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clusterEnhancer.R
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clusterEnhancer.R
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#clusterEnhancer.R
#140121
#Charles Lin
# '''
# The MIT License (MIT)
# Copyright (c) 2014 Charles Lin
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
# '''
# '''
# program to perform 2D clustering by enhancer signal
# can perform initial enhancer mapping or draw from a set of finished rose outputs
# '''
#===================================================================
#========================INPUT PARAMETERS===========================
#===================================================================
args <- commandArgs()
print(args[3:6])
genome = args[3]
outputFolder = args[4]
analysisName = args[5]
enhancerFile = args[6]
#===================================================================
#====================MAKING ENHANCER MATRIX=========================
#===================================================================
#enhancerTable = read.delim('/Volumes/raider/temp/enhancerClusterTesting/output/HG18_dlbcl_enhancers_signalTable.txt')
enhancerTable = read.delim(enhancerFile)
enhancerMatrix = as.matrix(enhancerTable[,7:ncol(enhancerTable)])
#rownames(enhancerMatrix) = enhancerTable[,1]
#===================================================================
#==================SAMPLE DISTANCE MATRICIES========================
#===================================================================
#distance by samples
sampleDist = as.dist(1-cor(enhancerMatrix))
sampleHC = hclust(sampleDist)
#===================================================================
#=====================PLOTTING SAMPLE TREE==========================
#===================================================================
#PLOTTING THE SAMPLE TREE
treePDFFile = paste(outputFolder,genome,'_',analysisName,"_treePlot.pdf",sep='')
pdf(treePDFFile,width=8,height=8)
plot(sampleHC,xlab='',main = paste(genome,'_',analysisName,sep=''))
dev.off()
#===================================================================
#=======================SAMPLE CLUSTER ORDERING=====================
#===================================================================
sampleOrder = sampleHC$order
#===================================================================
#=================MAKING SAMPLE PAIRWISE HEATMAP====================
#===================================================================
#distance by samples
sampleSimMatrix = cor(enhancerMatrix)
#Set the color spectrum
colorSpectrum <- colorRampPalette(c("white","red"))(100)
#setting a color data range
minValue <- .1
maxValue <- .9
color_cuts <- seq(minValue,maxValue,length=100)
color_cuts <- c(0, color_cuts,1)
#add one extra min color to even out sampling
colorSpectrum <- c(colorSpectrum[1],colorSpectrum)
clusterSampleFile = paste(outputFolder,genome,'_',analysisName,"_clusterSamples.pdf",sep='')
pdf(file = clusterSampleFile,width = 10,height =10)
layout(matrix(data=c(1,2,2,2,1,3,3,3,1,3,3,3,1,3,3,3,1,3,3,3,1,3,3,3,1,4,4,4),ncol= 7))
par(mar=c(8,9,5,9))
plot(as.dendrogram(sampleHC),ylab='Distance')
par(mar=c(4,2,2,2))
plot(as.dendrogram(sampleHC),horiz=TRUE,xlab='Distance',leaflab='none')
par(mar=c(6,4,4,2))
image(1:ncol(sampleSimMatrix),1:nrow(sampleSimMatrix),t(sampleSimMatrix[sampleOrder,sampleOrder]),breaks=color_cuts,col=colorSpectrum,xaxt="n",yaxt="n",ylab='',xlab='')
par(mar=c(6,5,4,2))
image(1:2,color_cuts[2:101],t(matrix(data=color_cuts[2:101],ncol=2,nrow=100)),breaks=color_cuts,col=colorSpectrum,xaxt="n",xlab="",ylab="Similarity")
dev.off()
