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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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29
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8 mergeOn = args[6]
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3
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9
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10 cat("<html><table><tr><td>Starting analysis</td></tr>", file=logfile, append=F)
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0
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11
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4
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12 library(ggplot2)
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13 library(reshape2)
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14 library(data.table)
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15 library(grid)
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16 library(parallel)
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0
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17 #require(xtable)
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3
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18 cat("<tr><td>Reading input</td></tr>", file=logfile, append=T)
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13
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19 dat = read.table(inFile, header=T, sep="\t", dec=".", fill=T, stringsAsFactors=F)
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34
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20 dat = dat[,c("Patient", "Receptor", "Sample", "Cell_Count", "Clone_Molecule_Count_From_Spikes", "Log10_Frequency", "Total_Read_Count", "J_Segment_Major_Gene", "V_Segment_Major_Gene", "CDR3_Sense_Sequence", "Related_to_leukemia_clone", "Clone_Sequence")]
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21 dat$dsPerM = 0
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9
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22 dat = dat[!is.na(dat$Patient),]
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13
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23 dat$Related_to_leukemia_clone = F
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9
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24
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0
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25 setwd(outDir)
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3
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26 cat("<tr><td>Selecting first V/J Genes</td></tr>", file=logfile, append=T)
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2
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27 dat$V_Segment_Major_Gene = as.factor(as.character(lapply(strsplit(as.character(dat$V_Segment_Major_Gene), "; "), "[[", 1)))
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28 dat$J_Segment_Major_Gene = as.factor(as.character(lapply(strsplit(as.character(dat$J_Segment_Major_Gene), "; "), "[[", 1)))
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29
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3
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30 cat("<tr><td>Calculating Frequency</td></tr>", file=logfile, append=T)
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12
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31
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13
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32 dat$Frequency = ((10^dat$Log10_Frequency)*100)
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2
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33
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3
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34 dat = dat[dat$Frequency >= min_freq,]
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35
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13
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36 triplets = dat[grepl("VanDongen_cALL_14696", dat$Patient) | grepl("(16278)|(26402)|(26759)", dat$Sample),]
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37
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38 cat("<tr><td>Normalizing to lowest cell count within locus</td></tr>", file=logfile, append=T)
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39
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40 dat$locus_V = substring(dat$V_Segment_Major_Gene, 0, 4)
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41 dat$locus_J = substring(dat$J_Segment_Major_Gene, 0, 4)
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42 min_cell_count = data.frame(data.table(dat)[, list(min_cell_count=min(.SD$Cell_Count)), by=c("Patient", "locus_V", "locus_J")])
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43
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44 dat$min_cell_paste = paste(dat$Patient, dat$locus_V, dat$locus_J)
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45 min_cell_count$min_cell_paste = paste(min_cell_count$Patient, min_cell_count$locus_V, min_cell_count$locus_J)
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46
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47 min_cell_count = min_cell_count[,c("min_cell_paste", "min_cell_count")]
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34
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48 print(paste("rows:", nrow(dat)))
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13
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49 dat = merge(dat, min_cell_count, by="min_cell_paste")
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34
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50 print(paste("rows:", nrow(dat)))
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13
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51 dat$normalized_read_count = round(dat$Clone_Molecule_Count_From_Spikes / dat$Cell_Count * dat$min_cell_count / 2, digits=2) #??????????????????????????????????? wel of geen / 2
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52
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3
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53 dat = dat[dat$normalized_read_count >= min_cells,]
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13
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54
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55 dat$paste = paste(dat$Sample, dat$Clone_Sequence)
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9
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56
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22
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57 patients = split(dat, dat$Patient, drop=T)
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9
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58 intervalReads = rev(c(0,10,25,50,100,250,500,750,1000,10000))
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6
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59 intervalFreq = rev(c(0,0.01,0.05,0.1,0.5,1,5))
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0
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60 V_Segments = c(".*", "IGHV", "IGHD", "IGKV", "IGKV", "IgKINTR", "TRGV", "TRDV", "TRDD" , "TRBV")
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61 J_Segments = c(".*", ".*", ".*", "IGKJ", "KDE", ".*", ".*", ".*", ".*", ".*")
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62 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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63 Titles = factor(Titles, levels=Titles)
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64 TitlesOrder = data.frame("Title"=Titles, "TitlesOrder"=1:length(Titles))
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65
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29
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66 single_patients = data.frame("Patient" = character(0),"Sample" = character(0), "on" = character(0), "Clone_Sequence" = character(0), "Frequency" = numeric(0), "normalized_read_count" = numeric(0), "V_Segment_Major_Gene" = character(0), "J_Segment_Major_Gene" = character(0), "Rearrangement" = character(0))
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67
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51
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68 patient.merge.list = list() #cache the 'both' table, 2x speedup for more memory...
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69 patient.merge.list.second = list()
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56
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70 cat(paste("<table border='0' style='font-family:courier;'>", sep=""), file="multiple_matches.html", append=T)
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71 cat(paste("<table border='0' style='font-family:courier;'>", sep=""), file="single_matches.html", append=T)
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0
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72 patientCountOnColumn <- function(x, product, interval, on, appendtxt=F){
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28
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73 if (!is.data.frame(x) & is.list(x)){
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74 x = x[[1]]
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75 }
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34
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76 #x$Sample = factor(x$Sample, levels=unique(x$Sample))
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77 x = data.frame(x,stringsAsFactors=F)
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0
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78 onShort = "reads"
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79 if(on == "Frequency"){
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80 onShort = "freq"
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81 }
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18
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82 onx = paste(on, ".x", sep="")
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83 ony = paste(on, ".y", sep="")
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0
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84 splt = split(x, x$Sample, drop=T)
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4
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85 type="pair"
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2
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86 if(length(splt) == 1){
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3
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87 print(paste(paste(x[1,which(colnames(x) == "Patient")]), "has one sample"))
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4
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88 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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89 type="single"
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2
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90 }
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0
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91 patient1 = splt[[1]]
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92 patient2 = splt[[2]]
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93
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94 threshholdIndex = which(colnames(product) == "interval")
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95 V_SegmentIndex = which(colnames(product) == "V_Segments")
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96 J_SegmentIndex = which(colnames(product) == "J_Segments")
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97 titleIndex = which(colnames(product) == "Titles")
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98 sampleIndex = which(colnames(x) == "Sample")
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99 patientIndex = which(colnames(x) == "Patient")
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100 oneSample = paste(patient1[1,sampleIndex], sep="")
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101 twoSample = paste(patient2[1,sampleIndex], sep="")
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102 patient = paste(x[1,patientIndex])
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3
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103
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0
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104 switched = F
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105 if(length(grep(".*_Right$", twoSample)) == 1 || length(grep(".*_Dx_BM$", twoSample)) == 1 || length(grep(".*_Dx$", twoSample)) == 1 ){
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106 tmp = twoSample
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107 twoSample = oneSample
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108 oneSample = tmp
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109 tmp = patient1
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110 patient1 = patient2
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111 patient2 = tmp
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112 switched = T
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113 }
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114 if(appendtxt){
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4
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115 cat(paste(patient, oneSample, twoSample, type, sep="\t"), file="patients.txt", append=T, sep="", fill=3)
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0
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116 }
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51
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117 cat(paste("<tr><td>", patient, "</td>", sep=""), file=logfile, append=T)
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9
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118
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29
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119 if(mergeOn == "Clone_Sequence"){
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120 patient1$merge = paste(patient1$Clone_Sequence)
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121 patient2$merge = paste(patient2$Clone_Sequence)
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122 } else {
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123 patient1$merge = paste(patient1$V_Segment_Major_Gene, patient1$J_Segment_Major_Gene, patient1$CDR3_Sense_Sequence)
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124 patient2$merge = paste(patient2$V_Segment_Major_Gene, patient2$J_Segment_Major_Gene, patient2$CDR3_Sense_Sequence)
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125 }
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9
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126
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30
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127 scatterplot_data_columns = c("Patient", "Sample", "Frequency", "normalized_read_count", "V_Segment_Major_Gene", "J_Segment_Major_Gene", "merge")
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29
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128 scatterplot_data = rbind(patient1[,scatterplot_data_columns], patient2[,scatterplot_data_columns])
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30
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129 scatterplot_data = scatterplot_data[!duplicated(scatterplot_data$merge),]
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130 scatterplot_data$type = factor(x=oneSample, levels=c(oneSample, twoSample, "In Both"))
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29
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131 scatterplot_data$on = onShort
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132
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49
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133 #patientMerge = merge(patient1, patient2, by.x="merge", by.y="merge") #merge alles 'fuzzy'
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134 patientMerge = merge(patient1, patient2, by.x="merge", by.y="merge")[NULL,] #blegh
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135
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136 cs.exact.matches = patient1[patient1$Clone_Sequence %in% patient2$Clone_Sequence,]$Clone_Sequence
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137
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51
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138 start.time = proc.time()
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139 merge.list = c()
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140
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141 if(patient %in% names(patient.merge.list)){
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142 patientMerge = patient.merge.list[[patient]]
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143 merge.list[["second"]] = patient.merge.list.second[[patient]]
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144 cat(paste("<td>", nrow(patient1), " in ", oneSample, " and ", nrow(patient2), " in ", twoSample, ", ", nrow(patientMerge), " in both (fetched from cache)</td></tr>", sep=""), file=logfile, append=T)
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145
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146 print(names(patient.merge.list))
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147 } else {
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148 #fuzzy matching here...
