inst/bioclassifier_R/subtypePrediction_functions.R

In miRLAB: Dry lab for exploring miRNA-mRNA relationships

# function for boxplots of correlation by subtype
myplot<-function(y,short,pro){
	par(mfrow=c(3,2),mar=c(5,3,2,2),las=3)
	y$prediction<-factor(y$prediction,levels=c("Basal","Her2","LumA","LumB","Normal"))
	boxplot(y$distances[,1]~y$prediction,border=8,ylab="Basal Correlation",main=paste(short,": Basal",sep=""))
	stripchart(y$distances[,1]~y$prediction,vertical=T,method="jitter",pch=3,add=T)
	boxplot(y$distances[,2]~y$prediction,border=8,ylab="Her2 Correlation",main=paste(short,": Her2",sep=""))
	stripchart(y$distances[,2]~y$prediction,vertical=T,method="jitter",pch=3,add=T)
	boxplot(y$distances[,3]~y$prediction,border=8,ylab="LumA Correlation",main=paste(short,": LumA",sep=""))
	stripchart(y$distances[,3]~y$prediction,vertical=T,method="jitter",pch=3,add=T)
	boxplot(y$distances[,4]~y$prediction,border=8,ylab="LumB Correlation",main=paste(short,": LumB",sep=""))
	stripchart(y$distances[,4]~y$prediction,vertical=T,method="jitter",pch=3,add=T)
	boxplot(y$distances[,5]~y$prediction,border=8,ylab="Normal Correlation",main=paste(short,": Normal",sep=""))
	stripchart(y$distances[,5]~y$prediction,vertical=T,method="jitter",pch=3,add=T)
	boxplot(pro~y$prediction,border=8,ylab="Proliferation Index",main=paste(short,": Proliferation Index",sep=""))
	stripchart(pro~y$prediction,vertical=T,method="jitter",pch=3,add=T)
}

medianCtr<-function(x){
	annAll <- dimnames(x)
	medians <- apply(x,1,median,na.rm=T)
	x <- t(scale(t(x),center=medians,scale=F))
	dimnames(x) <- annAll
	return(x)
}

pcaEA<-function(x,classes,size=1,showLegend=T,legendloc="topright",mainStr="",startPC=1,stopPC=2,showNames=T,showClasses=F,axisExpansion=0,groupColors=NA){
	
	features<- dim(x)[1]
	samples<- dim(x)[2]
	sampleNames<- dimnames(x)[[2]]
	featureNames<-dimnames(x)[[1]]
	x<-apply(x,2,as.numeric)
	
	#principal components plots
	data.pca<-prcomp(as.matrix(x))

	# Proportion of total variance distributed over 10 first components:
	tmp<-data.pca$sdev[1:10]^2/sum(data.pca$sdev^2)

	gr.labels<-as.vector(t(classes))
	gr.labels.fac<-factor(as.vector(t(classes)),exclude="")
	nlabels<-nlevels(gr.labels.fac)
	legendLabels<-vector()
	legendColors<-vector()
	for(k in 1:nlabels){
		group<-levels(gr.labels.fac)[k]
		legendLabels[k]<-group
		if(length(groupColors)>1){
				gr.labels[gr.labels.fac==group]<-groupColors[k]
				legendColors[k]<-groupColors[k]
		}else{
				gr.labels[gr.labels.fac==group]<-k
				legendColors[k]<-k
		}
	}
	
	if(length(groupColors)==1){
		gr.labels<-as.numeric(gr.labels)
	}
	
	#plot 2pcs by each other
	i<-startPC
	j<-stopPC
	
	#graphing parameters
	par(lab=c(3,4,3))
 	par(mgp=c(.3,.5,.0))
	par(mai=c(.5,.5,.5,.5))
	par(xaxt="n",yaxt="n")

