param mat numeric matrix of the values to be plotted.
-#' @param color vector of colors used in heatmap.
-#' @param kmeans_k the number of kmeans clusters to make, if we want to agggregate the 
-#' rows before drawing heatmap. If NA then the rows are not aggregated.
-#' @param breaks a sequence of numbers that covers the range of values in mat and is one 
-#' element longer than color vector. Used for mapping values to colors. Useful, if needed 
-#' to map certain values to certain colors, to certain values. If value is NA then the 
-#' breaks are calculated automatically.
-#' @param border_color color of cell borders on heatmap, use NA if no border should be 
-#' drawn.
-#' @param cellwidth individual cell width in points. If left as NA, then the values 
-#' depend on the size of plotting window.
-#' @param cellheight individual cell height in points. If left as NA, 
-#' then the values depend on the size of plotting window.
-#' @param scale character indicating if the values should be centered and scaled in 
-#' either the row direction or the column direction, or none. Corresponding values are 
-#' \code{"row"}, \code{"column"} and \code{"none"}
-#' @param cluster_rows boolean values determining if rows should be clustered,
-#' @param cluster_cols boolean values determining if columns should be clustered.
-#' @param clustering_distance_rows distance measure used in clustering rows. Possible 
-#' values are \code{"correlation"} for Pearson correlation and all the distances 
-#' supported by \code{\link{dist}}, such as \code{"euclidean"}, etc. If the value is none 
-#' of the above it is assumed that a distance matrix is provided.
-#' @param clustering_distance_cols distance measure used in clustering columns. Possible 
-#' values the same as for clustering_distance_rows.
-#' @param clustering_method clustering method used. Accepts the same values as 
-#' \code{\link{hclust}}.
-#' @param treeheight_row the height of a tree for rows, if these are clustered. 
-#' Default value 50 points.
-#' @param treeheight_col the height of a tree for columns, if these are clustered. 
-#' Default value 50 points.
-#' @param legend logical to determine if legend should be drawn or not.
-#' @param legend_breaks vector of breakpoints for the legend.
-#' @param legend_labels vector of labels for the \code{legend_breaks}.
-#' @param annotation data frame that specifies the annotations shown on top of the 
-#' columns. Each row defines the features for a specific column. The columns in the data 
-#' and rows in the annotation are matched using corresponding row and column names. Note 
-#' that color schemes takes into account if variable is continuous or discrete.
-#' @param annotation_colors list for specifying annotation track colors manually. It is 
-#' possible to define the colors for only some of the features. Check examples for 
-#' details.
-#' @param annotation_legend boolean value showing if the legend for annotation tracks 
-#' should be drawn. 
-#' @param drop_levels logical to determine if unused levels are also shown in the legend
-#' @param show_rownames boolean specifying if column names are be shown.
-#' @param show_colnames boolean specifying if column names are be shown.
-#' @param main the title of the plot
-#' @param fontsize base fontsize for the plot 
-#' @param fontsize_row fontsize for rownames (Default: fontsize) 
-#' @param fontsize_col fontsize for colnames (Default: fontsize) 
-#' @param display_numbers logical determining if the numeric values are also printed to 
-#' the cells. 
-#' @param number_format format strings (C printf style) of the numbers shown in cells. 
-#' For example "\code{\%.2f}" shows 2 decimal places and "\code{\%.1e}" shows exponential 
-#' notation (see more in \code{\link{sprintf}}).    
-#' @param fontsize_number fontsize of the numbers displayed in cells
-#' @param filename file path where to save the picture. Filetype is decided by 
-#' the extension in the path. Currently following formats are supported: png, pdf, tiff,
-#'  bmp, jpeg. Even if the plot does not fit into the plotting window, the file size is 
-#' calculated so that the plot would fit there, unless specified otherwise.
-#' @param width manual option for determining the output file width in inches.
-#' @param height manual option for determining the output file height in inches.
-#' @param \dots graphical parameters for the text used in plot. Parameters passed to 
-#' \code{\link{grid.text}}, see \code{\link{gpar}}. 
-#' 
-#' @return 
-#' Invisibly a list of components 
-#' \itemize{
-#' 	\item \code{tree_row} the clustering of rows as \code{\link{hclust}} object 
-#' 	\item \code{tree_col} the clustering of columns as \code{\link{hclust}} object
-#' 	\item \code{kmeans} the kmeans clustering of rows if parameter \code{kmeans_k} was 
-#' specified 
-#' }
-#' 
-#' @author  Raivo Kolde <rkolde@@gmail.com>
-#' @examples
-#'  # Generate some data
-#' test = matrix(rnorm(200), 20, 10)
-#' test[1:10, seq(1, 10, 2)] = test[1:10, seq(1, 10, 2)] + 3
-#' test[11:20, seq(2, 10, 2)] = test[11:20, seq(2, 10, 2)] + 2
-#' test[15:20, seq(2, 10, 2)] = test[15:20, seq(2, 10, 2)] + 4
-#' colnames(test) = paste("Test", 1:10, sep = "")
-#' rownames(test) = paste("Gene", 1:20, sep = "")
-#' 
-#' # Draw heatmaps
-#' pheatmap(test)
-#' pheatmap(test, kmeans_k = 2)
-#' pheatmap(test, scale = "row", clustering_distance_rows = "correlation")
-#' pheatmap(test, color = colorRampPalette(c("navy", "white", "firebrick3"))(50))
-#' pheatmap(test, cluster_row = FALSE)
-#' pheatmap(test, legend = FALSE)
-#' pheatmap(test, display_numbers = TRUE)
-#' pheatmap(test, display_numbers = TRUE, number_format = "%.1e")
-#' pheatmap(test, cluster_row = FALSE, legend_breaks = -1:4, legend_labels = c("0", 
-#' "1e-4", "1e-3", "1e-2", "1e-1", "1"))
-#' pheatmap(test, cellwidth = 15, cellheight = 12, main = "Example heatmap")
-#' pheatmap(test, cellwidth = 15, cellheight = 12, fontsize = 8, filename = "test.pdf")
-#' 
-#' 
-#' # Generate column annotations
-#' annotation = data.frame(Var1 = factor(1:10 %% 2 == 0, 
-#' 				labels = c("Class1", "Class2")), Var2 = 1:10)
-#' annotation$Var1 = factor(annotation$Var1, levels = c("Class1", "Class2", "Class3"))
-#' rownames(annotation) = paste("Test", 1:10, sep = "")
-#' 
-#' pheatmap(test, annotation = annotation)
-#' pheatmap(test, annotation = annotation, annotation_legend = FALSE)
-#' pheatmap(test, annotation = annotation, annotation_legend = FALSE, drop_levels = FALSE)
-#' 
-#' # Specify colors
-#' Var1 = c("navy", "darkgreen")
-#' names(Var1) = c("Class1", "Class2")
-#' Var2 = c("lightgreen", "navy")
-#' 
-#' ann_colors = list(Var1 = Var1, Var2 = Var2)
-#' 
-#' pheatmap(test, annotation = annotation, annotation_colors = ann_colors, main = "Example")
-#' 
-#' # Specifying clustering from distance matrix
-#' drows = dist(test, method = "minkowski")
-#' dcols = dist(t(test), method = "minkowski")
-#' pheatmap(test, clustering_distance_rows = drows, clustering_distance_cols = dcols)
-#'
-#' @export
-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, ...){
-	
-	# Preprocess matrix
-	mat = as.matrix(mat)
-	if(scale != "none"){
-		mat = scale_mat(mat, scale)
-		if(is.na(breaks)){
-			breaks = generate_breaks(mat, length(color), center = T)
-		}
-	}
-	
-	
-	# Kmeans
-	if(!is.na(kmeans_k)){
-		# Cluster data
-		km = kmeans(mat, kmeans_k, iter.max = 100)
-		mat = km$centers
-
-		# Compose rownames
-		t = table(km$cluster)
-		rownames(mat) = sprintf("cl%s_size_%d", names(t), t)
-	}
-	else{
-		km = NA
-	}
-	
-	# Do clustering
-	if(cluster_rows){
-		tree_row = cluster_mat(mat, distance = clustering_distance_rows, method = clustering_method)
-		mat = mat[tree_row$order, , drop = FALSE]
-	}
-	else{
-		tree_row = NA
-		treeheight_row = 0
-	}
-	
-	if(cluster_cols){
-		tree_col = cluster_mat(t(mat), distance = clustering_distance_cols, method = clustering_method)
-		mat = mat[, tree_col$order, drop = FALSE]
-	}
-	else{
-		tree_col = NA
-		treeheight_col = 0
-	}
-	
-	# Format numbers to be displayed in cells 
-	if(display_numbers){
-		fmat = matrix(sprintf(number_format, mat), nrow = nrow(mat), ncol = ncol(mat))
-		attr(fmat, "draw") = TRUE
-	}
-	else{
-		fmat = matrix(NA, nrow = nrow(mat), ncol = ncol(mat))
-		attr(fmat, "draw") = FALSE
-	}
-	
-	
-	# Colors and scales
-	if(!is.na(legend_breaks[1]) & !is.na(legend_labels[1])){
-		if(length(legend_breaks) != length(legend_labels)){
-			stop("Lengths of legend_breaks and legend_labels must be the same")
-		}
-	}
-	
-	
-	if(is.na(breaks[1])){
-      breaks = generate_breaks(as.vector(mat), length(color))
-  }
-  if (legend & is.na(legend_breaks[1])) {
-      legend = grid.pretty(range(as.vector(breaks)))
-			names(legend) = legend
-  }
-	else if(legend & !is.na(legend_breaks[1])){
-		legend = legend_breaks[legend_breaks >= min(breaks) & legend_breaks <= max(breaks)]
-		
-		if(!is.na(legend_labels[1])){
-			legend_labels = legend_labels[legend_breaks >= min(breaks) & legend_breaks <= max(breaks)]
-			names(legend) = legend_labels
-		}
-		else{
-			names(legend) = legend
-		}
-	}
-  else {
-      legend = NA
-  }
-	mat = scale_colours(mat, col = color, breaks = breaks)
-	
-	# Preparing annotation colors
-	if(!is.na(annotation[[1]][1])){
-		annotation = annotation[colnames(mat), , drop = F]
-		annotation_colors = generate_annotation_colours(annotation, annotation_colors, drop = drop_levels)
-	}
-	
-	if(!show_rownames){
-		rownames(mat) = NULL
-	}
-	
-	if(!show_colnames){
-		colnames(mat) = NULL
-	}
-	
-	# Draw heatmap
-	heatmap_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, ...)