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49
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149 #merge.list = patientMerge$merge
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51
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150
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49
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151 #patient1.fuzzy = patient1[!(patient1$merge %in% merge.list),]
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152 #patient2.fuzzy = patient2[!(patient2$merge %in% merge.list),]
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153
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154 patient1.fuzzy = patient1
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155 patient2.fuzzy = patient2
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156
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36
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157 #patient1.fuzzy$merge = paste(patient1.fuzzy$V_Segment_Major_Gene, patient1.fuzzy$J_Segment_Major_Gene, patient1.fuzzy$CDR3_Sense_Sequence)
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158 #patient2.fuzzy$merge = paste(patient2.fuzzy$V_Segment_Major_Gene, patient2.fuzzy$J_Segment_Major_Gene, patient2.fuzzy$CDR3_Sense_Sequence)
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51
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159
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48
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160 #patient1.fuzzy$merge = paste(patient1.fuzzy$locus_V, patient1.fuzzy$locus_J, patient1.fuzzy$CDR3_Sense_Sequence)
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161 #patient2.fuzzy$merge = paste(patient2.fuzzy$locus_V, patient2.fuzzy$locus_J, patient2.fuzzy$CDR3_Sense_Sequence)
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51
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162
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48
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163 patient1.fuzzy$merge = paste(patient1.fuzzy$locus_V, patient1.fuzzy$locus_J)
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164 patient2.fuzzy$merge = paste(patient2.fuzzy$locus_V, patient2.fuzzy$locus_J)
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51
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165
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49
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166 #merge.freq.table = data.frame(table(c(patient1.fuzzy[!duplicated(patient1.fuzzy$merge),"merge"], patient2.fuzzy[!duplicated(patient2.fuzzy$merge),"merge"]))) #also remove?
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167 #merge.freq.table.gt.1 = merge.freq.table[merge.freq.table$Freq > 1,]
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51
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168
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49
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169 #patient1.fuzzy = patient1.fuzzy[patient1.fuzzy$merge %in% merge.freq.table.gt.1$Var1,]
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170 #patient2.fuzzy = patient2.fuzzy[patient2.fuzzy$merge %in% merge.freq.table.gt.1$Var1,]
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51
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171
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37
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172 patient.fuzzy = rbind(patient1.fuzzy, patient2.fuzzy)
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173 patient.fuzzy = patient.fuzzy[order(nchar(patient.fuzzy$Clone_Sequence)),]
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49
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174
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175 merge.list = list()
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176
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177 merge.list[["second"]] = vector()
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178
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37
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179 while(nrow(patient.fuzzy) > 1){
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180 first.merge = patient.fuzzy[1,"merge"]
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181 first.clone.sequence = patient.fuzzy[1,"Clone_Sequence"]
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49
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182 first.sample = patient.fuzzy[1,"Sample"]
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37
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183 merge.filter = first.merge == patient.fuzzy$merge
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51
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184
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49
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185 #length.filter = nchar(patient.fuzzy$Clone_Sequence) - nchar(first.clone.sequence) <= 9
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51
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186
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49
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187 first.sample.filter = first.sample == patient.fuzzy$Sample
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188 second.sample.filter = first.sample != patient.fuzzy$Sample
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189
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190 #first match same sample, sum to a single row, same for other sample
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191 #then merge rows like 'normal'
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51
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192
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48
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193 sequence.filter = grepl(paste("^", first.clone.sequence, sep=""), patient.fuzzy$Clone_Sequence)
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49
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194
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195
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196
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44
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197 #match.filter = merge.filter & grepl(first.clone.sequence, patient.fuzzy$Clone_Sequence) & length.filter & sample.filter
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49
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198 first.match.filter = merge.filter & sequence.filter & first.sample.filter
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199 second.match.filter = merge.filter & sequence.filter & second.sample.filter
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200
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201 first.rows = patient.fuzzy[first.match.filter,]
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202 second.rows = patient.fuzzy[second.match.filter,]
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203
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50
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204 first.rows.v = table(first.rows$V_Segment_Major_Gene)
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205 first.rows.v = names(first.rows.v[which.max(first.rows.v)])
