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CHM.R |
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| diff -r e2b3920e20c1 -r 603d5c39e8dc CHM.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/CHM.R Thu Jan 26 09:53:30 2017 -0500 |
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| @@ -0,0 +1,146 @@ +### 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])) |