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0
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1 args <- commandArgs(trailingOnly = TRUE)
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2
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3 inFile = args[1]
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4 outDir = args[2]
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3
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5 logfile = args[3]
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6 min_freq = as.numeric(args[4])
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7 min_cells = as.numeric(args[5])
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8
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9 cat("<html><table><tr><td>Starting analysis</td></tr>", file=logfile, append=F)
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10
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4
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11 library(ggplot2)
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12 library(reshape2)
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13 library(data.table)
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14 library(grid)
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15 library(parallel)
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0
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16 #require(xtable)
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3
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17 cat("<tr><td>Reading input</td></tr>", file=logfile, append=T)
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2
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18 dat = read.csv(inFile, sep="\t")
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19 #dat = data.frame(fread(inFile)) #faster but with a dep
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20 setwd(outDir)
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3
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21 cat("<tr><td>Selecting first V/J Genes</td></tr>", file=logfile, append=T)
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2
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22 dat$V_Segment_Major_Gene = as.factor(as.character(lapply(strsplit(as.character(dat$V_Segment_Major_Gene), "; "), "[[", 1)))
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23 dat$J_Segment_Major_Gene = as.factor(as.character(lapply(strsplit(as.character(dat$J_Segment_Major_Gene), "; "), "[[", 1)))
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24
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3
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25 cat("<tr><td>Calculating Frequency</td></tr>", file=logfile, append=T)
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26 dat$Frequency = ((10^dat$Log10_Frequency)*100)
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2
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27
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3
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28 dat = dat[dat$Frequency >= min_freq,]
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29
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30 cat("<tr><td>Normalizing cell count to 1.000.000</td></tr>", file=logfile, append=T)
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2
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31 dat$normalized_read_count = round(dat$Clone_Molecule_Count_From_Spikes / dat$Cell_Count * 1000000 / 2)
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32 dat = dat[dat$normalized_read_count >= min_cells,]
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2
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33 dat$paste = paste(dat$Sample, dat$V_Segment_Major_Gene, dat$J_Segment_Major_Gene, dat$CDR3_Sense_Sequence)
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34 cat("<tr><td>Removing duplicates</td></tr>", file=logfile, append=T)
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35 dat = dat[!duplicated(dat$paste),]
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36 patients = split(dat, dat$Patient, drop=T)
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4
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37 rm(dat)
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38 intervalReads = rev(c(0,10,25,50,100,1000,10000))
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6
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39 intervalFreq = rev(c(0,0.01,0.05,0.1,0.5,1,5))
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40 V_Segments = c(".*", "IGHV", "IGHD", "IGKV", "IGKV", "IgKINTR", "TRGV", "TRDV", "TRDD" , "TRBV")
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41 J_Segments = c(".*", ".*", ".*", "IGKJ", "KDE", ".*", ".*", ".*", ".*", ".*")
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42 Titles = c("Total", "IGH-Vh-Jh", "IGH-Dh-Jh", "Vk-Jk", "Vk-Kde" , "Intron-Kde", "TCRG", "TCRD-Vd-Dd", "TCRD-Dd-Dd", "TCRB-Vb-Jb")
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43 Titles = factor(Titles, levels=Titles)
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44 TitlesOrder = data.frame("Title"=Titles, "TitlesOrder"=1:length(Titles))
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45
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46 patientCountOnColumn <- function(x, product, interval, on, appendtxt=F){
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47 x$Sample = factor(x$Sample, levels=unique(x$Sample))
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48 onShort = "reads"
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49 if(on == "Frequency"){
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50 onShort = "freq"
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51 }
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52 splt = split(x, x$Sample, drop=T)
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4
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53 type="pair"
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2
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54 if(length(splt) == 1){
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55 print(paste(paste(x[1,which(colnames(x) == "Patient")]), "has one sample"))
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4
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56 splt[[2]] = data.frame("Patient" = character(0), "Receptor" = character(0), "Sample" = character(0), "Cell_Count" = numeric(0), "Clone_Molecule_Count_From_Spikes" = numeric(0), "Log10_Frequency" = numeric(0), "Total_Read_Count" = numeric(0), "dsMol_per_1e6_cells" = numeric(0), "J_Segment_Major_Gene" = character(0), "V_Segment_Major_Gene" = character(0), "Clone_Sequence" = character(0), "CDR3_Sense_Sequence" = character(0), "Related_to_leukemia_clone" = logical(0), "Frequency"= numeric(0), "normalized_read_count" = numeric(0), "paste" = character(0))