-	
-	invisible(list(tree_row = tree_row, tree_col = tree_col, kmeans = km))
-}
-
-
diff -r 744905986eb9 -r 59e57834c366 toolshed_version/Dotplot_Release.tar.gz
Binary file toolshed_version/Dotplot_Release.tar.gz has changed
diff -r 744905986eb9 -r 59e57834c366 toolshed_version/GNU Free Documentation License.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolshed_version/GNU Free Documentation License.txt	Tue Mar 15 15:30:44 2016 -0400
@@ -0,0 +1,451 @@
+
+                GNU Free Documentation License
+                 Version 1.3, 3 November 2008
+
+
+ Copyright (C) 2000, 2001, 2002, 2007, 2008 Free Software Foundation, Inc.
+     <http://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+0. PREAMBLE
+
+The purpose of this License is to make a manual, textbook, or other
+functional and useful document "free" in the sense of freedom: to
+assure everyone the effective freedom to copy and redistribute it,
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+Secondarily, this License preserves for the author and publisher a way
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+for modifications made by others.
+
+This License is a kind of "copyleft", which means that derivative
+works of the document must themselves be free in the same sense.  It
+complements the GNU General Public License, which is a copyleft
+license designed for free software.
+
+We have designed this License in order to use it for manuals for free
+software, because free software needs free documentation: a free
+program should come with manuals providing the same freedoms that the
+software does.  But this License is not limited to software manuals;
+it can be used for any textual work, regardless of subject matter or
+whether it is published as a printed book.  We recommend this License
+principally for works whose purpose is instruction or reference.
+
+
+1. APPLICABILITY AND DEFINITIONS
+
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diff -r 744905986eb9 -r 59e57834c366 toolshed_version/ProHits_DotPlot_Generator.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolshed_version/ProHits_DotPlot_Generator.py	Tue Mar 15 15:30:44 2016 -0400
@@ -0,0 +1,56 @@
+#######################################################################################
+# Python-code: Dotplot Runner
+# Author: Adam L Borne
+# Contributers: Paul A Stewart, Brent Kuenzi
+#######################################################################################
+# This script runs the dotplot program found at http://prohitstools.mshri.on.ca/.
+#######################################################################################
+# Copyright (C)  Adam Borne.
+# Permission is granted to copy, distribute and/or modify this document
+# under the terms of the GNU Free Documentation License, Version 1.3
+# or any later version published by the Free Software Foundation;
+# with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
+# A copy of the license is included in the section entitled "GNU
+# Free Documentation License".
+#######################################################################################
+## REQUIRED INPUT ##
+
+# 1) list_file: SaintExpress output file.
+# 2) FDR1: Primary false discovery rate. (default = 0.01)
+# 3) FDR2: Secondary false discovery rate. (default = 0.025)
+# 4) spec_max: Maximum spectral count. (default = 50)
+#######################################################################################
+import os 
+import os
+import sys
+
+
+FDR1 = sys.argv[2]
+FDR2 = sys.argv[3]
+spec_max = sys.argv[4]
+os.rename(sys.argv[1], "saint_input.txt")
+ins_path = sys.argv[9]
+
+dirs_list = []
+for (dirpath, dirnames, filename) in os.walk(str(ins_path)):
+    dirs_list.extend(dirnames)
+    break
+if r"Dotplot_Release" in dirs_list:
+    pass
+else:
+    cmd = r"tar -xvf /Dotplot_Release.tar.gz " + str(ins_path) + "/Dotplot_Release.tar.gz" 
+    os.system(cmd)
+
+
+cmd = (str(ins_path) + r"Dotplot_Release/dotplot.bash -f saint_input.txt" + r" -c b -s " + str(FDR1) +
+    r" -t " + str(FDR2) + " -m " + str(spec_max))
+os.system(cmd)
+
+cmd1 = r"cp -a ./Output_saint_input/. ."
+os.system(cmd1)
+
+os.rename("saint_input.txt", str(sys.argv[1]))
+os.rename('dotplot.pdf', str(sys.argv[5])) 
+os.rename('bait2bait.pdf', str(sys.argv[6])) 
+os.rename('estimated.pdf', str(sys.argv[7])) 
+os.rename('stats.pdf', str(sys.argv[8]))
\ No newline at end of file
diff -r 744905986eb9 -r 59e57834c366 toolshed_version/ProHits_DotPlot_Generator.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolshed_version/ProHits_DotPlot_Generator.xml	Tue Mar 15 15:30:44 2016 -0400
@@ -0,0 +1,31 @@
+<tool id="ProHits_DotPlot_Generator" name="ProHits DotPlot Generator">
+  <description></description>
+  <command interpreter="python">ProHits_DotPlot_Generator.py $saint_file $FDR1 $FDR2 $spec_max $dotplot $heatmap $estimated $stats</command>
+  <inputs>
+    <param format="txt" name="saint_file" type="data" label="SAINT Output"/>
+    <param type="float" name="FDR1" label="False Discovery Rate 1" value="0.01"/>
+    <param type="float" name="FDR2" label="False Discovery Rate 2" value="0.025"/>
+    <param type="integer" name="spec_max" label="Maximum spectral count" value="50"/>
+  </inputs>
+  <outputs>
+    <data format="pdf" name="dotplot" label="DotPlot" />    
+    <data format="pdf" name="heatmap" label="Heatmap" /> 
+    <data format="pdf" name="estimated" label="Estimated" />  
+    <data format="pdf" name="stats" label="Stats" />   
+  </outputs>
+  <stdio>
+    <regex match="error"
+	   source="stdout"
+           level="fatal"
+           description="Unknown error"/>
+  </stdio> 
+
+  <tests>
+    <test>
+      <param name="input" value="fa_gc_content_input.fa"/>
+      <output name="out_file1" file="fa_gc_content_output.txt"/>
+    </test>
+  </tests>
+  <help>
+  </help>
+</tool>
\ No newline at end of file
diff -r 744905986eb9 -r 59e57834c366 toolshed_version/test_files.tar
Binary file toolshed_version/test_files.tar has changed
diff -r 744905986eb9 -r 59e57834c366 toolshed_version/test_files/SC_SAINT_list.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolshed_version/test_files/SC_SAINT_list.txt	Tue Mar 15 15:30:44 2016 -0400
@@ -0,0 +1,473 @@
+Bait	Prey	PreyGene	Spec	SpecSum	AvgSpec	NumReplicates	ctrlCounts	AvgP	MaxP	TopoAvgP	TopoMaxP	SaintScore	logOddsScore	FoldChange	BFDR	boosted_by
+WT_EGFR	EGFR_HUMAN	EGFR	67|52|45|55	219	54.75	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	68.87	547.50	0.00	
+WT_EGFR	ERBB2_HUMAN	ERBB2	4|5|3|5	17	4.25	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	6.28	42.50	0.00	