#===================================================================
#===================ENHANCER DISTANCE MATRICIES=====================
#===================================================================
#distances by SEs
seDist = as.dist(1-cor(t(enhancerMatrix)))
seHC = hclust(seDist)
#plot(seHC)
#===================================================================
#======================ENHANCER CLUSTER ORDERING====================
#===================================================================
seOrder = seHC$order
#===================================================================
#======================INDIVIDUAL CLUSTERS==========================
#===================================================================
#establishing individual clusters
seClusterOrder = cutree(seHC,k=10)
# #try to guess the distance cutoff
# seDistRange = range(seDist)
# n = (seDistRange[2]-seDistRange[1])/20
# cutVector = c()
# nClustVector = c()
# for(i in seq(seDistRange[1]+n,seDistRange[2],n)){
# n = max(cutree(seHC,h=i))
# cutVector = c(cutVector,i)
# nClustVector = c(nClustVector,n)
# }
#===================================================================
#======================WRITING CLUSTER TABLE========================
#===================================================================
clusterOrderTable = enhancerTable[which(seClusterOrder==1),]
for(i in 2:max(seClusterOrder)){
clusterOrderTable =rbind(clusterOrderTable,enhancerTable[which(seClusterOrder==i),])
}
clusterColVector = c()
for(i in 1:max(seClusterOrder)){
clusterColVector = c(clusterColVector,rep(i,length(which(seClusterOrder==i))))
}
clusterOrderTable = cbind(clusterOrderTable,clusterColVector)
colnames(clusterOrderTable)[ncol(clusterOrderTable)] = 'CLUSTER'
clusterOrderTable = cbind(clusterOrderTable,clusterColVector)
colnames(clusterOrderTable)[ncol(clusterOrderTable)] = 'CLUSTER'
clusterTableFile = paste(outputFolder,genome,'_',analysisName,"_clusterTable.txt",sep='')
write.table(clusterOrderTable,file= clusterTableFile,quote=FALSE,sep='\t',col.names=TRUE,row.names=FALSE)
#===================================================================
#======================PLOTTING EXEMPLARS===========================
#===================================================================
exemplarPDFFile = paste(outputFolder,genome,'_',analysisName,"_exemplarPlots.pdf",sep='')
pdf(exemplarPDFFile,width=5,height=5)
exemplarMatrix = matrix(nrow=max(seClusterOrder),ncol = ncol(enhancerMatrix))
#Plot the examplars
for(i in 1:max(seClusterOrder)){
cluster = matrix(enhancerMatrix[which(seClusterOrder==i),],ncol=ncol(enhancerMatrix))
plot(1:ncol(enhancerMatrix),rep(0,ncol(enhancerMatrix)),ylim =quantile(cluster,c(0.025,0.975)),cex=0,xlab='Samples',xaxt='n',ylab='Enhancer signal (fold/median)',main = paste('Cluster',i))
axis(1,1:ncol(enhancerMatrix),colnames(enhancerMatrix)[sampleOrder])
for(j in 1:nrow(cluster)){
lines(1:ncol(enhancerMatrix),cluster[j,sampleOrder],col = rgb(0.2,0.2,0.2,0.1),lwd=1)
}
lines(1:ncol(enhancerMatrix),apply(cluster,2,median)[sampleOrder],col='red',lwd=4)
legend(1,quantile(cluster,.95),paste("n =",nrow(cluster)))
if(nrow(cluster) == 1){
exemplarMatrix[i,] = cluster[1,sampleOrder]
}else{
exemplarMatrix[i,] = apply(cluster[,sampleOrder],2,median)
}
}
colnames(exemplarMatrix) = colnames(enhancerMatrix)[sampleOrder]
rownames(exemplarMatrix) = 1:max(seClusterOrder)
dev.off()
#===================================================================
#======================WRITING EXEMPLAR TAB=========================
#===================================================================