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57 type="single"
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2
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58 }
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59 patient1 = splt[[1]]
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60 patient2 = splt[[2]]
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61
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62 threshholdIndex = which(colnames(product) == "interval")
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63 V_SegmentIndex = which(colnames(product) == "V_Segments")
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64 J_SegmentIndex = which(colnames(product) == "J_Segments")
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65 titleIndex = which(colnames(product) == "Titles")
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66 sampleIndex = which(colnames(x) == "Sample")
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67 patientIndex = which(colnames(x) == "Patient")
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68 oneSample = paste(patient1[1,sampleIndex], sep="")
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69 twoSample = paste(patient2[1,sampleIndex], sep="")
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70 patient = paste(x[1,patientIndex])
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3
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71
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0
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72 switched = F
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73 if(length(grep(".*_Right$", twoSample)) == 1 || length(grep(".*_Dx_BM$", twoSample)) == 1 || length(grep(".*_Dx$", twoSample)) == 1 ){
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74 tmp = twoSample
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75 twoSample = oneSample
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76 oneSample = tmp
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77 tmp = patient1
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78 patient1 = patient2
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79 patient2 = tmp
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80 switched = T
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81 }
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82 if(appendtxt){
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83 cat(paste(patient, oneSample, twoSample, type, sep="\t"), file="patients.txt", append=T, sep="", fill=3)
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84 }
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85 cat(paste("<tr><td>", patient, "</td></tr>", sep=""), file=logfile, append=T)
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86 patientMerge = merge(patient1, patient2, by="Clone_Sequence")
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87 res1 = vector()
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88 res2 = vector()
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89 resBoth = vector()
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90 read1Count = vector()
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91 read2Count = vector()
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2
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92 locussum1 = vector()
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93 locussum2 = vector()
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94
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2
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95 print(patient)
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0
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96 #for(iter in 1){
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97 for(iter in 1:length(product[,1])){
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98 threshhold = product[iter,threshholdIndex]
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99 V_Segment = paste(".*", as.character(product[iter,V_SegmentIndex]), ".*", sep="")
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100 J_Segment = paste(".*", as.character(product[iter,J_SegmentIndex]), ".*", sep="")
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101 both = (grepl(V_Segment, patientMerge$V_Segment_Major_Gene.x) & grepl(J_Segment, patientMerge$J_Segment_Major_Gene.x) & patientMerge[,paste(on, ".x", sep="")] > threshhold & patientMerge[,paste(on, ".y", sep="")] > threshhold)
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102 one = (grepl(V_Segment, patient1$V_Segment_Major_Gene) & grepl(J_Segment, patient1$J_Segment_Major_Gene) & patient1[,on] > threshhold & !(patient1$Clone_Sequence %in% patientMerge[both,]$Clone_Sequence))
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103 two = (grepl(V_Segment, patient2$V_Segment_Major_Gene) & grepl(J_Segment, patient2$J_Segment_Major_Gene) & patient2[,on] > threshhold & !(patient2$Clone_Sequence %in% patientMerge[both,]$Clone_Sequence))
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2
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104 read1Count = append(read1Count, sum(patient1[one,]$normalized_read_count) + sum(patientMerge[both,]$normalized_read_count.x))
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105 read2Count = append(read2Count, sum(patient2[two,]$normalized_read_count) + sum(patientMerge[both,]$normalized_read_count.y))
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106 res1 = append(res1, sum(one))
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107 res2 = append(res2, sum(two))
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108 resBoth = append(resBoth, sum(both))
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2
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109 locussum1 = append(locussum1, sum(patient1[(grepl(V_Segment, patient1$V_Segment_Major_Gene) & grepl(J_Segment, patient1$J_Segment_Major_Gene)),]$normalized_read_count))
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110 locussum2 = append(locussum2, sum(patient2[(grepl(V_Segment, patient2$V_Segment_Major_Gene) & grepl(J_Segment, patient2$J_Segment_Major_Gene)),]$normalized_read_count))
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0
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111 #threshhold = 0
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112 if(threshhold != 0){
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113 if(sum(one) > 0){
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4
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114 dfOne = patient1[one,c("V_Segment_Major_Gene", "J_Segment_Major_Gene", "normalized_read_count", "Frequency", "Clone_Sequence", "Related_to_leukemia_clone")]