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206 first.rows.j = table(first.rows$J_Segment_Major_Gene)
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207 first.rows.j = names(first.rows.j[which.max(first.rows.j)])
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208
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49
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209 first.sum = data.frame(merge = first.clone.sequence,
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210 Patient = patient,
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211 Receptor = first.rows[1,"Receptor"],
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212 Sample = first.rows[1,"Sample"],
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213 Cell_Count = first.rows[1,"Cell_Count"],
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214 Clone_Molecule_Count_From_Spikes = sum(first.rows$Clone_Molecule_Count_From_Spikes),
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215 Log10_Frequency = log10(sum(first.rows$Frequency)),
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216 Total_Read_Count = sum(first.rows$Total_Read_Count),
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217 dsPerM = sum(first.rows$dsPerM),
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50
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218 J_Segment_Major_Gene = first.rows.j,
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219 V_Segment_Major_Gene = first.rows.v,
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49
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220 Clone_Sequence = first.clone.sequence,
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221 CDR3_Sense_Sequence = first.rows[1,"CDR3_Sense_Sequence"],
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222 Related_to_leukemia_clone = F,
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223 Frequency = sum(first.rows$Frequency),
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224 locus_V = first.rows[1,"locus_V"],
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225 locus_J = first.rows[1,"locus_J"],
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226 min_cell_count = first.rows[1,"min_cell_count"],
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227 normalized_read_count = sum(first.rows$normalized_read_count),
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228 paste = first.rows[1,"paste"],
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229 min_cell_paste = first.rows[1,"min_cell_paste"])
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230
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231 if(nrow(second.rows) > 0){
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50
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232 second.rows.v = table(second.rows$V_Segment_Major_Gene)
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233 second.rows.v = names(second.rows.v[which.max(second.rows.v)])
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234 second.rows.j = table(second.rows$J_Segment_Major_Gene)
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235 second.rows.j = names(second.rows.j[which.max(second.rows.j)])
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236
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55
|
237 second.sum = data.frame(merge = first.clone.sequence,
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49
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238 Patient = patient,
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239 Receptor = second.rows[1,"Receptor"],
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240 Sample = second.rows[1,"Sample"],
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241 Cell_Count = second.rows[1,"Cell_Count"],
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242 Clone_Molecule_Count_From_Spikes = sum(second.rows$Clone_Molecule_Count_From_Spikes),
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243 Log10_Frequency = log10(sum(second.rows$Frequency)),
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244 Total_Read_Count = sum(second.rows$Total_Read_Count),
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245 dsPerM = sum(second.rows$dsPerM),
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50
|
246 J_Segment_Major_Gene = second.rows.j,
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247 V_Segment_Major_Gene = second.rows.v,
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49
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248 Clone_Sequence = first.clone.sequence,
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249 CDR3_Sense_Sequence = second.rows[1,"CDR3_Sense_Sequence"],
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250 Related_to_leukemia_clone = F,
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251 Frequency = sum(second.rows$Frequency),
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252 locus_V = second.rows[1,"locus_V"],
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253 locus_J = second.rows[1,"locus_J"],
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254 min_cell_count = second.rows[1,"min_cell_count"],
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255 normalized_read_count = sum(second.rows$normalized_read_count),
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256 paste = second.rows[1,"paste"],
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257 min_cell_paste = second.rows[1,"min_cell_paste"])
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258
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259 patientMerge = rbind(patientMerge, merge(first.sum, second.sum, by="merge"))
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260 patient.fuzzy = patient.fuzzy[!(first.match.filter | second.match.filter),]
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261
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50
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262 hidden.clone.sequences = c(first.rows[-1,"Clone_Sequence"], second.rows[second.rows$Clone_Sequence != first.clone.sequence,"Clone_Sequence"])
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263 merge.list[["second"]] = append(merge.list[["second"]], hidden.clone.sequences)
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|
49
|
264
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56
|
265 tmp.rows = rbind(first.rows, second.rows)
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266 tmp.rows = tmp.rows[order(nchar(tmp.rows$Clone_Sequence)),]
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267
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268 if (nrow(first.rows) > 1 | nrow(second.rows) > 1) {
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269 cat(paste("<tr><td>", patient, " row ", 1:nrow(tmp.rows), "</td><td>", tmp.rows$Sample, ":</td><td>", tmp.rows$Clone_Sequence, "</td><td>", tmp.rows$normalized_read_count, "</td></tr>", sep=""), file="multiple_matches.html", append=T)
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270 } else {
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271 second.clone.sequence = second.rows[1,"Clone_Sequence"]
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272 if(nchar(first.clone.sequence) != nchar(second.clone.sequence)){