+WT_EGFR	P85B_HUMAN	PIK3R2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_EGFR	P55G_HUMAN	PIK3R3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_EGFR	P85A_HUMAN	PIK3R1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_EGFR	K2C1_HUMAN	KRT1	3|4|6|5	18	4.50	4	10|10|7|7	0.00	0.00	0.00	0.00	0.00	-27.06	0.53	0.51	
+WT_EGFR	K1C10_HUMAN	KRT10	2|0|3|3	8	2.00	4	1|2|4|3	0.01	0.02	0.01	0.02	0.01	-9.56	0.80	0.49	
+WT_EGFR	K1C16_HUMAN	KRT16	0|0|2|1	3	0.75	4	1|1|2|2	0.03	0.12	0.03	0.12	0.03	-5.35	0.50	0.47	
+WT_EGFR	GRB2_HUMAN	GRB2	2|0|2|2	6	1.50	4	0|0|0|0	0.74	0.99	0.74	0.99	0.74	-0.11	15.00	0.05	
+WT_EGFR	GRP78_HUMAN	HSPA5	16|14|11|12	53	13.25	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	19.16	132.50	0.00	
+WT_EGFR	K22E_HUMAN	KRT2	2|3|6|4	15	3.75	4	4|4|1|1	0.15	0.50	0.15	0.50	0.15	-5.61	1.50	0.44	
+WT_EGFR	K2C8_HUMAN	KRT8	1|1|2|2	6	1.50	4	0|0|0|0	0.49	0.99	0.49	0.99	0.49	2.40	15.00	0.19	
+WT_EGFR	K2C5_HUMAN	KRT5	1|1|3|2	7	1.75	4	1|1|2|0	0.29	0.74	0.29	0.74	0.29	-1.74	1.75	0.26	
+WT_EGFR	K2C4_HUMAN	KRT4	0|0|0|0	0	0.00	4	0|1|0|0	0.00	0.00	0.00	0.00	0.00	-0.65	0.00	0.51	
+WT_EGFR	K2C6B_HUMAN	KRT6B	1|2|4|4	11	2.75	4	2|2|2|1	0.25	0.47	0.25	0.47	0.25	-4.44	1.57	0.42	
+WT_EGFR	K2C7_HUMAN	KRT7	0|0|0|2	2	0.50	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	5.00	0.28	
+WT_EGFR	HSP7C_HUMAN	HSPA8	15|10|12|11	48	12.00	4	0|0|1|1	1.00	1.00	1.00	1.00	1.00	12.83	24.00	0.00	
+WT_EGFR	PK3CA_HUMAN	PIK3CA	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_EGFR	ERBB3_HUMAN	ERBB3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_EGFR	ADT2_HUMAN	SLC25A5	5|3|5|5	18	4.50	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	6.28	45.00	0.00	
+WT_EGFR	ADT3_HUMAN	SLC25A6	5|4|5|5	19	4.75	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	8.02	47.50	0.00	
+WT_EGFR	K1C9_HUMAN	KRT9	1|1|1|0	3	0.75	4	0|0|1|0	0.00	0.00	0.00	0.00	0.00	-0.65	3.00	0.51	
+WT_EGFR	TBA1A_HUMAN	TUBA1A	5|3|5|3	16	4.00	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	6.28	40.00	0.00	
+WT_EGFR	TBA4A_HUMAN	TUBA4A	3|2|3|1	9	2.25	4	0|0|0|0	0.75	1.00	0.75	1.00	0.75	2.40	22.50	0.02	
+WT_EGFR	ARHG5_HUMAN	ARHGEF5	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_EGFR	PK3CB_HUMAN	PIK3CB	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_EGFR	IRS1_HUMAN	IRS1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_EGFR	TBB5_HUMAN	TUBB	8|6|5|2	21	5.25	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	4.43	52.50	0.00	
+WT_EGFR	TBB3_HUMAN	TUBB3	2|2|3|1	8	2.00	4	0|0|0|0	0.74	1.00	0.74	1.00	0.74	2.40	20.00	0.05	
+WT_EGFR	HS90A_HUMAN	HSP90AA1	3|3|1|2	9	2.25	4	0|0|0|0	0.75	1.00	0.75	1.00	0.75	2.40	22.50	0.02	
+WT_EGFR	HS90B_HUMAN	HSP90AB1	4|2|1|2	9	2.25	4	0|0|0|0	0.74	1.00	0.74	1.00	0.74	2.40	22.50	0.04	
+WT_EGFR	1433E_HUMAN	YWHAE	1|0|0|0	1	0.25	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	2.50	0.51	
+WT_EGFR	1433T_HUMAN	YWHAQ	1|0|0|0	1	0.25	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	2.50	0.51	
+WT_EGFR	LPPRC_HUMAN	LRPPRC	1|3|3|3	10	2.50	4	0|0|0|0	0.75	1.00	0.75	1.00	0.75	2.40	25.00	0.00	
+WT_EGFR	ERRFI_HUMAN	ERRFI1	1|2|1|1	5	1.25	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	2.40	12.50	0.28	
+WT_EGFR	AP2A1_HUMAN	AP2A1	1|1|1|1	4	1.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	2.40	10.00	0.51	
+WT_EGFR	GRB7_HUMAN	GRB7	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_EGFR	STAT3_HUMAN	STAT3	4|5|3|3	15	3.75	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	6.28	37.50	0.00	
+WT_EGFR	ANXA2_HUMAN	ANXA2	3|4|0|1	8	2.00	4	0|0|0|0	0.50	1.00	0.50	1.00	0.50	-0.11	20.00	0.10	
+WT_EGFR	PTPRA_HUMAN	PTPRA	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_EGFR	C1QBP_HUMAN	C1QBP	0|0|0|0	0	0.00	4	7|5|0|0	0.00	0.00	0.00	0.00	0.00	-11.03	0.00	0.51	
+WT_EGFR	SHC1_HUMAN	SHC1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_EGFR	PTN11_HUMAN	PTPN11	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_EGFR	PON2_HUMAN	PON2	1|2|1|1	5	1.25	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	2.40	12.50	0.28	
+WT_EGFR	MET_HUMAN	MET	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_EGFR	CRKL_HUMAN	CRKL	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_EGFR	CBL_HUMAN	CBL	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_EGFR	PHLA2_HUMAN	PHLDA2	1|1|0|1	3	0.75	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	7.50	0.51	
+WT_EGFR	RT27_HUMAN	MRPS27	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_EGFR	M2OM_HUMAN	SLC25A11	1|0|1|2	4	1.00	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	10.00	0.28	
+WT_EGFR	STAT1_HUMAN	STAT1	3|2|1|1	7	1.75	4	0|0|0|0	0.50	1.00	0.50	1.00	0.50	2.40	17.50	0.14	
+WT_EGFR	SLIRP_HUMAN	SLIRP	2|1|2|1	6	1.50	4	0|0|0|0	0.49	0.99	0.49	0.99	0.49	2.40	15.00	0.19	
+WT_EGFR	PK3CD_HUMAN	PIK3CD	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_EGFR	GRP75_HUMAN	HSPA9	1|2|1|1	5	1.25	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	2.40	12.50	0.28	
+WT_EGFR	AIFM1_HUMAN	AIFM1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_EGFR	PHB_HUMAN	PHB	2|1|1|0	4	1.00	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	10.00	0.28	
+WT_EGFR	HNRPK_HUMAN	HNRNPK	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_EGFR	TMM33_HUMAN	TMEM33	2|0|1|0	3	0.75	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	7.50	0.28	
+WT_EGFR	RT34_HUMAN	MRPS34	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	EGFR_HUMAN	EGFR	5|3|4|5	17	4.25	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	6.28	42.50	0.00	
+WT_ERBB3	ERBB2_HUMAN	ERBB2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	P85B_HUMAN	PIK3R2	4|4|4|3	15	3.75	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	6.28	37.50	0.00	
+WT_ERBB3	P55G_HUMAN	PIK3R3	2|2|3|3	10	2.50	4	0|0|0|0	0.99	1.00	0.99	1.00	0.99	4.43	25.00	0.00	
+WT_ERBB3	P85A_HUMAN	PIK3R1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	K2C1_HUMAN	KRT1	8|8|6|6	28	7.00	4	10|10|7|7	0.00	0.00	0.00	0.00	0.00	-21.15	0.82	0.51	
+WT_ERBB3	K1C10_HUMAN	KRT10	5|5|3|2	15	3.75	4	1|2|4|3	0.11	0.21	0.11	0.21	0.11	-5.60	1.50	0.45	
+WT_ERBB3	K1C16_HUMAN	KRT16	0|1|0|0	1	0.25	4	1|1|2|2	0.00	0.00	0.00	0.00	0.00	-5.35	0.17	0.51	
+WT_ERBB3	GRB2_HUMAN	GRB2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	GRP78_HUMAN	HSPA5	4|3|6|3	16	4.00	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	6.28	40.00	0.00	
+WT_ERBB3	K22E_HUMAN	KRT2	5|5|4|4	18	4.50	4	4|4|1|1	0.14	0.21	0.14	0.21	0.14	-2.71	1.80	0.44	
+WT_ERBB3	K2C8_HUMAN	KRT8	0|0|0|1	1	0.25	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	2.50	0.51	
+WT_ERBB3	K2C5_HUMAN	KRT5	3|2|2|2	9	2.25	4	1|1|2|0	0.51	0.74	0.51	0.74	0.51	-0.28	2.25	0.07	
+WT_ERBB3	K2C4_HUMAN	KRT4	0|0|0|0	0	0.00	4	0|1|0|0	0.00	0.00	0.00	0.00	0.00	-0.65	0.00	0.51	
+WT_ERBB3	K2C6B_HUMAN	KRT6B	3|3|3|3	12	3.00	4	2|2|2|1	0.18	0.18	0.18	0.18	0.18	-1.49	1.71	0.44	
+WT_ERBB3	K2C7_HUMAN	KRT7	0|0|0|1	1	0.25	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	2.50	0.51	
+WT_ERBB3	HSP7C_HUMAN	HSPA8	5|4|6|5	20	5.00	4	0|0|1|1	0.99	1.00	0.99	1.00	0.99	4.13	10.00	0.00	