	strM<-mainStr
	strX<-paste("PC",i,paste("(",round(tmp[i],4)*100,"%)",sep=""),sep=" ")
	strY<-paste("PC",j,paste("(",round(tmp[j],4)*100,"%)",sep=""),sep=" ")
	xmin<-min(data.pca$rotation[,i])-abs(axisExpansion*min(data.pca$rotation[,i]))
	xmax<-max(data.pca$rotation[,i])+abs(axisExpansion*max(data.pca$rotation[,i]))
	ymin<-min(data.pca$rotation[,j])-abs(axisExpansion*min(data.pca$rotation[,j]))
	ymax<-max(data.pca$rotation[,j])+abs(axisExpansion*max(data.pca$rotation[,j]))
	plot(data.pca$rotation[,i],data.pca$rotation[,j], xlab=strX, ylab=strY, 
							main=strM, col=gr.labels,cex=size,pch="",xlim=c(xmin,xmax),ylim=c(ymin,ymax))
	if(showNames){
		text(data.pca$rotation[,i],data.pca$rotation[,j],labels=names(data.pca$rotation[,i]),cex=size*.6)
	}else{
		if(showClasses){
				text(data.pca$rotation[,i],data.pca$rotation[,j],labels=gr.labels.fac,cex=size*.6)
		}else{
				points(data.pca$rotation[,i],data.pca$rotation[,j],col=gr.labels,cex=size*1.5,pch=19)
		}
		if(showLegend){
			legend(legendloc,legend=legendLabels,col=legendColors,pch=19,x.intersp=.3,yjust=.5,bty="n",cex=size)
		}
	}
}

cols <- function(lowi = "green", highi = "red", ncolors = 20) {
        low <- col2rgb(lowi)/255
        high <- col2rgb("black")/255
        col1 <- rgb(seq(low[1], high[1], len = ncolors), seq(low[2], 
            high[2], len = ncolors), seq(low[3], high[3], len = ncolors))
        low <- col2rgb("black")/255
        high <- col2rgb(highi)/255
        col2 <- rgb(seq(low[1], high[1], len = ncolors), seq(low[2], 
            high[2], len = ncolors), seq(low[3], high[3], len = ncolors))
        col<-c(col1[1:(ncolors-1)],col2)
        return(col)
}
  
myHeatmap<-function(x,t.colors=NA,fileName="cluster.cdt",linkage="complete",distance="pearson",contrast=2,returnSampleClust=F,rowNames=NA,rightMar=7,bottomMar=1,colNames=NA){
	
	#####temp<-hclust2treeview(x,method=distance,file=fileName,link=linkage,keep.hclust=T)
	temp<-ctc::hclust2treeview(x,method=distance,file=fileName,link=linkage,keep.hclust=T)
	gTree<-temp[[1]]
	sTree<-temp[[2]]
	
	imageVals<-x
	imageVals[x > contrast] <- contrast
	imageVals[x < -1 * contrast] <- -1 * contrast
	
	if(sum(is.na(t.colors))>0){
			heatmap(imageVals,Rowv=as.dendrogram(gTree),Colv=as.dendrogram(sTree),
							col=cols(),labCol=colNames, scale="none",
							margins=c(bottomMar,rightMar),labRow=rowNames)
	}else{
		if(length(t.colors)>dim(imageVals)[2]){
			#####heatmap.plus(imageVals,Rowv=as.dendrogram(gTree),Colv=as.dendrogram(sTree),
			#####					col=cols(),labCol=colNames,labRow=rowNames,scale="none",
			#####					ColSideColors=t.colors, margins=c(bottomMar,rightMar))
			heatmap.plus::heatmap.plus(imageVals,Rowv=as.dendrogram(gTree),Colv=as.dendrogram(sTree),
								col=cols(),labCol=colNames,labRow=rowNames,scale="none",
								ColSideColors=t.colors, margins=c(bottomMar,rightMar))
		}else{
			heatmap(imageVals,Rowv=as.dendrogram(gTree),Colv=as.dendrogram(sTree),
								col=cols(),labCol=colNames,labRow=rowNames,scale="none",
								ColSideColors=as.vector(t(t.colors)), margins=c(bottomMar,rightMar))
		}
	}
	if(returnSampleClust){
		return(sTree)
	}
}


standardize<-function(x){
	annAll<-dimnames(x)
	x<-scale(x)
	dimnames(x)<-annAll
	return(x)
}


overlapSets<-function(x,y){

	# subset the two lists to have a commonly ordered gene list
	x<-x[dimnames(x)[[1]] %in% dimnames(y)[[1]],]
	y<-y[dimnames(y)[[1]] %in% dimnames(x)[[1]],]