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273 cat(paste("<tr bgcolor='#DDD'><td>", patient, " row ", 1:nrow(tmp.rows), "</td><td>", tmp.rows$Sample, ":</td><td>", tmp.rows$Clone_Sequence, "</td><td>", tmp.rows$normalized_read_count, "</td></tr>", sep=""), file="single_matches.html", append=T)
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|
|
274 } else {
|
|
58
|
275 #cat(paste("<tr><td>", patient, " row ", 1:nrow(tmp.rows), "</td><td>", tmp.rows$Sample, ":</td><td>", tmp.rows$Clone_Sequence, "</td><td>", tmp.rows$normalized_read_count, "</td></tr>", sep=""), file="single_matches.html", append=T)
|
|
49
|
276 }
|
|
|
277 }
|
|
|
278
|
|
59
|
279 } else if(nrow(first.rows) > 1) {
|
|
|
280 if(patient1[1,"Sample"] == first.sample){
|
|
|
281 patient1 = patient1[!(patient1$Clone_Sequence %in% first.rows$Clone_Sequence),]
|
|
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282 patient1 = rbind(patient1, first.sum)
|
|
|
283 } else {
|
|
|
284 patient2 = patient2[!(patient2$Clone_Sequence %in% first.rows$Clone_Sequence),]
|
|
|
285 patient2 = rbind(patient2, first.sum)
|
|
|
286 }
|
|
|
287
|
|
|
288 hidden.clone.sequences = c(first.rows[-1,"Clone_Sequence"])
|
|
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289 merge.list[["second"]] = append(merge.list[["second"]], hidden.clone.sequences)
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|
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290
|
|
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291 patient.fuzzy = patient.fuzzy[-first.match.filter,]
|
|
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292
|
|
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293 cat(paste("<tr bgcolor='#DDF'><td>", patient, " row ", 1:nrow(first.rows), "</td><td>", first.rows$Sample, ":</td><td>", first.rows$Clone_Sequence, "</td><td>", first.rows$normalized_read_count, "</td></tr>", sep=""), file="single_matches.html", append=T)
|
|
37
|
294 } else {
|
|
|
295 patient.fuzzy = patient.fuzzy[-1,]
|
|
34
|
296 }
|
|
|
297 }
|
|
51
|
298 patient.merge.list[[patient]] <<- patientMerge
|
|
|
299 patient.merge.list.second[[patient]] <<- merge.list[["second"]]
|
|
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300 cat(paste("<td>", nrow(patient1), " in ", oneSample, " and ", nrow(patient2), " in ", twoSample, ", ", nrow(patientMerge), " in both (finding both took ", (proc.time() - start.time)[[3]], "s)</td></tr>", sep=""), file=logfile, append=T)
|
|
34
|
301 }
|
|
51
|
302
|
|
52
|
303 patient1 = patient1[!(patient1$Clone_Sequence %in% patient.merge.list.second[[patient]]),]
|
|
|
304 patient2 = patient2[!(patient2$Clone_Sequence %in% patient.merge.list.second[[patient]]),]
|
|
51
|
305
|
|
37
|
306
|
|
18
|
307 patientMerge$thresholdValue = pmax(patientMerge[,onx], patientMerge[,ony])
|
|
0
|
308 res1 = vector()
|
|
|
309 res2 = vector()
|
|
|
310 resBoth = vector()
|
|
|
311 read1Count = vector()
|
|
|
312 read2Count = vector()
|
|
2
|
313 locussum1 = vector()
|
|
|
314 locussum2 = vector()
|
|
9
|
315
|
|
0
|
316 #for(iter in 1){
|
|
|
317 for(iter in 1:length(product[,1])){
|
|
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318 threshhold = product[iter,threshholdIndex]
|
|
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319 V_Segment = paste(".*", as.character(product[iter,V_SegmentIndex]), ".*", sep="")
|
|
|
320 J_Segment = paste(".*", as.character(product[iter,J_SegmentIndex]), ".*", sep="")
|
|
18
|
321 #both = (grepl(V_Segment, patientMerge$V_Segment_Major_Gene.x) & grepl(J_Segment, patientMerge$J_Segment_Major_Gene.x) & patientMerge[,onx] > threshhold & patientMerge[,ony] > threshhold) #both higher than threshold
|
|
29
|
322 both = (grepl(V_Segment, patientMerge$V_Segment_Major_Gene.x) & grepl(J_Segment, patientMerge$J_Segment_Major_Gene.x) & patientMerge$thresholdValue > threshhold) #highest of both is higher than threshold
|
|
30
|
323 one = (grepl(V_Segment, patient1$V_Segment_Major_Gene) & grepl(J_Segment, patient1$J_Segment_Major_Gene) & patient1[,on] > threshhold & !(patient1$merge %in% patientMerge[both,]$merge))
|
|
|
324 two = (grepl(V_Segment, patient2$V_Segment_Major_Gene) & grepl(J_Segment, patient2$J_Segment_Major_Gene) & patient2[,on] > threshhold & !(patient2$merge %in% patientMerge[both,]$merge))
|
|
14
|
325 read1Count = append(read1Count, sum(patient1[one,]$normalized_read_count))
|
|
|
326 read2Count = append(read2Count, sum(patient2[two,]$normalized_read_count))
|
|
0
|
327 res1 = append(res1, sum(one))
|
|
2
|
328 res2 = append(res2, sum(two))
|
|
0
|
329 resBoth = append(resBoth, sum(both))
|
|
2
|
330 locussum1 = append(locussum1, sum(patient1[(grepl(V_Segment, patient1$V_Segment_Major_Gene) & grepl(J_Segment, patient1$J_Segment_Major_Gene)),]$normalized_read_count))
|
|
|
331 locussum2 = append(locussum2, sum(patient2[(grepl(V_Segment, patient2$V_Segment_Major_Gene) & grepl(J_Segment, patient2$J_Segment_Major_Gene)),]$normalized_read_count))
|
|
0
|
332 #threshhold = 0
|
|
|
333 if(threshhold != 0){
|
|
|
334 if(sum(one) > 0){
|
|
15
|
335 dfOne = patient1[one,c("V_Segment_Major_Gene", "J_Segment_Major_Gene", "normalized_read_count", "Frequency", "Clone_Sequence", "Related_to_leukemia_clone")]
|
|
|
336 colnames(dfOne) = c("Proximal segment", "Distal segment", "normalized_read_count", "Frequency", "Clone Sequence", "Related_to_leukemia_clone")
|
|
0
|
337 filenameOne = paste(oneSample, "_", product[iter, titleIndex], "_", threshhold, sep="")
|
|
|
338 write.table(dfOne, file=paste(filenameOne, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T)
|
|
|
339 }
|
|
|
340 if(sum(two) > 0){
|
|
15
|
341 dfTwo = patient2[two,c("V_Segment_Major_Gene", "J_Segment_Major_Gene", "normalized_read_count", "Frequency", "Clone_Sequence", "Related_to_leukemia_clone")]
|
|
|
342 colnames(dfTwo) = c("Proximal segment", "Distal segment", "normalized_read_count", "Frequency", "Clone Sequence", "Related_to_leukemia_clone")
|
|
0
|
343 filenameTwo = paste(twoSample, "_", product[iter, titleIndex], "_", threshhold, sep="")
|
|
|
344 write.table(dfTwo, file=paste(filenameTwo, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T)
|
|
|
345 }
|
|
29
|
346 } else {
|
|
|
347 scatterplot_locus_data = scatterplot_data[grepl(V_Segment, scatterplot_data$V_Segment_Major_Gene) & grepl(J_Segment, scatterplot_data$J_Segment_Major_Gene),]
|
|
49
|
348 #scatterplot_locus_data = scatterplot_locus_data[!(scatterplot_locus_data$merge %in% merge.list[[twoSample]]),]
|
|
|
349 scatterplot_locus_data = scatterplot_locus_data[!(scatterplot_locus_data$merge %in% merge.list[["second"]]),]
|
|
29
|
350 if(nrow(scatterplot_locus_data) > 0){
|
|
|
351 scatterplot_locus_data$Rearrangement = product[iter, titleIndex]
|
|
|
352 }
|
|
34
|
353 in_one = (scatterplot_locus_data$merge %in% patient1$merge)
|
|
|
354 in_two = (scatterplot_locus_data$merge %in% patient2$merge)
|
|
|
355 if(any(in_two)){
|
|
49
|
356 scatterplot_locus_data[in_two,]$type = twoSample
|
|
34
|
357 }
|
|
49
|
358 in_both = (scatterplot_locus_data$merge %in% patientMerge$merge)
|
|
|
359 #merge.list.filter = (scatterplot_locus_data$merge %in% merge.list[[oneSample]])
|
|
|
360 #exact.matches.filter = (scatterplot_locus_data$merge %in% cs.exact.matches)
|
|
30
|
361 if(any(in_both)){
|
|
|
362 scatterplot_locus_data[in_both,]$type = "In Both"
|
|
|
363 }
|
|
29
|
364 if(type == "single"){
|
|
|
365 single_patients <<- rbind(single_patients, scatterplot_locus_data)