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115 colnames(dfOne) = c("Proximal segment", "Distal segment", "normalized_read_count", "Frequency", "Sequence", "Related_to_leukemia_clone")
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116 filenameOne = paste(oneSample, "_", product[iter, titleIndex], "_", threshhold, sep="")
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117 write.table(dfOne, file=paste(filenameOne, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T)
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118 }
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119 if(sum(two) > 0){
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4
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120 dfTwo = patient2[two,c("V_Segment_Major_Gene", "J_Segment_Major_Gene", "normalized_read_count", "Frequency", "Clone_Sequence", "Related_to_leukemia_clone")]
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121 colnames(dfTwo) = c("Proximal segment", "Distal segment", "normalized_read_count", "Frequency", "Sequence", "Related_to_leukemia_clone")
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122 filenameTwo = paste(twoSample, "_", product[iter, titleIndex], "_", threshhold, sep="")
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123 write.table(dfTwo, file=paste(filenameTwo, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T)
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124 }
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125 }
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126 if(sum(both) > 0){
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4
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127 dfBoth = patientMerge[both,c("V_Segment_Major_Gene.x", "J_Segment_Major_Gene.x", "normalized_read_count.x", "Frequency.x", "Related_to_leukemia_clone.x", "Clone_Sequence", "V_Segment_Major_Gene.y", "J_Segment_Major_Gene.y", "normalized_read_count.y", "Frequency.y", "Related_to_leukemia_clone.y")]
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128 colnames(dfBoth) = c(paste("Proximal segment", oneSample), paste("Distal segment", oneSample), paste("Normalized_Read_Count", oneSample), paste("Frequency", oneSample), paste("Related_to_leukemia_clone", oneSample),"Sequence", paste("Proximal segment", twoSample), paste("Distal segment", twoSample), paste("Normalized_Read_Count", twoSample), paste("Frequency", twoSample), paste("Related_to_leukemia_clone", twoSample))
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129 filenameBoth = paste(oneSample, "_", twoSample, "_", product[iter, titleIndex], "_", threshhold, sep="")
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130 write.table(dfBoth, file=paste(filenameBoth, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T)
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131 }
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132 }
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2
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133 patientResult = data.frame("Locus"=product$Titles, "J_Segment"=product$J_Segments, "V_Segment"=product$V_Segments, "cut_off_value"=paste(">", product$interval, sep=""), "Both"=resBoth, "tmp1"=res1, "read_count1" = round(read1Count), "tmp2"=res2, "read_count2"= round(read2Count), "Sum"=res1 + res2 + resBoth, "percentage" = round((resBoth/(res1 + res2 + resBoth)) * 100, digits=2), "Locus_sum1"=locussum1, "Locus_sum2"=locussum2)
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134 if(sum(is.na(patientResult$percentage)) > 0){
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135 patientResult[is.na(patientResult$percentage),]$percentage = 0
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136 }
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137 colnames(patientResult)[6] = oneSample
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138 colnames(patientResult)[8] = twoSample
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139 colnamesBak = colnames(patientResult)
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2
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140 colnames(patientResult) = c("Ig/TCR gene rearrangement type", "Distal Gene segment", "Proximal gene segment", "cut_off_value", paste("Number of sequences ", patient, "_Both", sep=""), paste("Number of sequences", oneSample, sep=""), paste("Normalized Read Count", oneSample), paste("Number of sequences", twoSample, sep=""), paste("Normalized Read Count", twoSample), paste("Sum number of sequences", patient), paste("Percentage of sequences ", patient, "_Both", sep=""), paste("Locus Sum", oneSample), paste("Locus Sum", twoSample))
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141 write.table(patientResult, file=paste(patient, "_", onShort, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T)
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142 colnames(patientResult) = colnamesBak
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143
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144 patientResult$Locus = factor(patientResult$Locus, Titles)
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145 patientResult$cut_off_value = factor(patientResult$cut_off_value, paste(">", interval, sep=""))
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146
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147 plt = ggplot(patientResult[,c("Locus", "cut_off_value", "Both")])
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148 plt = plt + geom_bar( aes( x=factor(cut_off_value), y=Both), stat='identity', position="dodge", fill="#79c36a")
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149 plt = plt + facet_grid(.~Locus) + theme(axis.text.x = element_text(angle = 45, hjust = 1))
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150 plt = plt + geom_text(aes(ymax=max(Both), x=cut_off_value,y=Both,label=Both), angle=90, hjust=0)
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151 plt = plt + xlab("Reads per locus") + ylab("Count") + ggtitle("Number of clones in both")
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152 plt = plt + theme(plot.margin = unit(c(1,8.8,0.5,1.5), "lines"))
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153 png(paste(patient, "_", onShort, ".png", sep=""), width=1920, height=1080)
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154 print(plt)
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155 dev.off()
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156 #(t,r,b,l)
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157 plt = ggplot(patientResult[,c("Locus", "cut_off_value", "percentage")])
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158 plt = plt + geom_bar( aes( x=factor(cut_off_value), y=percentage), stat='identity', position="dodge", fill="#79c36a")
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159 plt = plt + facet_grid(.~Locus) + theme(axis.text.x = element_text(angle = 45, hjust = 1))