	#and sort such that thing are in the correct order
	x<-x[sort.list(row.names(x)),]
	y<-y[sort.list(row.names(y)),]

	return(list(x=x,y=y))
}


collapseIDs<-function(x,allids=row.names(x),method="mean"){

        allids<-as.vector(allids)
        ids<- levels(as.factor(allids))
        x.col<- NULL

        if(length(ids)==dim(x)[1]){ 
                        dimnames(x)[[1]]<-allids
                        return(x) 
        }
        
        for(i in 1:length(ids)){
                if(sum(allids==ids[i])>1){
                        indices <- allids==ids[i] 
                        if(method=="mean"){
                                vals<-apply(x[indices,],2,mean,na.rm=T)
                        }
                        if(method=="median"){
                                vals<-apply(x[indices,],2,median,na.rm=T)
                        }
                        if(method=="stdev"){   
                                temp<- x[indices,]
                                stdevs<- apply(temp,1,sd,na.rm=T)
                                vals<- temp[match(max(stdevs),stdevs),]
                        }
                        if(method=="iqr"){   
                                temp<- x[indices,]
                                iqrs<- apply(temp,1,function(x){quantile(x,.75,na.rm=T)-quantile(x,.25,na.rm=T)})
                                vals<- temp[match(max(iqrs),iqrs),]
                        }
                        x.col <- rbind(x.col,vals)
                }else{
                        x.col <- rbind(x.col,x[allids==ids[i],])
                }
        }

        dimnames(x.col)<- list(ids,dimnames(x)[[2]])
        return(x.col)
        
}


readarray<-function(dataFile,designFile=NA,hr=1,impute=T,method="mean"){

	headerRows <- hr

	x<-read.table(dataFile,sep="\t",header=F,fill=T,stringsAsFactors=FALSE)

	if(headerRows==1){
			sampleNames<-as.vector(t(x[1,-1]))
			x<-x[-1,]
			classes<-NULL
			ids<-x[,1]
			xd<-x[,-1]
			xd<-apply(xd,2,as.numeric)
			xd<-collapseIDs(xd,ids,method)	
	}else{
			sampleNames<-as.vector(t(x[1,-1]))
			x<-x[-1,]
			
			classes<-x[1:(headerRows-1),]
			dimnames(classes)[[1]]<-classes[,1]
			classes<-classes[,-1]
			classes[classes==""]<-NA
			classes<-t(classes)
			rownames(classes)<-sampleNames
			classes<-as.data.frame(classes)
						
			xd<-x[(-1:-(headerRows-1)),]
			ids<-as.vector(t(xd[,1]))
			xd<-xd[,-1]
			xd<-apply(xd,2,as.numeric)
			xd<-collapseIDs(xd,ids,method)
	}
	
	features<- dim(xd)[1]
	samples<- dim(xd)[2]
	geneNames<-rownames(xd)
	xd<-apply(xd,2,as.numeric)
	rownames(xd)<-geneNames
	colnames(xd)<-sampleNames

	if(!is.na(designFile)){
		x<-read.table(designFile,sep="\t",header=T,row.names=1,fill=T,,stringsAsFactors=FALSE)
		xd<-xd[,sort.list(colnames(xd))]
		xd<-xd[,colnames(xd) %in% rownames(x)]
		x<-x[rownames(x) %in% colnames(xd),]
		x<-x[sort.list(rownames(x)),]
		classes<-as.data.frame(x)
	}
	
	if(sum(apply(xd,2,is.na))>0 & impute){
		library(impute)
		allAnn<-dimnames(xd)
		data.imputed<-impute.knn(as.matrix(xd))$data
		xd<-data.imputed[1:features,]
		dimnames(xd)<-allAnn
	}
	
	return(list(xd=xd, classes=classes, nfeatures=features, nsamples=samples, fnames=geneNames, snames=sampleNames))
}


sspPredict<-function(x,classes="",y,nGenes="",priors="equal",std=F,distm="euclidean",centroids=F){

	dataMatrix<-x
	features<- dim(x)[1]
	samples<- dim(x)[2]
	sampleNames<- dimnames(x)[[2]]
	featureNames<- dimnames(x)[[1]]
	