|
|
|
366 }
|
|
|
367 p = NULL
|
|
|
368 if(nrow(scatterplot_locus_data) != 0){
|
|
|
369 if(on == "normalized_read_count"){
|
|
30
|
370 scales = 10^(0:6) #(0:ceiling(log10(max(scatterplot_locus_data$normalized_read_count))))
|
|
49
|
371 p = ggplot(scatterplot_locus_data, aes(type, normalized_read_count)) + scale_y_log10(breaks=scales,labels=scales) + expand_limits(y=10^6) + scale_x_discrete(breaks=levels(scatterplot_data$type), labels=levels(scatterplot_data$type), drop=FALSE)
|
|
29
|
372 } else {
|
|
49
|
373 p = ggplot(scatterplot_locus_data, aes(type, Frequency)) + scale_y_continuous(limits = c(0, 100)) + expand_limits(y=c(0,100)) + scale_x_discrete(breaks=levels(scatterplot_data$type), labels=levels(scatterplot_data$type), drop=FALSE)
|
|
29
|
374 }
|
|
|
375 p = p + geom_point(aes(colour=type), position="jitter")
|
|
|
376 p = p + xlab("In one or both samples") + ylab(onShort) + ggtitle(paste(patient1[1,patientIndex], patient1[1,sampleIndex], patient2[1,sampleIndex], onShort, product[iter, titleIndex]))
|
|
|
377 } else {
|
|
|
378 p = ggplot(NULL, aes(x=c("In one", "In Both"),y=0)) + geom_blank(NULL) + xlab("In one or both of the samples") + ylab(onShort) + ggtitle(paste(patient1[1,patientIndex], patient1[1,sampleIndex], patient2[1,sampleIndex], onShort, product[iter, titleIndex]))
|
|
|
379 }
|
|
|
380 png(paste(patient1[1,patientIndex], "_", patient1[1,sampleIndex], "_", patient2[1,sampleIndex], "_", onShort, "_", product[iter, titleIndex],"_scatter.png", sep=""))
|
|
|
381 print(p)
|
|
|
382 dev.off()
|
|
0
|
383 }
|
|
|
384 if(sum(both) > 0){
|
|
15
|
385 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.x", "V_Segment_Major_Gene.y", "J_Segment_Major_Gene.y", "normalized_read_count.y", "Frequency.y", "Related_to_leukemia_clone.y")]
|
|
|
386 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),"Clone Sequence", paste("Proximal segment", twoSample), paste("Distal segment", twoSample), paste("Normalized_Read_Count", twoSample), paste("Frequency", twoSample), paste("Related_to_leukemia_clone", twoSample))
|
|
0
|
387 filenameBoth = paste(oneSample, "_", twoSample, "_", product[iter, titleIndex], "_", threshhold, sep="")
|
|
|
388 write.table(dfBoth, file=paste(filenameBoth, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T)
|
|
29
|
389 }
|
|
0
|
390 }
|
|
2
|
391 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)
|
|
0
|
392 if(sum(is.na(patientResult$percentage)) > 0){
|
|
|
393 patientResult[is.na(patientResult$percentage),]$percentage = 0
|
|
|
394 }
|
|
|
395 colnames(patientResult)[6] = oneSample
|
|
|
396 colnames(patientResult)[8] = twoSample
|
|
|
397 colnamesBak = colnames(patientResult)
|
|
2
|
398 colnames(patientResult) = c("Ig/TCR gene rearrangement type", "Distal Gene segment", "Proximal gene segment", "cut_off_value", paste("Number of sequences ", patient, "_Both", sep=""), paste("Number of sequences", oneSample, sep=""), paste("Normalized Read Count", oneSample), paste("Number of sequences", twoSample, sep=""), paste("Normalized Read Count", twoSample), paste("Sum number of sequences", patient), paste("Percentage of sequences ", patient, "_Both", sep=""), paste("Locus Sum", oneSample), paste("Locus Sum", twoSample))
|
|
0
|
399 write.table(patientResult, file=paste(patient, "_", onShort, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T)
|
|
|
400 colnames(patientResult) = colnamesBak
|
|
|
401
|
|
|
402 patientResult$Locus = factor(patientResult$Locus, Titles)
|
|
|
403 patientResult$cut_off_value = factor(patientResult$cut_off_value, paste(">", interval, sep=""))
|
|
|
404
|
|
|
405 plt = ggplot(patientResult[,c("Locus", "cut_off_value", "Both")])
|
|
|
406 plt = plt + geom_bar( aes( x=factor(cut_off_value), y=Both), stat='identity', position="dodge", fill="#79c36a")
|
|
|
407 plt = plt + facet_grid(.~Locus) + theme(axis.text.x = element_text(angle = 45, hjust = 1))
|
|
|
408 plt = plt + geom_text(aes(ymax=max(Both), x=cut_off_value,y=Both,label=Both), angle=90, hjust=0)
|
|
|
409 plt = plt + xlab("Reads per locus") + ylab("Count") + ggtitle("Number of clones in both")
|
|
|
410 plt = plt + theme(plot.margin = unit(c(1,8.8,0.5,1.5), "lines"))
|
|
|
411 png(paste(patient, "_", onShort, ".png", sep=""), width=1920, height=1080)
|
|
|
412 print(plt)
|
|
|
413 dev.off()
|
|
|
414 #(t,r,b,l)
|
|
|
415 plt = ggplot(patientResult[,c("Locus", "cut_off_value", "percentage")])
|
|
|
416 plt = plt + geom_bar( aes( x=factor(cut_off_value), y=percentage), stat='identity', position="dodge", fill="#79c36a")
|
|
|
417 plt = plt + facet_grid(.~Locus) + theme(axis.text.x = element_text(angle = 45, hjust = 1))
|
|
|
418 plt = plt + geom_text(aes(ymax=max(percentage), x=cut_off_value,y=percentage,label=percentage), angle=90, hjust=0)
|
|
|
419 plt = plt + xlab("Reads per locus") + ylab("Count") + ggtitle("% clones in both left and right")
|
|
|
420 plt = plt + theme(plot.margin = unit(c(1,8.8,0.5,1.5), "lines"))
|
|
|
421 png(paste(patient, "_percent_", onShort, ".png", sep=""), width=1920, height=1080)
|
|
|
422 print(plt)
|
|
|
423 dev.off()
|
|
|
424
|
|
|
425 patientResult = melt(patientResult[,c('Locus','cut_off_value', oneSample, twoSample)] ,id.vars=1:2)
|
|
|
426 patientResult$relativeValue = patientResult$value * 10
|
|
|
427 patientResult[patientResult$relativeValue == 0,]$relativeValue = 1
|
|
|
428 plt = ggplot(patientResult)
|
|
|
429 plt = plt + geom_bar( aes( x=factor(cut_off_value), y=relativeValue, fill=variable), stat='identity', position="dodge")
|
|
|
430 plt = plt + facet_grid(.~Locus) + theme(axis.text.x = element_text(angle = 45, hjust = 1))
|
|
|
431 plt = plt + scale_y_continuous(trans="log", breaks=10^c(0:10), labels=c(0, 10^c(0:9)))
|
|
|
432 plt = plt + geom_text(data=patientResult[patientResult$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)
|
|
|
433 plt = plt + geom_text(data=patientResult[patientResult$variable == twoSample,], aes(ymax=max(value), x=cut_off_value,y=relativeValue,label=value), angle=90, position=position_dodge(width=0.9), hjust=0, vjust=0.8)
|
|
|
434 plt = plt + xlab("Reads per locus") + ylab("Count") + ggtitle(paste("Number of clones in only ", oneSample, " and only ", twoSample, sep=""))
|
|
|
435 png(paste(patient, "_", onShort, "_both.png", sep=""), width=1920, height=1080)
|
|
|
436 print(plt)
|
|
|
437 dev.off()
|
|
|
438 }
|
|
|
439
|
|
3
|
440 cat("<tr><td>Starting Frequency analysis</td></tr>", file=logfile, append=T)
|
|
|
441
|
|
0
|
442 interval = intervalFreq
|
|
|
443 intervalOrder = data.frame("interval"=paste(">", interval, sep=""), "intervalOrder"=1:length(interval))
|
|
4
|
444 product = data.frame("Titles"=rep(Titles, each=length(interval)), "interval"=rep(interval, times=10), "V_Segments"=rep(V_Segments, each=length(interval)), "J_Segments"=rep(J_Segments, each=length(interval)))
|
|
29
|
445 lapply(patients, FUN=patientCountOnColumn, product = product, interval=interval, on="Frequency", appendtxt=T)
|
|
0
|
446
|
|
3
|
447 cat("<tr><td>Starting Cell Count analysis</td></tr>", file=logfile, append=T)
|
|
|
448
|
|
0
|
449 interval = intervalReads
|
|
|
450 intervalOrder = data.frame("interval"=paste(">", interval, sep=""), "intervalOrder"=1:length(interval))
|
|
4
|
451 product = data.frame("Titles"=rep(Titles, each=length(interval)), "interval"=rep(interval, times=10), "V_Segments"=rep(V_Segments, each=length(interval)), "J_Segments"=rep(J_Segments, each=length(interval)))