+WT_ERBB3	PK3CA_HUMAN	PIK3CA	3|2|3|3	11	2.75	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	4.43	27.50	0.00	
+WT_ERBB3	ERBB3_HUMAN	ERBB3	4|5|7|6	22	5.50	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	8.02	55.00	0.00	
+WT_ERBB3	ADT2_HUMAN	SLC25A5	2|1|2|1	6	1.50	4	0|0|0|0	0.49	0.99	0.49	0.99	0.49	2.40	15.00	0.19	
+WT_ERBB3	ADT3_HUMAN	SLC25A6	1|0|2|1	4	1.00	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	10.00	0.28	
+WT_ERBB3	K1C9_HUMAN	KRT9	1|2|0|1	4	1.00	4	0|0|1|0	0.23	0.93	0.23	0.93	0.23	-0.65	4.00	0.42	
+WT_ERBB3	TBA1A_HUMAN	TUBA1A	1|1|2|2	6	1.50	4	0|0|0|0	0.49	0.99	0.49	0.99	0.49	2.40	15.00	0.19	
+WT_ERBB3	TBA4A_HUMAN	TUBA4A	0|0|1|1	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+WT_ERBB3	ARHG5_HUMAN	ARHGEF5	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	PK3CB_HUMAN	PIK3CB	0|1|1|1	3	0.75	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	7.50	0.51	
+WT_ERBB3	IRS1_HUMAN	IRS1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	TBB5_HUMAN	TUBB	0|1|2|1	4	1.00	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	10.00	0.28	
+WT_ERBB3	TBB3_HUMAN	TUBB3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	HS90A_HUMAN	HSP90AA1	1|1|1|1	4	1.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	2.40	10.00	0.51	
+WT_ERBB3	HS90B_HUMAN	HSP90AB1	1|1|1|1	4	1.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	2.40	10.00	0.51	
+WT_ERBB3	1433E_HUMAN	YWHAE	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	1433T_HUMAN	YWHAQ	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	LPPRC_HUMAN	LRPPRC	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	ERRFI_HUMAN	ERRFI1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	AP2A1_HUMAN	AP2A1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	GRB7_HUMAN	GRB7	0|1|1|1	3	0.75	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	7.50	0.51	
+WT_ERBB3	STAT3_HUMAN	STAT3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	ANXA2_HUMAN	ANXA2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	PTPRA_HUMAN	PTPRA	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	C1QBP_HUMAN	C1QBP	0|0|0|0	0	0.00	4	7|5|0|0	0.00	0.00	0.00	0.00	0.00	-11.03	0.00	0.51	
+WT_ERBB3	SHC1_HUMAN	SHC1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	PTN11_HUMAN	PTPN11	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	PON2_HUMAN	PON2	1|1|1|0	3	0.75	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	7.50	0.51	
+WT_ERBB3	MET_HUMAN	MET	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	CRKL_HUMAN	CRKL	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	CBL_HUMAN	CBL	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	PHLA2_HUMAN	PHLDA2	2|0|0|1	3	0.75	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	7.50	0.28	
+WT_ERBB3	RT27_HUMAN	MRPS27	1|2|1|1	5	1.25	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	2.40	12.50	0.28	
+WT_ERBB3	M2OM_HUMAN	SLC25A11	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	STAT1_HUMAN	STAT1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	SLIRP_HUMAN	SLIRP	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	PK3CD_HUMAN	PIK3CD	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	GRP75_HUMAN	HSPA9	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	AIFM1_HUMAN	AIFM1	1|1|1|2	5	1.25	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	2.40	12.50	0.28	
+WT_ERBB3	PHB_HUMAN	PHB	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	HNRPK_HUMAN	HNRNPK	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	TMM33_HUMAN	TMEM33	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_ERBB3	RT34_HUMAN	MRPS34	1|0|1|2	4	1.00	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	10.00	0.28	
+WT_GRB2	EGFR_HUMAN	EGFR	19|18|36|36	109	27.25	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	29.68	272.50	0.00	
+WT_GRB2	ERBB2_HUMAN	ERBB2	0|0|3|1	4	1.00	4	0|0|0|0	0.25	1.00	0.25	1.00	0.25	-0.11	10.00	0.27	
+WT_GRB2	P85B_HUMAN	PIK3R2	2|0|3|3	8	2.00	4	0|0|0|0	0.75	1.00	0.75	1.00	0.75	-0.11	20.00	0.02	
+WT_GRB2	P55G_HUMAN	PIK3R3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	P85A_HUMAN	PIK3R1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	K2C1_HUMAN	KRT1	10|7|13|10	40	10.00	4	10|10|7|7	0.00	0.00	0.00	0.00	0.00	-19.52	1.18	0.50	
+WT_GRB2	K1C10_HUMAN	KRT10	3|3|9|9	24	6.00	4	1|2|4|3	0.50	0.97	0.50	0.97	0.50	-4.12	2.40	0.18	
+WT_GRB2	K1C16_HUMAN	KRT16	0|0|0|0	0	0.00	4	1|1|2|2	0.00	0.00	0.00	0.00	0.00	-5.35	0.00	0.51	
+WT_GRB2	GRB2_HUMAN	GRB2	9|6|10|11	36	9.00	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	11.34	90.00	0.00	
+WT_GRB2	GRP78_HUMAN	HSPA5	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	K22E_HUMAN	KRT2	4|5|4|4	17	4.25	4	4|4|1|1	0.10	0.21	0.10	0.21	0.10	-2.71	1.70	0.45	
+WT_GRB2	K2C8_HUMAN	KRT8	0|0|2|0	2	0.50	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	5.00	0.28	
+WT_GRB2	K2C5_HUMAN	KRT5	2|2|2|1	7	1.75	4	1|1|2|0	0.32	0.43	0.32	0.43	0.32	-1.74	1.75	0.25	
+WT_GRB2	K2C4_HUMAN	KRT4	0|0|0|0	0	0.00	4	0|1|0|0	0.00	0.00	0.00	0.00	0.00	-0.65	0.00	0.51	
+WT_GRB2	K2C6B_HUMAN	KRT6B	2|2|3|2	9	2.25	4	2|2|2|1	0.08	0.18	0.08	0.18	0.08	-2.92	1.29	0.46	
+WT_GRB2	K2C7_HUMAN	KRT7	0|0|2|0	2	0.50	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	5.00	0.28	
+WT_GRB2	HSP7C_HUMAN	HSPA8	2|2|1|1	6	1.50	4	0|0|1|1	0.40	0.80	0.40	0.80	0.40	0.06	3.00	0.25	
+WT_GRB2	PK3CA_HUMAN	PIK3CA	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	ERBB3_HUMAN	ERBB3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	ADT2_HUMAN	SLC25A5	2|3|3|3	11	2.75	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	4.43	27.50	0.00	
+WT_GRB2	ADT3_HUMAN	SLC25A6	1|2|2|2	7	1.75	4	0|0|0|0	0.74	0.99	0.74	0.99	0.74	2.40	17.50	0.05	
+WT_GRB2	K1C9_HUMAN	KRT9	3|1|2|1	7	1.75	4	0|0|1|0	0.48	0.99	0.48	0.99	0.48	0.96	7.00	0.24	
+WT_GRB2	TBA1A_HUMAN	TUBA1A	0|0|2|0	2	0.50	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	5.00	0.28	
+WT_GRB2	TBA4A_HUMAN	TUBA4A	0|0|2|1	3	0.75	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	7.50	0.28	
+WT_GRB2	ARHG5_HUMAN	ARHGEF5	5|7|12|11	35	8.75	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	9.70	87.50	0.00	
+WT_GRB2	PK3CB_HUMAN	PIK3CB	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	IRS1_HUMAN	IRS1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	TBB5_HUMAN	TUBB	1|1|2|1	5	1.25	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	2.40	12.50	0.28	
+WT_GRB2	TBB3_HUMAN	TUBB3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	HS90A_HUMAN	HSP90AA1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	HS90B_HUMAN	HSP90AB1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	1433E_HUMAN	YWHAE	0|0|2|0	2	0.50	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	5.00	0.28	
+WT_GRB2	1433T_HUMAN	YWHAQ	0|0|2|1	3	0.75	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	7.50	0.28	
+WT_GRB2	LPPRC_HUMAN	LRPPRC	0|1|0|0	1	0.25	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	2.50	0.51	
