Mercurial > repos > md-anderson-bioinformatics > heat_map_creation
view CHM.R @ 10:1b0182d3d262 draft
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author | insilico-bob |
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date | Thu, 26 Jan 2017 10:15:31 -0500 |
parents | 603d5c39e8dc |
children | 16593e40c2cd |
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### This method generates a row and column ordering given an input matrix and ordering methods. ### ### matrixData - numeric matrix ### rowOrderMethod - Hierarchical, Original, Random ### rowDistanceMeasure - For clustering, distance measure. May be: euclidean, binary, manhattan, maximum, canberra, minkowski, or correlation. ### rowAgglomerationMethod - For clustering, agglomeration method. May be: 'average' for Average Linkage, 'complete' for Complete Linkage, ### 'single' for Single Linkage, 'ward', 'mcquitty', 'median', or 'centroid'. ### colOrderMethod ### colDistanceMeasure ### colAgglomerationMethod ### rowOrderFile - output file of order of rows ### rowDendroFile - output file of row dendrogram ### colOrderFile - output file of order of cols ### colDendroFile - output file of col dendrogram ### rowCut - For rows the number of classifications to automatically generate based on dendrogram into a classification file. 0 for turned off. ### colCut - For columns the number of classifications to automatically generate based on dendrogram into a classification file. 0 for turned off. performDataOrdering<-function(dataFile, rowOrderMethod, rowDistanceMeasure, rowAgglomerationMethod, colOrderMethod, colDistanceMeasure, colAgglomerationMethod,rowOrderFile, colOrderFile, rowDendroFile, colDendroFile, rowCut, colCut) { dataMatrix = read.table(dataFile, header=TRUE, sep = "\t", row.names = 1, as.is=TRUE, na.strings=c("NA","N/A","-","?")) rowOrder <- createOrdering(dataMatrix, rowOrderMethod, "row", rowDistanceMeasure, rowAgglomerationMethod) if (rowOrderMethod == "Hierarchical") { writeHCDataTSVs(rowOrder, rowDendroFile, rowOrderFile) writeHCCut(rowOrder, rowCut, paste(rowOrderFile,".cut", sep="")) } else { writeOrderTSV(rowOrder, rownames(dataMatrix), rowOrderFile) } colOrder <- createOrdering(dataMatrix, colOrderMethod, "col", colDistanceMeasure, colAgglomerationMethod) if (colOrderMethod == "Hierarchical") { writeHCDataTSVs(colOrder, colDendroFile, colOrderFile) writeHCCut(colOrder, colCut, paste(colOrderFile,".cut", sep="")) } else { writeOrderTSV(colOrder, colnames(dataMatrix), colOrderFile) } } #creates output files for hclust ordering writeHCDataTSVs<-function(uDend, outputHCDataFileName, outputHCOrderFileName) { data<-cbind(uDend$merge, uDend$height, deparse.level=0) colnames(data)<-c("A", "B", "Height") write.table(data, file = outputHCDataFileName, append = FALSE, quote = FALSE, sep = "\t", row.names=FALSE) data=matrix(,length(uDend$labels),2); for (i in 1:length(uDend$labels)) { data[i,1] = uDend$labels[i]; data[i,2] = which(uDend$order==i); } colnames(data)<-c("Id", "Order") write.table(data, file = outputHCOrderFileName, append = FALSE, quote = FALSE, sep = "\t", row.names=FALSE) } #creates order file for non-clustering methods writeOrderTSV<-function(newOrder, originalOrder, outputHCOrderFileName) { data=matrix(,length(originalOrder),2); for (i in 1:length(originalOrder)) { data[i,1] = originalOrder[i]; data[i,2] = which(newOrder==originalOrder[i]); } colnames(data)<-c("Id", "Order") write.table(data, file = outputHCOrderFileName, append = FALSE, quote = FALSE, sep = "\t", row.names=FALSE) } #creates a classification file based on user specified cut of dendrogram writeHCCut<-function(uDend, cutNum, outputCutFileName) { if (cutNum < 2) { return() } print (paste("Writing cut file ", outputCutFileName)) cut <- cutree(uDend, cutNum); id <- names(cut); data=matrix(,length(cut),2); for (i in 1:length(cut)) { data[i,1] = id[i]; data[i,2] = sprintf("Cluster %d", cut[i]); } write.table(data, file = outputCutFileName, append = FALSE, quote = FALSE, sep = "\t", row.names=FALSE, col.names = FALSE); } createOrdering<-function(matrixData, orderMethod, direction, distanceMeasure, agglomerationMethod) { ordering <- NULL if (orderMethod == "Hierarchical") { # Compute dendrogram for "Distance Metric" distVals <- NULL if(direction=="row") { if (distanceMeasure == "correlation") { geneGeneCor <- cor(t(matrixData), use="pairwise") distVals <- as.dist((1-geneGeneCor)/2) } else { distVals <- dist(matrixData, method=distanceMeasure) } } else { #column if (distanceMeasure == "correlation") { geneGeneCor <- cor(matrixData, use="pairwise") distVals <- as.dist((1-geneGeneCor)/2) } else { distVals <- dist(t(matrixData), method=distanceMeasure) } } # if (agglomerationMethod == "ward") { # ordering <- hclust(distVals * distVals, method="ward.D2") # } else { ordering <- hclust(distVals, method=agglomerationMethod) # } } else if (orderMethod == "Random") { if(direction=="row") { headerList <- rownames(matrixData) ordering <- sample(headerList, length(headerList)) } else { headerList <- colnames(matrixData) ordering <- sample(headerList, length(headerList)) } } else if (orderMethod == "Original") { if(direction=="row") { ordering <- rownames(matrixData) } else { ordering <- colnames(matrixData) } } else { stop("createOrdering -- failed to find ordering method") } return(ordering) } ### Initialize command line arguments and call performDataOrdering options(warn=-1) args = commandArgs(TRUE) performDataOrdering(dataFile=args[1], rowOrderMethod=args[2], rowDistanceMeasure=args[3], rowAgglomerationMethod=args[4], colOrderMethod=args[5], colDistanceMeasure=args[6], colAgglomerationMethod=args[7],rowOrderFile=args[8], colOrderFile=args[9], rowDendroFile=args[10], colDendroFile=args[11], rowCut=args[12], colCut=args[13]) #suppressWarnings(performDataOrdering(dataFile=args[1], rowOrderMethod=args[2], rowDistanceMeasure=args[3], rowAgglomerationMethod=args[4], colOrderMethod=args[5], colDistanceMeasure=args[6], colAgglomerationMethod=args[7],rowOrderFile=args[8], colOrderFile=args[9], rowDendroFile=args[10], colDendroFile=args[11]))