|
|
29
|
452 lapply(patients, FUN=patientCountOnColumn, product = product, interval=interval, on="normalized_read_count")
|
|
0
|
453
|
|
3
|
454 cat("</table></html>", file=logfile, append=T)
|
|
|
455
|
|
33
|
456
|
|
|
457 if(nrow(single_patients) > 0){
|
|
|
458 scales = 10^(0:6) #(0:ceiling(log10(max(scatterplot_locus_data$normalized_read_count))))
|
|
|
459 p = ggplot(single_patients, aes(Rearrangement, normalized_read_count)) + scale_y_log10(breaks=scales,labels=scales) + expand_limits(y=c(0,1000000))
|
|
|
460 p = p + geom_point(aes(colour=type), position="jitter")
|
|
|
461 p = p + xlab("In one or both samples") + ylab("Reads")
|
|
|
462 p = p + facet_grid(.~Patient) + ggtitle("Scatterplot of the reads of the patients with a single sample")
|
|
|
463 png("singles_reads_scatterplot.png", width=640 * length(unique(single_patients$Patient)) + 100, height=1080)
|
|
|
464 print(p)
|
|
|
465 dev.off()
|
|
7
|
466
|
|
33
|
467 p = ggplot(single_patients, aes(Rearrangement, Frequency)) + scale_y_continuous(limits = c(0, 100)) + expand_limits(y=c(0,100))
|
|
|
468 p = p + geom_point(aes(colour=type), position="jitter")
|
|
|
469 p = p + xlab("In one or both samples") + ylab("Frequency")
|
|
|
470 p = p + facet_grid(.~Patient) + ggtitle("Scatterplot of the frequency of the patients with a single sample")
|
|
|
471 png("singles_freq_scatterplot.png", width=640 * length(unique(single_patients$Patient)) + 100, height=1080)
|
|
|
472 print(p)
|
|
|
473 dev.off()
|
|
|
474 } else {
|
|
|
475 empty <- data.frame()
|
|
|
476 p = ggplot(empty) + geom_point() + xlim(0, 10) + ylim(0, 100) + xlab("In one or both samples") + ylab("Frequency") + ggtitle("Scatterplot of the frequency of the patients with a single sample")
|
|
|
477
|
|
|
478 png("singles_reads_scatterplot.png", width=400, height=300)
|
|
|
479 print(p)
|
|
|
480 dev.off()
|
|
|
481
|
|
|
482 png("singles_freq_scatterplot.png", width=400, height=300)
|
|
|
483 print(p)
|
|
|
484 dev.off()
|
|
|
485 }
|
|
7
|
486 tripletAnalysis <- function(patient1, label1, patient2, label2, patient3, label3, product, interval, on, appendTriplets= FALSE){
|
|
|
487 onShort = "reads"
|
|
|
488 if(on == "Frequency"){
|
|
|
489 onShort = "freq"
|
|
|
490 }
|
|
18
|
491 onx = paste(on, ".x", sep="")
|
|
|
492 ony = paste(on, ".y", sep="")
|
|
|
493 onz = paste(on, ".z", sep="")
|
|
7
|
494 type="triplet"
|
|
|
495
|
|
|
496 threshholdIndex = which(colnames(product) == "interval")
|
|
|
497 V_SegmentIndex = which(colnames(product) == "V_Segments")
|
|
|
498 J_SegmentIndex = which(colnames(product) == "J_Segments")
|
|
|
499 titleIndex = which(colnames(product) == "Titles")
|
|
|
500 sampleIndex = which(colnames(patient1) == "Sample")
|
|
|
501 patientIndex = which(colnames(patient1) == "Patient")
|
|
|
502 oneSample = paste(patient1[1,sampleIndex], sep="")
|
|
|
503 twoSample = paste(patient2[1,sampleIndex], sep="")
|
|
|
504 threeSample = paste(patient3[1,sampleIndex], sep="")
|
|
|
505
|
|
29
|
506 if(mergeOn == "Clone_Sequence"){
|
|
|
507 patient1$merge = paste(patient1$Clone_Sequence)
|
|
|
508 patient2$merge = paste(patient2$Clone_Sequence)
|
|
|
509 patient3$merge = paste(patient3$Clone_Sequence)
|
|
|
510
|
|
|
511 } else {
|
|
|
512 patient1$merge = paste(patient1$V_Segment_Major_Gene, patient1$J_Segment_Major_Gene, patient1$CDR3_Sense_Sequence)
|
|
|
513 patient2$merge = paste(patient2$V_Segment_Major_Gene, patient2$J_Segment_Major_Gene, patient2$CDR3_Sense_Sequence)
|
|
|
514 patient3$merge = paste(patient3$V_Segment_Major_Gene, patient3$J_Segment_Major_Gene, patient3$CDR3_Sense_Sequence)
|
|
|
515 }
|
|
9
|
516
|
|
|
517 patientMerge = merge(patient1, patient2, by="merge")
|
|
|
518 patientMerge = merge(patientMerge, patient3, by="merge")
|
|
28
|
519 colnames(patientMerge)[which(!grepl("(\\.x$)|(\\.y$)|(merge)", names(patientMerge)))] = paste(colnames(patientMerge)[which(!grepl("(\\.x$)|(\\.y$)|(merge)", names(patientMerge), perl=T))], ".z", sep="")
|
|
18
|
520 patientMerge$thresholdValue = pmax(patientMerge[,onx], patientMerge[,ony], patientMerge[,onz])
|
|
20
|
521 patientMerge12 = merge(patient1, patient2, by="merge")
|
|
|
522 patientMerge12$thresholdValue = pmax(patientMerge12[,onx], patientMerge12[,ony])
|
|
|
523 patientMerge13 = merge(patient1, patient3, by="merge")
|
|
|
524 patientMerge13$thresholdValue = pmax(patientMerge13[,onx], patientMerge13[,ony])
|
|
|
525 patientMerge23 = merge(patient2, patient3, by="merge")
|
|
|
526 patientMerge23$thresholdValue = pmax(patientMerge23[,onx], patientMerge23[,ony])
|
|
|
527
|
|
28
|
528
|
|
30
|
529 scatterplot_data_columns = c("Clone_Sequence", "Frequency", "normalized_read_count", "V_Segment_Major_Gene", "J_Segment_Major_Gene", "merge")
|
|
20
|
530 scatterplot_data = rbind(patient1[,scatterplot_data_columns], patient2[,scatterplot_data_columns], patient3[,scatterplot_data_columns])
|
|
30
|
531 scatterplot_data = scatterplot_data[!duplicated(scatterplot_data$merge),]
|
|
27
|
532 scatterplot_data$type = factor(x="In one", levels=c("In one", "In two", "In three", "In multiple"))
|
|
20
|
533
|
|
7
|
534 res1 = vector()
|
|
|
535 res2 = vector()
|
|
|
536 res3 = vector()
|
|
20
|
537 res12 = vector()
|
|
|
538 res13 = vector()
|
|
|
539 res23 = vector()
|
|
7
|
540 resAll = vector()
|
|
|
541 read1Count = vector()
|
|
|
542 read2Count = vector()
|
|
|
543 read3Count = vector()
|
|
|
544
|
|
|
545 if(appendTriplets){
|
|
9
|
546 cat(paste(label1, label2, label3, sep="\t"), file="triplets.txt", append=T, sep="", fill=3)
|
|
7
|
547 }
|
|
|
548 for(iter in 1:length(product[,1])){
|
|
|
549 threshhold = product[iter,threshholdIndex]
|
|
|
550 V_Segment = paste(".*", as.character(product[iter,V_SegmentIndex]), ".*", sep="")
|
|
|
551 J_Segment = paste(".*", as.character(product[iter,J_SegmentIndex]), ".*", sep="")
|
|
18
|
552 #all = (grepl(V_Segment, patientMerge$V_Segment_Major_Gene.x) & grepl(J_Segment, patientMerge$J_Segment_Major_Gene.x) & patientMerge[,onx] > threshhold & patientMerge[,ony] > threshhold & patientMerge[,onz] > threshhold)
|
|
|
553 all = (grepl(V_Segment, patientMerge$V_Segment_Major_Gene.x) & grepl(J_Segment, patientMerge$J_Segment_Major_Gene.x) & patientMerge$thresholdValue > threshhold)
|
|
7
|
554
|
|
30
|
555 one_two = (grepl(V_Segment, patientMerge12$V_Segment_Major_Gene.x) & grepl(J_Segment, patientMerge12$J_Segment_Major_Gene.x) & patientMerge12$thresholdValue > threshhold & !(patientMerge12$merge %in% patientMerge[all,]$merge))
|
|
|
556 one_three = (grepl(V_Segment, patientMerge13$V_Segment_Major_Gene.x) & grepl(J_Segment, patientMerge13$J_Segment_Major_Gene.x) & patientMerge13$thresholdValue > threshhold & !(patientMerge13$merge %in% patientMerge[all,]$merge))
|
|
|
557 two_three = (grepl(V_Segment, patientMerge23$V_Segment_Major_Gene.x) & grepl(J_Segment, patientMerge23$J_Segment_Major_Gene.x) & patientMerge23$thresholdValue > threshhold & !(patientMerge23$merge %in% patientMerge[all,]$merge))
|
|
24
|
558
|
|
30
|
559 one = (grepl(V_Segment, patient1$V_Segment_Major_Gene) & grepl(J_Segment, patient1$J_Segment_Major_Gene) & patient1[,on] > threshhold & !(patient1$merge %in% patientMerge[all,]$merge) & !(patient1$merge %in% patientMerge12[one_two,]$merge) & !(patient1$merge %in% patientMerge13[one_three,]$merge))
|
|
|