	#parse the test file - same as train file but no rows of classes
	tdataMatrix<-y
	tfeatures<- dim(y)[1]
	tsamples<- dim(y)[2]
	tsampleNames<- dimnames(y)[[2]]
	tfeatureNames<- dimnames(y)[[1]]
	
	#dimnames(tdataMatrix)[[2]]<-paste("x",seq(1,471))
	temp <- overlapSets(dataMatrix,tdataMatrix)
	dataMatrix <- temp$x
	tdataMatrix <- temp$y
	sfeatureNames<-row.names(dataMatrix)
	
	# standardize both sets
	if(std){
		dataMatrix<-standardize(dataMatrix)
		tdataMatrix<-standardize(tdataMatrix)
	}
	
	if(!centroids){
		thisClass <- as.vector(classes[,1])
		nClasses<-nlevels(as.factor(thisClass))
		classLevels<-levels(as.factor(thisClass))
		for(j in 1:nClasses){
			thisClass[thisClass==classLevels[j]] <- j
		}
		thisClass<-as.numeric(thisClass)
		dataMatrix <- dataMatrix[,!(is.na(thisClass))]
		thisClass <- thisClass[!(is.na(thisClass))]
	
		scores<-apply(dataMatrix,1,bwss,thisClass)
		trainscores<-vector()	
		for(j in 1:dim(dataMatrix)[1]){			
			trainscores[j]<-scores[[row.names(dataMatrix)[j]]]$bss / scores[[row.names(dataMatrix)[j]]]$wss
		}
		
		dataMatrix<-dataMatrix[sort.list(trainscores,decreasing=T),]
		tdataMatrix<-tdataMatrix[sort.list(trainscores,decreasing=T),]	
		
		if(nGenes==""){
			nGenes<-dim(dataMatrix)[1]
		}
		print(paste("Number of genes used:",nGenes))
		
		dataMatrix<-dataMatrix[1:nGenes,]
		tdataMatrix<-tdataMatrix[1:nGenes,]

		centroids<-matrix(,nrow=nGenes,ncol=nClasses)
		for(j in 1:nClasses){
			centroids[,j]<-apply(dataMatrix[,thisClass==j],1,mean)
		}
		dimnames(centroids)<-list(row.names(dataMatrix),NULL)
		
	}else{
		nGenes<-dim(dataMatrix)[1]
		print(paste("Number of genes used:",nGenes))
		centroids<-dataMatrix
		nClasses<-dim(centroids)[2]
		classLevels<-dimnames(centroids)[[2]]
	}
	
	distances<-matrix(ncol=nClasses,nrow=dim(tdataMatrix)[2])
	for(j in 1:nClasses){
		if(distm=="euclidean"){
			distances[,j]<-dist(t(cbind(centroids[,j],tdataMatrix)))[1:(dim(tdataMatrix)[2])]
		}
		if(distm=="correlation" | distm=="pearson"){
			distances[,j]<-t(-1*cor(cbind(centroids[,j],tdataMatrix),use="pairwise.complete.obs"))[2:(dim(tdataMatrix)[2]+1)]
		}
		if(distm=="spearman"){
			distances[,j]<-t(-1*cor(cbind(centroids[,j],tdataMatrix),method="spearman",use="pairwise.complete.obs"))[2:(dim(tdataMatrix)[2]+1)]
		}
	}
	
	scores<-apply(distances,1,min)
	prediction<-vector(length=tsamples)
	for(i in 1:tsamples){
		prediction[i]<-classLevels[match(scores[i],distances[i,])]
	}
	names(prediction)<-tsampleNames
	
	return(list(predictions=prediction,testData=tdataMatrix,distances=distances,centroids=centroids))
}