+WT_GRB2	ERRFI_HUMAN	ERRFI1	0|0|2|1	3	0.75	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	7.50	0.28	
+WT_GRB2	AP2A1_HUMAN	AP2A1	3|3|2|3	11	2.75	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	4.43	27.50	0.00	
+WT_GRB2	GRB7_HUMAN	GRB7	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	STAT3_HUMAN	STAT3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	ANXA2_HUMAN	ANXA2	0|0|3|3	6	1.50	4	0|0|0|0	0.50	1.00	0.50	1.00	0.50	-0.11	15.00	0.12	
+WT_GRB2	PTPRA_HUMAN	PTPRA	0|3|3|3	9	2.25	4	0|0|0|0	0.75	1.00	0.75	1.00	0.75	-0.11	22.50	0.00	
+WT_GRB2	C1QBP_HUMAN	C1QBP	0|0|0|0	0	0.00	4	7|5|0|0	0.00	0.00	0.00	0.00	0.00	-11.03	0.00	0.51	
+WT_GRB2	SHC1_HUMAN	SHC1	2|3|3|2	10	2.50	4	0|0|0|0	0.99	1.00	0.99	1.00	0.99	4.43	25.00	0.00	
+WT_GRB2	PTN11_HUMAN	PTPN11	0|0|1|1	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+WT_GRB2	PON2_HUMAN	PON2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	MET_HUMAN	MET	0|0|1|1	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+WT_GRB2	CRKL_HUMAN	CRKL	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	CBL_HUMAN	CBL	0|0|1|0	1	0.25	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	2.50	0.51	
+WT_GRB2	PHLA2_HUMAN	PHLDA2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	RT27_HUMAN	MRPS27	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	M2OM_HUMAN	SLC25A11	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	STAT1_HUMAN	STAT1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	SLIRP_HUMAN	SLIRP	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	PK3CD_HUMAN	PIK3CD	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	GRP75_HUMAN	HSPA9	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	AIFM1_HUMAN	AIFM1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	PHB_HUMAN	PHB	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	HNRPK_HUMAN	HNRNPK	0|1|1|2	4	1.00	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	10.00	0.28	
+WT_GRB2	TMM33_HUMAN	TMEM33	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_GRB2	RT34_HUMAN	MRPS34	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	EGFR_HUMAN	EGFR	7|6|12|7	32	8.00	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	11.34	80.00	0.00	
+WT_P85B	ERBB2_HUMAN	ERBB2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	P85B_HUMAN	PIK3R2	26|41|47|39	153	38.25	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	41.42	382.50	0.00	
+WT_P85B	P55G_HUMAN	PIK3R3	3|4|2|3	12	3.00	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	4.43	30.00	0.00	
+WT_P85B	P85A_HUMAN	PIK3R1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	K2C1_HUMAN	KRT1	16|14|9|7	46	11.50	4	10|10|7|7	0.00	0.00	0.00	0.00	0.00	-19.52	1.35	0.50	
+WT_P85B	K1C10_HUMAN	KRT10	11|8|2|3	24	6.00	4	1|2|4|3	0.48	1.00	0.48	1.00	0.48	-5.60	2.40	0.23	
+WT_P85B	K1C16_HUMAN	KRT16	2|1|0|0	3	0.75	4	1|1|2|2	0.03	0.12	0.03	0.12	0.03	-5.35	0.50	0.47	
+WT_P85B	GRB2_HUMAN	GRB2	0|1|1|1	3	0.75	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	7.50	0.51	
+WT_P85B	GRP78_HUMAN	HSPA5	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	K22E_HUMAN	KRT2	8|8|3|4	23	5.75	4	4|4|1|1	0.48	0.93	0.48	0.93	0.48	-4.13	2.30	0.23	
+WT_P85B	K2C8_HUMAN	KRT8	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	K2C5_HUMAN	KRT5	0|2|0|0	2	0.50	4	1|1|2|0	0.11	0.43	0.11	0.43	0.11	-3.14	0.50	0.45	
+WT_P85B	K2C4_HUMAN	KRT4	4|3|0|0	7	1.75	4	0|1|0|0	0.50	1.00	0.50	1.00	0.50	-0.65	7.00	0.18	
+WT_P85B	K2C6B_HUMAN	KRT6B	4|4|2|0	10	2.50	4	2|2|2|1	0.25	0.47	0.25	0.47	0.25	-6.40	1.43	0.42	
+WT_P85B	K2C7_HUMAN	KRT7	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	HSP7C_HUMAN	HSPA8	1|0|0|0	1	0.25	4	0|0|1|1	0.00	0.00	0.00	0.00	0.00	-1.55	0.50	0.51	
+WT_P85B	PK3CA_HUMAN	PIK3CA	6|7|11|10	34	8.50	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	11.34	85.00	0.00	
+WT_P85B	ERBB3_HUMAN	ERBB3	2|2|4|4	12	3.00	4	0|0|0|0	0.99	1.00	0.99	1.00	0.99	4.43	30.00	0.00	
+WT_P85B	ADT2_HUMAN	SLC25A5	1|0|1|0	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+WT_P85B	ADT3_HUMAN	SLC25A6	0|0|1|0	1	0.25	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	2.50	0.51	
+WT_P85B	K1C9_HUMAN	KRT9	4|2|0|1	7	1.75	4	0|0|1|0	0.48	1.00	0.48	1.00	0.48	-0.65	7.00	0.24	
+WT_P85B	TBA1A_HUMAN	TUBA1A	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	TBA4A_HUMAN	TUBA4A	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	ARHG5_HUMAN	ARHGEF5	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	PK3CB_HUMAN	PIK3CB	4|2|5|4	15	3.75	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	4.43	37.50	0.00	
+WT_P85B	IRS1_HUMAN	IRS1	3|2|6|3	14	3.50	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	4.43	35.00	0.00	
+WT_P85B	TBB5_HUMAN	TUBB	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	TBB3_HUMAN	TUBB3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	HS90A_HUMAN	HSP90AA1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	HS90B_HUMAN	HSP90AB1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	1433E_HUMAN	YWHAE	2|2|1|1	6	1.50	4	0|0|0|0	0.49	0.99	0.49	0.99	0.49	2.40	15.00	0.19	
+WT_P85B	1433T_HUMAN	YWHAQ	1|1|2|1	5	1.25	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	2.40	12.50	0.28	
+WT_P85B	LPPRC_HUMAN	LRPPRC	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	ERRFI_HUMAN	ERRFI1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	AP2A1_HUMAN	AP2A1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	GRB7_HUMAN	GRB7	1|1|5|3	10	2.50	4	0|0|0|0	0.50	1.00	0.50	1.00	0.50	2.40	25.00	0.09	
+WT_P85B	STAT3_HUMAN	STAT3	0|0|0|1	1	0.25	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	2.50	0.51	
+WT_P85B	ANXA2_HUMAN	ANXA2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	PTPRA_HUMAN	PTPRA	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	C1QBP_HUMAN	C1QBP	0|0|0|0	0	0.00	4	7|5|0|0	0.00	0.00	0.00	0.00	0.00	-11.03	0.00	0.51	
+WT_P85B	SHC1_HUMAN	SHC1	0|0|1|0	1	0.25	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	2.50	0.51	
+WT_P85B	PTN11_HUMAN	PTPN11	1|1|2|1	5	1.25	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	2.40	12.50	0.28	
+WT_P85B	PON2_HUMAN	PON2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	MET_HUMAN	MET	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	CRKL_HUMAN	CRKL	2|1|1|2	6	1.50	4	0|0|0|0	0.49	0.99	0.49	0.99	0.49	2.40	15.00	0.19	
+WT_P85B	CBL_HUMAN	CBL	0|2|2|0	4	1.00	4	0|0|0|0	0.49	0.99	0.49	0.99	0.49	-0.11	10.00	0.19	
+WT_P85B	PHLA2_HUMAN	PHLDA2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	RT27_HUMAN	MRPS27	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	M2OM_HUMAN	SLC25A11	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	STAT1_HUMAN	STAT1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	SLIRP_HUMAN	SLIRP	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	PK3CD_HUMAN	PIK3CD	2|2|2|1	7	1.75	4	0|0|0|0	0.74	0.99	0.74	0.99	0.74	2.40	17.50	0.05	