560 two = (grepl(V_Segment, patient2$V_Segment_Major_Gene) & grepl(J_Segment, patient2$J_Segment_Major_Gene) & patient2[,on] > threshhold & !(patient2$merge %in% patientMerge[all,]$merge) & !(patient2$merge %in% patientMerge12[one_two,]$merge) & !(patient2$merge %in% patientMerge23[two_three,]$merge))
|
|
|
561 three = (grepl(V_Segment, patient3$V_Segment_Major_Gene) & grepl(J_Segment, patient3$J_Segment_Major_Gene) & patient3[,on] > threshhold & !(patient3$merge %in% patientMerge[all,]$merge) & !(patient3$merge %in% patientMerge13[one_three,]$merge) & !(patient3$merge %in% patientMerge23[two_three,]$merge))
|
|
20
|
562
|
|
18
|
563 read1Count = append(read1Count, sum(patient1[one,]$normalized_read_count) + sum(patientMerge[all,]$normalized_read_count.x))
|
|
|
564 read2Count = append(read2Count, sum(patient2[two,]$normalized_read_count) + sum(patientMerge[all,]$normalized_read_count.y))
|
|
|
565 read3Count = append(read3Count, sum(patient3[three,]$normalized_read_count) + sum(patientMerge[all,]$normalized_read_count.z))
|
|
7
|
566 res1 = append(res1, sum(one))
|
|
|
567 res2 = append(res2, sum(two))
|
|
|
568 res3 = append(res3, sum(three))
|
|
|
569 resAll = append(resAll, sum(all))
|
|
20
|
570 res12 = append(res12, sum(one_two))
|
|
|
571 res13 = append(res13, sum(one_three))
|
|
|
572 res23 = append(res23, sum(two_three))
|
|
7
|
573 #threshhold = 0
|
|
|
574 if(threshhold != 0){
|
|
|
575 if(sum(one) > 0){
|
|
20
|
576 dfOne = patient1[one,c("V_Segment_Major_Gene", "J_Segment_Major_Gene", "normalized_read_count", "Frequency", "Clone_Sequence", "Related_to_leukemia_clone")]
|
|
|
577 colnames(dfOne) = c("Proximal segment", "Distal segment", "normalized_read_count", "Frequency", "Clone_Sequence", "Related_to_leukemia_clone")
|
|
7
|
578 filenameOne = paste(label1, "_", product[iter, titleIndex], "_", threshhold, sep="")
|
|
|
579 write.table(dfOne, file=paste(filenameOne, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T)
|
|
|
580 }
|
|
|
581 if(sum(two) > 0){
|
|
20
|
582 dfTwo = patient2[two,c("V_Segment_Major_Gene", "J_Segment_Major_Gene", "normalized_read_count", "Frequency", "Clone_Sequence", "Related_to_leukemia_clone")]
|
|
|
583 colnames(dfTwo) = c("Proximal segment", "Distal segment", "normalized_read_count", "Frequency", "Clone_Sequence", "Related_to_leukemia_clone")
|
|
7
|
584 filenameTwo = paste(label2, "_", product[iter, titleIndex], "_", threshhold, sep="")
|
|
|
585 write.table(dfTwo, file=paste(filenameTwo, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T)
|
|
|
586 }
|
|
|
587 if(sum(three) > 0){
|
|
20
|
588 dfThree = patient3[three,c("V_Segment_Major_Gene", "J_Segment_Major_Gene", "normalized_read_count", "Frequency", "Clone_Sequence", "Related_to_leukemia_clone")]
|
|
|
589 colnames(dfThree) = c("Proximal segment", "Distal segment", "normalized_read_count", "Frequency", "Clone_Sequence", "Related_to_leukemia_clone")
|
|
7
|
590 filenameThree = paste(label3, "_", product[iter, titleIndex], "_", threshhold, sep="")
|
|
|
591 write.table(dfThree, file=paste(filenameThree, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T)
|
|
|
592 }
|
|
20
|
593 if(sum(one_two) > 0){
|
|
|
594 dfOne_two = patientMerge12[one_two,c("V_Segment_Major_Gene.x", "J_Segment_Major_Gene.x", "normalized_read_count.x", "Frequency.x", "Related_to_leukemia_clone.x", "Clone_Sequence.x", "V_Segment_Major_Gene.y", "J_Segment_Major_Gene.y", "normalized_read_count.y", "Frequency.y", "Related_to_leukemia_clone.y")]
|
|
|
595 colnames(dfOne_two) = c(paste("Proximal segment", oneSample), paste("Distal segment", oneSample), paste("Normalized_Read_Count", oneSample), paste("Frequency", oneSample), paste("Related_to_leukemia_clone", oneSample),"Clone_Sequence", paste("Proximal segment", twoSample), paste("Distal segment", twoSample), paste("Normalized_Read_Count", twoSample), paste("Frequency", twoSample), paste("Related_to_leukemia_clone", twoSample))
|
|
|
596 filenameOne_two = paste(label1, "_", label2, "_", product[iter, titleIndex], "_", threshhold, onShort, sep="")
|
|
|
597 write.table(dfOne_two, file=paste(filenameOne_two, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T)
|
|
|
598 }
|
|
|
599 if(sum(one_three) > 0){
|
|
|
600 dfOne_three = patientMerge13[one_three,c("V_Segment_Major_Gene.x", "J_Segment_Major_Gene.x", "normalized_read_count.x", "Frequency.x", "Related_to_leukemia_clone.x", "Clone_Sequence.x", "V_Segment_Major_Gene.y", "J_Segment_Major_Gene.y", "normalized_read_count.y", "Frequency.y", "Related_to_leukemia_clone.y")]
|
|
|
601 colnames(dfOne_three) = c(paste("Proximal segment", oneSample), paste("Distal segment", oneSample), paste("Normalized_Read_Count", oneSample), paste("Frequency", oneSample), paste("Related_to_leukemia_clone", oneSample),"Clone_Sequence", paste("Proximal segment", threeSample), paste("Distal segment", threeSample), paste("Normalized_Read_Count", threeSample), paste("Frequency", threeSample), paste("Related_to_leukemia_clone", threeSample))
|
|
|
602 filenameOne_three = paste(label1, "_", label3, "_", product[iter, titleIndex], "_", threshhold, onShort, sep="")
|
|
|
603 write.table(dfOne_three, file=paste(filenameOne_three, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T)
|
|
|
604 }
|
|
|
605 if(sum(two_three) > 0){
|
|
|
606 dfTwo_three = patientMerge23[two_three,c("V_Segment_Major_Gene.x", "J_Segment_Major_Gene.x", "normalized_read_count.x", "Frequency.x", "Related_to_leukemia_clone.x", "Clone_Sequence.x", "V_Segment_Major_Gene.y", "J_Segment_Major_Gene.y", "normalized_read_count.y", "Frequency.y", "Related_to_leukemia_clone.y")]
|
|
|
607 colnames(dfTwo_three) = c(paste("Proximal segment", twoSample), paste("Distal segment", twoSample), paste("Normalized_Read_Count", twoSample), paste("Frequency", twoSample), paste("Related_to_leukemia_clone", twoSample),"Clone_Sequence", paste("Proximal segment", threeSample), paste("Distal segment", threeSample), paste("Normalized_Read_Count", threeSample), paste("Frequency", threeSample), paste("Related_to_leukemia_clone", threeSample))
|
|
|
608 filenameTwo_three = paste(label2, "_", label3, "_", product[iter, titleIndex], "_", threshhold, onShort, sep="")
|
|
|
609 write.table(dfTwo_three, file=paste(filenameTwo_three, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T)
|
|
|
610 }
|
|
|
611 } else { #scatterplot data
|
|
24
|
612 scatterplot_locus_data = scatterplot_data[grepl(V_Segment, scatterplot_data$V_Segment_Major_Gene) & grepl(J_Segment, scatterplot_data$J_Segment_Major_Gene),]
|
|
30
|
613 in_two = (scatterplot_locus_data$merge %in% patientMerge12[one_two,]$merge) | (scatterplot_locus_data$merge %in% patientMerge13[one_three,]$merge) | (scatterplot_locus_data$merge %in% patientMerge23[two_three,]$merge)
|
|
27
|
614 if(sum(in_two) > 0){
|
|
|
615 scatterplot_locus_data[in_two,]$type = "In two"
|
|
|
616 }
|
|
30
|
617 in_three = (scatterplot_locus_data$merge %in% patientMerge[all,]$merge)
|
|
27
|
618 if(sum(in_three)> 0){
|
|
|
619 scatterplot_locus_data[in_three,]$type = "In three"
|
|
|
620 }
|
|
|
621 not_in_one = scatterplot_locus_data$type != "In one"
|
|
|
622 if(sum(not_in_one) > 0){
|
|
|
623 scatterplot_locus_data[not_in_one,]$type = "In multiple"
|
|
|
624 }
|
|
20
|
625 p = NULL
|
|
|
626 if(nrow(scatterplot_locus_data) != 0){
|
|
|
627 if(on == "normalized_read_count"){
|
|
31
|
628 scales = 10^(0:6) #(0:ceiling(log10(max(scatterplot_locus_data$normalized_read_count))))
|
|
32
|