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160 plt = plt + geom_text(aes(ymax=max(percentage), x=cut_off_value,y=percentage,label=percentage), angle=90, hjust=0)
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161 plt = plt + xlab("Reads per locus") + ylab("Count") + ggtitle("% clones in both left and right")
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162 plt = plt + theme(plot.margin = unit(c(1,8.8,0.5,1.5), "lines"))
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163 png(paste(patient, "_percent_", onShort, ".png", sep=""), width=1920, height=1080)
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164 print(plt)
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165 dev.off()
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166
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167 patientResult = melt(patientResult[,c('Locus','cut_off_value', oneSample, twoSample)] ,id.vars=1:2)
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168 patientResult$relativeValue = patientResult$value * 10
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169 patientResult[patientResult$relativeValue == 0,]$relativeValue = 1
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170 plt = ggplot(patientResult)
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171 plt = plt + geom_bar( aes( x=factor(cut_off_value), y=relativeValue, fill=variable), stat='identity', position="dodge")
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172 plt = plt + facet_grid(.~Locus) + theme(axis.text.x = element_text(angle = 45, hjust = 1))
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173 plt = plt + scale_y_continuous(trans="log", breaks=10^c(0:10), labels=c(0, 10^c(0:9)))
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174 plt = plt + geom_text(data=patientResult[patientResult$variable == oneSample,], aes(ymax=max(value), x=cut_off_value,y=relativeValue,label=value), angle=90, position=position_dodge(width=0.9), hjust=0, vjust=-0.2)
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175 plt = plt + geom_text(data=patientResult[patientResult$variable == twoSample,], aes(ymax=max(value), x=cut_off_value,y=relativeValue,label=value), angle=90, position=position_dodge(width=0.9), hjust=0, vjust=0.8)
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176 plt = plt + xlab("Reads per locus") + ylab("Count") + ggtitle(paste("Number of clones in only ", oneSample, " and only ", twoSample, sep=""))
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177 png(paste(patient, "_", onShort, "_both.png", sep=""), width=1920, height=1080)
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178 print(plt)
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179 dev.off()
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180 }
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181
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3
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182 cat("<tr><td>Starting Frequency analysis</td></tr>", file=logfile, append=T)
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183
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0
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184 interval = intervalFreq
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185 intervalOrder = data.frame("interval"=paste(">", interval, sep=""), "intervalOrder"=1:length(interval))
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4
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186 product = data.frame("Titles"=rep(Titles, each=length(interval)), "interval"=rep(interval, times=10), "V_Segments"=rep(V_Segments, each=length(interval)), "J_Segments"=rep(J_Segments, each=length(interval)))
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187 #patientFrequencyCount(patient1)
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188 #lapply(patients[c(5,6,10)], FUN=patientFrequencyCount)
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189 #lapply(patients[c(5,6,7,8,13)], FUN=patientCountOnColumn, product = product, interval=interval, on="Frequency", appendtxt=T)
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190 #lapply(patients[c(6,7,8)], FUN=patientCountOnColumn, product = product, interval=interval, on="Frequency", appendtxt=T)
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191 #lapply(patients[c(6)], FUN=patientCountOnColumn, product = product, interval=interval, on="Frequency", appendtxt=T)
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4
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192 mclapply(patients, FUN=patientCountOnColumn, product = product, interval=interval, on="Frequency", appendtxt=T)
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0
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193
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3
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194 cat("<tr><td>Starting Cell Count analysis</td></tr>", file=logfile, append=T)
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195
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0
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196 interval = intervalReads
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197 intervalOrder = data.frame("interval"=paste(">", interval, sep=""), "intervalOrder"=1:length(interval))
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4
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198 product = data.frame("Titles"=rep(Titles, each=length(interval)), "interval"=rep(interval, times=10), "V_Segments"=rep(V_Segments, each=length(interval)), "J_Segments"=rep(J_Segments, each=length(interval)))
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0
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199 #patientResult = patientReadCount(patient1)
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200 #lapply(patients[c(5,6,10)], FUN=patientReadCount)
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201 #lapply(patients[c(5,6,7,8,13)], FUN=patientCountOnColumn, product = product, interval=interval, on="Clone_Molecule_Count_From_Spikes")
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202 #lapply(patients[c(6)], FUN=patientCountOnColumn, product = product, interval=interval, on="Clone_Molecule_Count_From_Spikes")
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4
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203 mclapply(patients, FUN=patientCountOnColumn, product = product, interval=interval, on="Clone_Molecule_Count_From_Spikes")
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0
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204
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3
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205 cat("</table></html>", file=logfile, append=T)
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206
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