exemplarTableFile = paste(outputFolder,genome,'_',analysisName,"_exmplarTable.txt",sep='')
write.table(signif(exemplarMatrix,4),file= exemplarTableFile,quote=FALSE,sep='\t')
#===================================================================
#=======================MAKING ENHANCER HEATMAPS====================
#===================================================================
#Set the color spectrum
colorSpectrum <- colorRampPalette(c("white","red"))(100)
#setting a color data range
minValue <- quantile(enhancerMatrix,na.rm=TRUE,prob=0.1,names=FALSE)
maxValue <- quantile(enhancerMatrix,na.rm=TRUE,prob=0.9,names=FALSE)
color_cuts <- seq(minValue,maxValue,length=100)
color_cuts <- c(0, color_cuts,max(enhancerMatrix))
#add one extra min color to even out sampling
colorSpectrum <- c(colorSpectrum[1],colorSpectrum)
#Making png
clusterPNGFile = paste(outputFolder,genome,'_',analysisName,'_2d_cluster.png',sep='')
png(filename = clusterPNGFile,width = 800,height =800)
layout(matrix(data=c(1,2,2,2,1,3,3,3,1,3,3,3,1,3,3,3,1,3,3,3,1,3,3,3,1,4,4,4),ncol= 7))
par(mar=c(7,9,2,9))
plot(as.dendrogram(sampleHC),ylab='Distance')
par(mar=c(4,2,2,0))
plot(as.dendrogram(seHC),horiz=TRUE,xlab='Distance',leaflab='none')
par(mar=c(6,2,4,2))
image(1:ncol(enhancerMatrix),1:nrow(enhancerMatrix),t(enhancerMatrix[seOrder,sampleOrder]),breaks=color_cuts,col=colorSpectrum,xaxt="n",yaxt="n",ylab='',xlab='')
par(mar=c(6,5,4,2))
image(1:2,color_cuts[2:101],t(matrix(data=color_cuts[2:101],ncol=2,nrow=100)),breaks=color_cuts,col=colorSpectrum,xaxt="n",xlab="",ylab="Fold vs. median")
dev.off()
#making pdf
clusterPDFFile = paste(outputFolder,genome,'_',analysisName,'_2d_cluster.pdf',sep='')
pdf(file = clusterPDFFile,width = 8,height =8)
layout(matrix(data=c(1,2,2,2,1,3,3,3,1,3,3,3,1,3,3,3,1,3,3,3,1,3,3,3,1,4,4,4),ncol= 7))
par(mar=c(7,9,2,9))
plot(as.dendrogram(sampleHC),ylab='Distance')
par(mar=c(4,2,2,0))
plot(as.dendrogram(seHC),horiz=TRUE,xlab='Distance',leaflab='none')
par(mar=c(6,2,4,2))
image(1:ncol(enhancerMatrix),1:nrow(enhancerMatrix),t(enhancerMatrix[seOrder,sampleOrder]),breaks=color_cuts,col=colorSpectrum,xaxt="n",yaxt="n",ylab='',xlab='')
par(mar=c(6,5,4,2))
image(1:2,color_cuts[2:101],t(matrix(data=color_cuts[2:101],ncol=2,nrow=100)),breaks=color_cuts,col=colorSpectrum,xaxt="n",xlab="",ylab="Fold vs. median")
dev.off()
#===================================================================
#====================MAKING EXEMPLAR HEATMAP========================
#===================================================================
#Set the color spectrum
colorSpectrum <- colorRampPalette(c("white","red"))(100)
#setting a color data range
minValue <- quantile(exemplarMatrix,na.rm=TRUE,prob=0.1,names=FALSE)
maxValue <- quantile(exemplarMatrix,na.rm=TRUE,prob=0.9,names=FALSE)
color_cuts <- seq(minValue,maxValue,length=100)
color_cuts <- c(0, color_cuts,max(exemplarMatrix))
#add one extra min color to even out sampling
colorSpectrum <- c(colorSpectrum[1],colorSpectrum)
clusterExemplarFile = paste(outputFolder,genome,'_',analysisName,"_clusterExemplars.pdf",sep='')
pdf(file = clusterExemplarFile,width = 8,height =8)
layout(matrix(data=c(1,2,2,2,1,2,2,2,1,2,2,2,1,2,2,2,1,3,3,3),ncol= 5))
par(mar=c(7,9,2,9))
plot(as.dendrogram(sampleHC),ylab='Distance')
par(mar=c(4,12,2,0))
image(1:ncol(exemplarMatrix),1:nrow(exemplarMatrix),t(exemplarMatrix),breaks=color_cuts,col=colorSpectrum,xaxt="n",yaxt="n",ylab='Clusters',xlab='')
axis(2,1:10,1:10)
par(mar=c(4,5,2,2))
image(1:2,color_cuts[2:101],t(matrix(data=color_cuts[2:101],ncol=2,nrow=100)),breaks=color_cuts,col=colorSpectrum,xaxt="n",xlab="",ylab="Fold vs. median")
dev.off()