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1 ### This method generates a row and column ordering given an input matrix and ordering methods.
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2 ###
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3 ### matrixData - numeric matrix
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4 ### rowOrderMethod - Hierarchical, Original, Random
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5 ### rowDistanceMeasure - For clustering, distance measure. May be: euclidean, binary, manhattan, maximum, canberra, minkowski, or correlation.
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6 ### rowAgglomerationMethod - For clustering, agglomeration method. May be: 'average' for Average Linkage, 'complete' for Complete Linkage,
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7 ### 'single' for Single Linkage, 'ward', 'mcquitty', 'median', or 'centroid'.
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8 ### colOrderMethod
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9 ### colDistanceMeasure
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10 ### colAgglomerationMethod
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11 ### rowOrderFile - output file of order of rows
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12 ### rowDendroFile - output file of row dendrogram
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13 ### colOrderFile - output file of order of cols
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14 ### colDendroFile - output file of col dendrogram
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15 ### rowCut - For rows the number of classifications to automatically generate based on dendrogram into a classification file. 0 for turned off.
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16 ### colCut - For columns the number of classifications to automatically generate based on dendrogram into a classification file. 0 for turned off.
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17
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18 performDataOrdering<-function(dataFile, rowOrderMethod, rowDistanceMeasure, rowAgglomerationMethod, colOrderMethod, colDistanceMeasure, colAgglomerationMethod,rowOrderFile, colOrderFile, rowDendroFile, colDendroFile, rowCut, colCut)
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19 {
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20 dataMatrix = read.table(dataFile, header=TRUE, sep = "\t", row.names = 1, as.is=TRUE, na.strings=c("NA","N/A","-","?"))
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21 rowOrder <- createOrdering(dataMatrix, rowOrderMethod, "row", rowDistanceMeasure, rowAgglomerationMethod)
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22 if (rowOrderMethod == "Hierarchical") {
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23 writeHCDataTSVs(rowOrder, rowDendroFile, rowOrderFile)
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24 if (rowCut != 0) {
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25 writeHCCut(rowOrder, rowCut, paste(rowOrderFile,".cut", sep=""))
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26 }
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27 }
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28
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29 colOrder <- createOrdering(dataMatrix, colOrderMethod, "col", colDistanceMeasure, colAgglomerationMethod)
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30 if (colOrderMethod == "Hierarchical") {
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31 writeHCDataTSVs(colOrder, colDendroFile, colOrderFile)
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32 if (colCut != 0) {
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33 writeHCCut(colOrder, colCut, paste(colOrderFile,".cut", sep=""))
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34 }
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35 }
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36 }
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37
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38 #creates output files for hclust ordering
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39 writeHCDataTSVs<-function(uDend, outputHCDataFileName, outputHCOrderFileName)
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40 {
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41 data<-cbind(uDend$merge, uDend$height, deparse.level=0)
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42 colnames(data)<-c("A", "B", "Height")
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43 write.table(data, file = outputHCDataFileName, append = FALSE, quote = FALSE, sep = "\t", row.names=FALSE)
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44
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45 data=matrix(,length(uDend$labels),2);
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46 for (i in 1:length(uDend$labels)) {
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47 data[i,1] = uDend$labels[i];
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48 data[i,2] = which(uDend$order==i);
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49 }
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50 colnames(data)<-c("Id", "Order")
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51 write.table(data, file = outputHCOrderFileName, append = FALSE, quote = FALSE, sep = "\t", row.names=FALSE)
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52 }
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53
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54 #creates a classification file based on user specified cut of dendrogram
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55 writeHCCut<-function(uDend, cutNum, outputCutFileName)
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56 {
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57 print (paste("Writing cut file ", outputCutFileName))
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58 cut <- cutree(uDend, cutNum);
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59 id <- names(cut);
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60 data=matrix(,length(cut),2);
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61 for (i in 1:length(cut)) {
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62 data[i,1] = id[i];
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63 data[i,2] = sprintf("Cluster %d", cut[i]);
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64 }
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65
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66 write.table(data, file = outputCutFileName, append = FALSE, quote = FALSE, sep = "\t", row.names=FALSE, col.names = FALSE);
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67 }
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68
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69
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70 createOrdering<-function(matrixData, orderMethod, direction, distanceMeasure, agglomerationMethod)
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71 {
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72 ordering <- NULL
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73
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74 if (orderMethod == "Hierarchical")
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75 {
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76
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77 # Compute dendrogram for "Distance Metric"
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78 distVals <- NULL
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79 if(direction=="row") {
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80 if (distanceMeasure == "correlation") {
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81 geneGeneCor <- cor(t(matrixData), use="pairwise")
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82 distVals <- as.dist((1-geneGeneCor)/2)
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83 } else {
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84 distVals <- dist(matrixData, method=distanceMeasure)
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85 }
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86 } else { #column
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87 if (distanceMeasure == "correlation") {
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88 geneGeneCor <- cor(matrixData, use="pairwise")
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89 distVals <- as.dist((1-geneGeneCor)/2)
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90 } else {
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91 distVals <- dist(t(matrixData), method=distanceMeasure)
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92 }
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93 }
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94
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95 # if (agglomerationMethod == "ward") {
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96 # ordering <- hclust(distVals * distVals, method="ward.D2")
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97 # } else {
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98 ordering <- hclust(distVals, method=agglomerationMethod)
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99 # }
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100 }
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101 else if (orderMethod == "Random")
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102 {
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103 if(direction=="row") {
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104 headerList <- rownames(matrixData)
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105 ordering <- sample(headerList, length(headerList))
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106 } else {
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107 headerList <- colnames(matrixData)
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108 ordering <- sample(headerList, length(headerList))
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109 }
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110 }
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111 else if (orderMethod == "Original")
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112 {
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113 if(direction=="row") {
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114 ordering <- rownames(matrixData)
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115 } else {
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116 ordering <- colnames(matrixData)
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117 }
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118 } else {
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119 stop("createOrdering -- failed to find ordering method")
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120 }
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121 return(ordering)
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122 }
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123 ### Initialize command line arguments and call performDataOrdering
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124
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125 options(warn=-1)
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126
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127 args = commandArgs(TRUE)
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128
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129 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])
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130
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131 #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]))
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