+WT_P85B	GRP75_HUMAN	HSPA9	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	AIFM1_HUMAN	AIFM1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	PHB_HUMAN	PHB	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	HNRPK_HUMAN	HNRNPK	0|0|1|0	1	0.25	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	2.50	0.51	
+WT_P85B	TMM33_HUMAN	TMEM33	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+WT_P85B	RT34_HUMAN	MRPS34	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	EGFR_HUMAN	EGFR	36|29|12|9	86	21.50	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	16.08	215.00	0.00	
+ER_EGFR	ERBB2_HUMAN	ERBB2	2|1|0|0	3	0.75	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	7.50	0.28	
+ER_EGFR	P85B_HUMAN	PIK3R2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	P55G_HUMAN	PIK3R3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	P85A_HUMAN	PIK3R1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	K2C1_HUMAN	KRT1	17|17|1|1	36	9.00	4	10|10|7|7	0.00	0.00	0.00	0.00	0.00	-32.37	1.06	0.49	
+ER_EGFR	K1C10_HUMAN	KRT10	10|11|1|1	23	5.75	4	1|2|4|3	0.50	1.00	0.50	1.00	0.50	-7.24	2.30	0.13	
+ER_EGFR	K1C16_HUMAN	KRT16	0|0|0|0	0	0.00	4	1|1|2|2	0.00	0.00	0.00	0.00	0.00	-5.35	0.00	0.51	
+ER_EGFR	GRB2_HUMAN	GRB2	1|1|0|0	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+ER_EGFR	GRP78_HUMAN	HSPA5	9|9|2|1	21	5.25	4	0|0|0|0	0.75	1.00	0.75	1.00	0.75	2.40	52.50	0.02	
+ER_EGFR	K22E_HUMAN	KRT2	7|5|1|1	14	3.50	4	4|4|1|1	0.25	0.79	0.25	0.79	0.25	-7.25	1.40	0.28	
+ER_EGFR	K2C8_HUMAN	KRT8	3|2|0|0	5	1.25	4	0|0|0|0	0.50	1.00	0.50	1.00	0.50	-0.11	12.50	0.14	
+ER_EGFR	K2C5_HUMAN	KRT5	3|2|0|0	5	1.25	4	1|1|2|0	0.29	0.74	0.29	0.74	0.29	-3.14	1.25	0.26	
+ER_EGFR	K2C4_HUMAN	KRT4	2|0|0|0	2	0.50	4	0|1|0|0	0.23	0.93	0.23	0.93	0.23	-0.65	2.00	0.42	
+ER_EGFR	K2C6B_HUMAN	KRT6B	5|3|0|0	8	2.00	4	2|2|2|1	0.24	0.76	0.24	0.76	0.24	-6.40	1.14	0.42	
+ER_EGFR	K2C7_HUMAN	KRT7	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	HSP7C_HUMAN	HSPA8	10|15|2|2	29	7.25	4	0|0|1|1	0.90	1.00	0.90	1.00	0.90	1.39	14.50	0.00	
+ER_EGFR	PK3CA_HUMAN	PIK3CA	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	ERBB3_HUMAN	ERBB3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	ADT2_HUMAN	SLC25A5	3|2|0|0	5	1.25	4	0|0|0|0	0.50	1.00	0.50	1.00	0.50	-0.11	12.50	0.14	
+ER_EGFR	ADT3_HUMAN	SLC25A6	3|2|0|0	5	1.25	4	0|0|0|0	0.50	1.00	0.50	1.00	0.50	-0.11	12.50	0.14	
+ER_EGFR	K1C9_HUMAN	KRT9	3|5|0|0	8	2.00	4	0|0|1|0	0.50	1.00	0.50	1.00	0.50	-0.65	8.00	0.17	
+ER_EGFR	TBA1A_HUMAN	TUBA1A	1|1|0|0	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+ER_EGFR	TBA4A_HUMAN	TUBA4A	1|2|0|0	3	0.75	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	7.50	0.28	
+ER_EGFR	ARHG5_HUMAN	ARHGEF5	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	PK3CB_HUMAN	PIK3CB	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	IRS1_HUMAN	IRS1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	TBB5_HUMAN	TUBB	3|1|0|0	4	1.00	4	0|0|0|0	0.25	1.00	0.25	1.00	0.25	-0.11	10.00	0.27	
+ER_EGFR	TBB3_HUMAN	TUBB3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	HS90A_HUMAN	HSP90AA1	2|2|0|0	4	1.00	4	0|0|0|0	0.49	0.99	0.49	0.99	0.49	-0.11	10.00	0.19	
+ER_EGFR	HS90B_HUMAN	HSP90AB1	1|1|0|0	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+ER_EGFR	1433E_HUMAN	YWHAE	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	1433T_HUMAN	YWHAQ	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	LPPRC_HUMAN	LRPPRC	1|1|0|0	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+ER_EGFR	ERRFI_HUMAN	ERRFI1	1|2|0|0	3	0.75	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	7.50	0.28	
+ER_EGFR	AP2A1_HUMAN	AP2A1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	GRB7_HUMAN	GRB7	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	STAT3_HUMAN	STAT3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	ANXA2_HUMAN	ANXA2	2|0|0|0	2	0.50	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	5.00	0.28	
+ER_EGFR	PTPRA_HUMAN	PTPRA	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	C1QBP_HUMAN	C1QBP	0|1|0|0	1	0.25	4	7|5|0|0	0.00	0.00	0.00	0.00	0.00	-11.03	0.08	0.51	
+ER_EGFR	SHC1_HUMAN	SHC1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	PTN11_HUMAN	PTPN11	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	PON2_HUMAN	PON2	1|2|0|0	3	0.75	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	7.50	0.28	
+ER_EGFR	MET_HUMAN	MET	1|1|0|0	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+ER_EGFR	CRKL_HUMAN	CRKL	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	CBL_HUMAN	CBL	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	PHLA2_HUMAN	PHLDA2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	RT27_HUMAN	MRPS27	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	M2OM_HUMAN	SLC25A11	0|2|0|0	2	0.50	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	5.00	0.28	
+ER_EGFR	STAT1_HUMAN	STAT1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	SLIRP_HUMAN	SLIRP	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	PK3CD_HUMAN	PIK3CD	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	GRP75_HUMAN	HSPA9	0|1|0|0	1	0.25	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	2.50	0.51	
+ER_EGFR	AIFM1_HUMAN	AIFM1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	PHB_HUMAN	PHB	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	HNRPK_HUMAN	HNRNPK	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_EGFR	TMM33_HUMAN	TMEM33	0|2|0|0	2	0.50	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	5.00	0.28	
+ER_EGFR	RT34_HUMAN	MRPS34	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	EGFR_HUMAN	EGFR	0|0|3|3	6	1.50	4	0|0|0|0	0.50	1.00	0.50	1.00	0.50	-0.11	15.00	0.12	
+ER_ERBB3	ERBB2_HUMAN	ERBB2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	P85B_HUMAN	PIK3R2	0|0|3|4	7	1.75	4	0|0|0|0	0.50	1.00	0.50	1.00	0.50	-0.11	17.50	0.10	
+ER_ERBB3	P55G_HUMAN	PIK3R3	0|0|1|1	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+ER_ERBB3	P85A_HUMAN	PIK3R1	0|0|1|0	1	0.25	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	2.50	0.51	
+ER_ERBB3	K2C1_HUMAN	KRT1	5|5|6|5	21	5.25	4	10|10|7|7	0.00	0.00	0.00	0.00	0.00	-22.93	0.62	0.51	
+ER_ERBB3	K1C10_HUMAN	KRT10	3|3|2|2	10	2.50	4	1|2|4|3	0.01	0.02	0.01	0.02	0.01	-5.60	1.00	0.49	
+ER_ERBB3	K1C16_HUMAN	KRT16	0|0|0|0	0	0.00	4	1|1|2|2	0.00	0.00	0.00	0.00	0.00	-5.35	0.00	0.51	
+ER_ERBB3	GRB2_HUMAN	GRB2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	GRP78_HUMAN	HSPA5	0|0|2|2	4	1.00	4	0|0|0|0	0.49	0.99	0.49	0.99	0.49	-0.11	10.00	0.19	
+ER_ERBB3	K22E_HUMAN	KRT2	3|3|4|3	13	3.25	4	4|4|1|1	0.03	0.06	0.03	0.06	0.03	-4.13	1.30	0.48	
+ER_ERBB3	K2C8_HUMAN	KRT8	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	K2C5_HUMAN	KRT5	0|0|1|1	2	0.50	4	1|1|2|0	0.00	0.00	0.00	0.00	0.00	-3.14	0.50	0.51	
+ER_ERBB3	K2C4_HUMAN	KRT4	0|0|0|1	1	0.25	4	0|1|0|0	0.00	0.00	0.00	0.00	0.00	-0.65	1.00	0.51	
+ER_ERBB3	K2C6B_HUMAN	KRT6B	1|0|2|1	4	1.00	4	2|2|2|1	0.01	0.05	0.01	0.05	0.01	-6.40	0.57	0.48	
+ER_ERBB3	K2C7_HUMAN	KRT7	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	HSP7C_HUMAN	HSPA8	0|1|1|2	4	1.00	4	0|0|1|1	0.20	0.80	0.20	0.80	0.20	-1.55	2.00	0.43	
+ER_ERBB3	PK3CA_HUMAN	PIK3CA	0|0|1|1	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+ER_ERBB3	ERBB3_HUMAN	ERBB3	1|2|2|3	8	2.00	4	0|0|0|0	0.74	1.00	0.74	1.00	0.74	2.40	20.00	0.05	
+ER_ERBB3	ADT2_HUMAN	SLC25A5	0|0|1|1	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+ER_ERBB3	ADT3_HUMAN	SLC25A6	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	K1C9_HUMAN	KRT9	3|2|1|1	7	1.75	4	0|0|1|0	0.48	0.99	0.48	0.99	0.48	0.96	7.00	0.24	
+ER_ERBB3	TBA1A_HUMAN	TUBA1A	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	TBA4A_HUMAN	TUBA4A	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	ARHG5_HUMAN	ARHGEF5	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	PK3CB_HUMAN	PIK3CB	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	IRS1_HUMAN	IRS1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	TBB5_HUMAN	TUBB	0|0|0|1	1	0.25	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	2.50	0.51	
+ER_ERBB3	TBB3_HUMAN	TUBB3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	HS90A_HUMAN	HSP90AA1	0|0|1|1	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+ER_ERBB3	HS90B_HUMAN	HSP90AB1	0|0|1|1	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+ER_ERBB3	1433E_HUMAN	YWHAE	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	1433T_HUMAN	YWHAQ	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	LPPRC_HUMAN	LRPPRC	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	ERRFI_HUMAN	ERRFI1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	AP2A1_HUMAN	AP2A1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	GRB7_HUMAN	GRB7	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	STAT3_HUMAN	STAT3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	ANXA2_HUMAN	ANXA2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	PTPRA_HUMAN	PTPRA	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	C1QBP_HUMAN	C1QBP	0|0|0|0	0	0.00	4	7|5|0|0	0.00	0.00	0.00	0.00	0.00	-11.03	0.00	0.51	
+ER_ERBB3	SHC1_HUMAN	SHC1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	PTN11_HUMAN	PTPN11	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	PON2_HUMAN	PON2	0|0|1|0	1	0.25	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	2.50	0.51	
+ER_ERBB3	MET_HUMAN	MET	0|0|1|0	1	0.25	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	2.50	0.51	
+ER_ERBB3	CRKL_HUMAN	CRKL	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	CBL_HUMAN	CBL	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	PHLA2_HUMAN	PHLDA2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	RT27_HUMAN	MRPS27	0|0|1|1	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+ER_ERBB3	M2OM_HUMAN	SLC25A11	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	STAT1_HUMAN	STAT1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	SLIRP_HUMAN	SLIRP	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	PK3CD_HUMAN	PIK3CD	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	GRP75_HUMAN	HSPA9	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	AIFM1_HUMAN	AIFM1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	PHB_HUMAN	PHB	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	HNRPK_HUMAN	HNRNPK	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	TMM33_HUMAN	TMEM33	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_ERBB3	RT34_HUMAN	MRPS34	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	EGFR_HUMAN	EGFR	5|8|0|0	13	3.25	4	0|0|0|0	0.50	1.00	0.50	1.00	0.50	-0.11	32.50	0.08	
+ER_GRB2	ERBB2_HUMAN	ERBB2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	P85B_HUMAN	PIK3R2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	P55G_HUMAN	PIK3R3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	P85A_HUMAN	PIK3R1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	K2C1_HUMAN	KRT1	6|5|0|0	11	2.75	4	10|10|7|7	0.00	0.00	0.00	0.00	0.00	-35.92	0.32	0.51	
+ER_GRB2	K1C10_HUMAN	KRT10	0|2|0|0	2	0.50	4	1|2|4|3	0.00	0.00	0.00	0.00	0.00	-9.56	0.20	0.51	
+ER_GRB2	K1C16_HUMAN	KRT16	1|2|0|0	3	0.75	4	1|1|2|2	0.03	0.12	0.03	0.12	0.03	-5.35	0.50	0.47	
+ER_GRB2	GRB2_HUMAN	GRB2	7|7|0|0	14	3.50	4	0|0|0|0	0.50	1.00	0.50	1.00	0.50	-0.11	35.00	0.08	
+ER_GRB2	GRP78_HUMAN	HSPA5	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	K22E_HUMAN	KRT2	2|1|0|0	3	0.75	4	4|4|1|1	0.00	0.00	0.00	0.00	0.00	-9.57	0.30	0.51	
+ER_GRB2	K2C8_HUMAN	KRT8	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	K2C5_HUMAN	KRT5	0|0|0|0	0	0.00	4	1|1|2|0	0.00	0.00	0.00	0.00	0.00	-3.14	0.00	0.51	
+ER_GRB2	K2C4_HUMAN	KRT4	0|0|0|0	0	0.00	4	0|1|0|0	0.00	0.00	0.00	0.00	0.00	-0.65	0.00	0.51	
+ER_GRB2	K2C6B_HUMAN	KRT6B	0|0|0|0	0	0.00	4	2|2|2|1	0.00	0.00	0.00	0.00	0.00	-6.40	0.00	0.51	
+ER_GRB2	K2C7_HUMAN	KRT7	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	HSP7C_HUMAN	HSPA8	1|0|0|0	1	0.25	4	0|0|1|1	0.00	0.00	0.00	0.00	0.00	-1.55	0.50	0.51	
+ER_GRB2	PK3CA_HUMAN	PIK3CA	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	ERBB3_HUMAN	ERBB3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	ADT2_HUMAN	SLC25A5	1|0|0|0	1	0.25	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	2.50	0.51	
+ER_GRB2	ADT3_HUMAN	SLC25A6	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	K1C9_HUMAN	KRT9	1|1|0|0	2	0.50	4	0|0|1|0	0.00	0.00	0.00	0.00	0.00	-0.65	2.00	0.51	
+ER_GRB2	TBA1A_HUMAN	TUBA1A	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	TBA4A_HUMAN	TUBA4A	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	ARHG5_HUMAN	ARHGEF5	1|1|0|0	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+ER_GRB2	PK3CB_HUMAN	PIK3CB	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	IRS1_HUMAN	IRS1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	TBB5_HUMAN	TUBB	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	TBB3_HUMAN	TUBB3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	HS90A_HUMAN	HSP90AA1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	HS90B_HUMAN	HSP90AB1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	1433E_HUMAN	YWHAE	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	1433T_HUMAN	YWHAQ	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	LPPRC_HUMAN	LRPPRC	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	ERRFI_HUMAN	ERRFI1	0|1|0|0	1	0.25	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	2.50	0.51	
+ER_GRB2	AP2A1_HUMAN	AP2A1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	GRB7_HUMAN	GRB7	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	STAT3_HUMAN	STAT3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	ANXA2_HUMAN	ANXA2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	PTPRA_HUMAN	PTPRA	3|2|0|0	5	1.25	4	0|0|0|0	0.50	1.00	0.50	1.00	0.50	-0.11	12.50	0.14	
+ER_GRB2	C1QBP_HUMAN	C1QBP	0|0|0|0	0	0.00	4	7|5|0|0	0.00	0.00	0.00	0.00	0.00	-11.03	0.00	0.51	
+ER_GRB2	SHC1_HUMAN	SHC1	1|1|0|0	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+ER_GRB2	PTN11_HUMAN	PTPN11	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	PON2_HUMAN	PON2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	MET_HUMAN	MET	2|2|0|0	4	1.00	4	0|0|0|0	0.49	0.99	0.49	0.99	0.49	-0.11	10.00	0.19	
+ER_GRB2	CRKL_HUMAN	CRKL	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	CBL_HUMAN	CBL	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	PHLA2_HUMAN	PHLDA2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	RT27_HUMAN	MRPS27	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	M2OM_HUMAN	SLC25A11	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	STAT1_HUMAN	STAT1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	SLIRP_HUMAN	SLIRP	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	PK3CD_HUMAN	PIK3CD	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	GRP75_HUMAN	HSPA9	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	AIFM1_HUMAN	AIFM1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	PHB_HUMAN	PHB	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	HNRPK_HUMAN	HNRNPK	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	TMM33_HUMAN	TMEM33	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_GRB2	RT34_HUMAN	MRPS34	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	EGFR_HUMAN	EGFR	1|1|1|2	5	1.25	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	2.40	12.50	0.28	
+ER_P85B	ERBB2_HUMAN	ERBB2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	P85B_HUMAN	PIK3R2	15|13|16|17	61	15.25	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	22.20	152.50	0.00	
+ER_P85B	P55G_HUMAN	PIK3R3	2|2|3|2	9	2.25	4	0|0|0|0	0.99	1.00	0.99	1.00	0.99	4.43	22.50	0.00	
+ER_P85B	P85A_HUMAN	PIK3R1	3|2|4|3	12	3.00	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	4.43	30.00	0.00	
+ER_P85B	K2C1_HUMAN	KRT1	5|4|10|8	27	6.75	4	10|10|7|7	0.00	0.00	0.00	0.00	0.00	-24.89	0.79	0.50	
+ER_P85B	K1C10_HUMAN	KRT10	0|1|5|4	10	2.50	4	1|2|4|3	0.07	0.21	0.07	0.21	0.07	-9.56	1.00	0.46	
+ER_P85B	K1C16_HUMAN	KRT16	1|0|3|3	7	1.75	4	1|1|2|2	0.17	0.35	0.17	0.35	0.17	-5.35	1.17	0.44	
+ER_P85B	GRB2_HUMAN	GRB2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	GRP78_HUMAN	HSPA5	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	K22E_HUMAN	KRT2	1|1|5|4	11	2.75	4	4|4|1|1	0.07	0.21	0.07	0.21	0.07	-7.25	1.10	0.46	
+ER_P85B	K2C8_HUMAN	KRT8	0|0|2|0	2	0.50	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	5.00	0.28	
+ER_P85B	K2C5_HUMAN	KRT5	0|0|2|2	4	1.00	4	1|1|2|0	0.21	0.43	0.21	0.43	0.21	-3.14	1.00	0.43	
+ER_P85B	K2C4_HUMAN	KRT4	0|0|0|0	0	0.00	4	0|1|0|0	0.00	0.00	0.00	0.00	0.00	-0.65	0.00	0.51	
+ER_P85B	K2C6B_HUMAN	KRT6B	0|0|3|2	5	1.25	4	2|2|2|1	0.06	0.18	0.06	0.18	0.06	-6.40	0.71	0.47	
+ER_P85B	K2C7_HUMAN	KRT7	0|0|2|0	2	0.50	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	5.00	0.28	
+ER_P85B	HSP7C_HUMAN	HSPA8	1|1|0|0	2	0.50	4	0|0|1|1	0.00	0.00	0.00	0.00	0.00	-1.55	1.00	0.51	
+ER_P85B	PK3CA_HUMAN	PIK3CA	3|3|6|6	18	4.50	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	6.28	45.00	0.00	
+ER_P85B	ERBB3_HUMAN	ERBB3	1|0|2|1	4	1.00	4	0|0|0|0	0.25	0.99	0.25	0.99	0.25	-0.11	10.00	0.28	
+ER_P85B	ADT2_HUMAN	SLC25A5	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	ADT3_HUMAN	SLC25A6	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	K1C9_HUMAN	KRT9	1|0|1|0	2	0.50	4	0|0|1|0	0.00	0.00	0.00	0.00	0.00	-0.65	2.00	0.51	
+ER_P85B	TBA1A_HUMAN	TUBA1A	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	TBA4A_HUMAN	TUBA4A	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	ARHG5_HUMAN	ARHGEF5	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	PK3CB_HUMAN	PIK3CB	1|1|4|2	8	2.00	4	0|0|0|0	0.50	1.00	0.50	1.00	0.50	2.40	20.00	0.14	
+ER_P85B	IRS1_HUMAN	IRS1	2|3|3|4	12	3.00	4	0|0|0|0	1.00	1.00	1.00	1.00	1.00	4.43	30.00	0.00	
+ER_P85B	TBB5_HUMAN	TUBB	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	TBB3_HUMAN	TUBB3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	HS90A_HUMAN	HSP90AA1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	HS90B_HUMAN	HSP90AB1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	1433E_HUMAN	YWHAE	0|0|1|1	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+ER_P85B	1433T_HUMAN	YWHAQ	0|0|1|1	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+ER_P85B	LPPRC_HUMAN	LRPPRC	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	ERRFI_HUMAN	ERRFI1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	AP2A1_HUMAN	AP2A1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	GRB7_HUMAN	GRB7	1|1|1|0	3	0.75	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	7.50	0.51	
+ER_P85B	STAT3_HUMAN	STAT3	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	ANXA2_HUMAN	ANXA2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	PTPRA_HUMAN	PTPRA	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	C1QBP_HUMAN	C1QBP	0|0|0|0	0	0.00	4	7|5|0|0	0.00	0.00	0.00	0.00	0.00	-11.03	0.00	0.51	
+ER_P85B	SHC1_HUMAN	SHC1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	PTN11_HUMAN	PTPN11	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	PON2_HUMAN	PON2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	MET_HUMAN	MET	0|0|1|1	2	0.50	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	5.00	0.51	
+ER_P85B	CRKL_HUMAN	CRKL	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	CBL_HUMAN	CBL	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	PHLA2_HUMAN	PHLDA2	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	RT27_HUMAN	MRPS27	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	M2OM_HUMAN	SLC25A11	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	STAT1_HUMAN	STAT1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	SLIRP_HUMAN	SLIRP	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	PK3CD_HUMAN	PIK3CD	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	GRP75_HUMAN	HSPA9	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	AIFM1_HUMAN	AIFM1	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	PHB_HUMAN	PHB	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	HNRPK_HUMAN	HNRNPK	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	TMM33_HUMAN	TMEM33	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
+ER_P85B	RT34_HUMAN	MRPS34	0|0|0|0	0	0.00	4	0|0|0|0	0.00	0.00	0.00	0.00	0.00	-0.11	0.00	0.51	
diff -r 744905986eb9 -r 59e57834c366 toolshed_version/tool_dependencies.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolshed_version/tool_dependencies.xml	Tue Mar 15 15:30:44 2016 -0400
@@ -0,0 +1,6 @@
+<?xml version="1.0"?>
+<tool_dependency>
+    <set_environment version="1.0">
+        <environment_variable name="INSTALL_RUN_PATH" action="set_to">$REPOSITORY_INSTALL_DIR</environment_variable>   
+    </set_environment>
+</tool_dependency>
\ No newline at end of file