629 p = ggplot(scatterplot_locus_data, aes(type, normalized_read_count)) + scale_y_log10(breaks=scales,labels=scales) + expand_limits(y=c(0,1000000))
|
|
20
|
630 } else {
|
|
32
|
631 p = ggplot(scatterplot_locus_data, aes(type, Frequency)) + scale_y_continuous(limits = c(0, 100)) + expand_limits(y=c(0,100))
|
|
20
|
632 }
|
|
|
633 p = p + geom_point(aes(colour=type), position="jitter")
|
|
25
|
634 p = p + xlab("In one or in multiple samples") + ylab(onShort) + ggtitle(paste(label1, label2, label3, onShort, product[iter, titleIndex]))
|
|
20
|
635 } else {
|
|
|
636 p = ggplot(NULL, aes(x=c("In one", "In multiple"),y=0)) + geom_blank(NULL) + xlab("In two or in three of the samples") + ylab(onShort) + ggtitle(paste(label1, label2, label3, onShort, product[iter, titleIndex]))
|
|
|
637 }
|
|
|
638 png(paste(label1, "_", label2, "_", label3, "_", onShort, "_", product[iter, titleIndex],"_scatter.png", sep=""))
|
|
|
639 print(p)
|
|
|
640 dev.off()
|
|
|
641 }
|
|
7
|
642 if(sum(all) > 0){
|
|
20
|
643 dfAll = patientMerge[all,c("V_Segment_Major_Gene.x", "J_Segment_Major_Gene.x", "normalized_read_count.x", "Frequency.x", "Related_to_leukemia_clone.x", "Clone_Sequence.x", "V_Segment_Major_Gene.y", "J_Segment_Major_Gene.y", "normalized_read_count.y", "Frequency.y", "Related_to_leukemia_clone.y", "V_Segment_Major_Gene.z", "J_Segment_Major_Gene.z", "normalized_read_count.z", "Frequency.z", "Related_to_leukemia_clone.z")]
|
|
|
644 colnames(dfAll) = c(paste("Proximal segment", oneSample), paste("Distal segment", oneSample), paste("Normalized_Read_Count", oneSample), paste("Frequency", oneSample), paste("Related_to_leukemia_clone", oneSample),"Clone_Sequence", paste("Proximal segment", twoSample), paste("Distal segment", twoSample), paste("Normalized_Read_Count", twoSample), paste("Frequency", twoSample), paste("Related_to_leukemia_clone", twoSample), paste("Proximal segment", threeSample), paste("Distal segment", threeSample), paste("Normalized_Read_Count", threeSample), paste("Frequency", threeSample), paste("Related_to_leukemia_clone", threeSample))
|
|
7
|
645 filenameAll = paste(label1, "_", label2, "_", label3, "_", product[iter, titleIndex], "_", threshhold, sep="")
|
|
|
646 write.table(dfAll, file=paste(filenameAll, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T)
|
|
|
647 }
|
|
|
648 }
|
|
20
|
649 #patientResult = data.frame("Locus"=product$Titles, "J_Segment"=product$J_Segments, "V_Segment"=product$V_Segments, "cut_off_value"=paste(">", product$interval, sep=""), "All"=resAll, "tmp1"=res1, "read_count1" = round(read1Count), "tmp2"=res2, "read_count2"= round(read2Count), "tmp3"=res3, "read_count3"=round(read3Count))
|
|
|
650 patientResult = data.frame("Locus"=product$Titles, "J_Segment"=product$J_Segments, "V_Segment"=product$V_Segments, "cut_off_value"=paste(">", product$interval, sep=""), "All"=resAll, "tmp1"=res1, "tmp2"=res2, "tmp3"=res3, "tmp12"=res12, "tmp13"=res13, "tmp23"=res23)
|
|
7
|
651 colnames(patientResult)[6] = oneSample
|
|
20
|
652 colnames(patientResult)[7] = twoSample
|
|
|
653 colnames(patientResult)[8] = threeSample
|
|
|
654 colnames(patientResult)[9] = paste(oneSample, twoSample, sep="_")
|
|
|
655 colnames(patientResult)[10] = paste(oneSample, twoSample, sep="_")
|
|
|
656 colnames(patientResult)[11] = paste(oneSample, twoSample, sep="_")
|
|
7
|
657
|
|
|
658 colnamesBak = colnames(patientResult)
|
|
20
|
659 colnames(patientResult) = c("Ig/TCR gene rearrangement type", "Distal Gene segment", "Proximal gene segment", "cut_off_value", "Number of sequences All", paste("Number of sequences", oneSample), paste("Number of sequences", twoSample), paste("Number of sequences", threeSample), paste("Number of sequences", oneSample, twoSample), paste("Number of sequences", oneSample, threeSample), paste("Number of sequences", twoSample, threeSample))
|
|
7
|
660 write.table(patientResult, file=paste(label1, "_", label2, "_", label3, "_", onShort, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T)
|
|
|
661 colnames(patientResult) = colnamesBak
|
|
|
662
|
|
|
663 patientResult$Locus = factor(patientResult$Locus, Titles)
|
|
|
664 patientResult$cut_off_value = factor(patientResult$cut_off_value, paste(">", interval, sep=""))
|
|
|
665
|
|
|
666 plt = ggplot(patientResult[,c("Locus", "cut_off_value", "All")])
|
|
|
667 plt = plt + geom_bar( aes( x=factor(cut_off_value), y=All), stat='identity', position="dodge", fill="#79c36a")
|
|
|
668 plt = plt + facet_grid(.~Locus) + theme(axis.text.x = element_text(angle = 45, hjust = 1))
|
|
|
669 plt = plt + geom_text(aes(ymax=max(All), x=cut_off_value,y=All,label=All), angle=90, hjust=0)
|
|
|
670 plt = plt + xlab("Reads per locus") + ylab("Count") + ggtitle("Number of clones in All")
|
|
|
671 plt = plt + theme(plot.margin = unit(c(1,8.8,0.5,1.5), "lines"))
|
|
|
672 png(paste(label1, "_", label2, "_", label3, "_", onShort, "_total_all.png", sep=""), width=1920, height=1080)
|
|
|
673 print(plt)
|
|
|
674 dev.off()
|
|
|
675
|
|
|
676 fontSize = 4
|
|
|
677
|
|
|
678 bak = patientResult
|
|
|
679 patientResult = melt(patientResult[,c('Locus','cut_off_value', oneSample, twoSample, threeSample)] ,id.vars=1:2)
|
|
|
680 patientResult$relativeValue = patientResult$value * 10
|
|
|
681 patientResult[patientResult$relativeValue == 0,]$relativeValue = 1
|
|
|
682 plt = ggplot(patientResult)
|
|
|
683 plt = plt + geom_bar( aes( x=factor(cut_off_value), y=relativeValue, fill=variable), stat='identity', position="dodge")
|
|
|
684 plt = plt + facet_grid(.~Locus) + theme(axis.text.x = element_text(angle = 45, hjust = 1))
|
|
|
685 plt = plt + scale_y_continuous(trans="log", breaks=10^c(0:10), labels=c(0, 10^c(0:9)))
|
|
|
686 plt = plt + geom_text(data=patientResult[patientResult$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=-0.7, size=fontSize)
|
|
|
687 plt = plt + geom_text(data=patientResult[patientResult$variable == twoSample,], aes(ymax=max(value), x=cut_off_value,y=relativeValue,label=value), angle=90, position=position_dodge(width=0.9), hjust=0, vjust=0.4, size=fontSize)
|
|
|
688 plt = plt + geom_text(data=patientResult[patientResult$variable == threeSample,], aes(ymax=max(value), x=cut_off_value,y=relativeValue,label=value), angle=90, position=position_dodge(width=0.9), hjust=0, vjust=1.5, size=fontSize)
|
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689 plt = plt + xlab("Reads per locus") + ylab("Count") + ggtitle("Number of clones in only one sample")
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690 png(paste(label1, "_", label2, "_", label3, "_", onShort, "_indiv_all.png", sep=""), width=1920, height=1080)
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691 print(plt)
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692 dev.off()
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693 }
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694
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33
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695 if(nrow(triplets) != 0){
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696 triplets$uniqueID = "ID"
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697
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698 triplets[grepl("16278_Left", triplets$Sample),]$uniqueID = "16278_26402_26759_Left"
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699 triplets[grepl("26402_Left", triplets$Sample),]$uniqueID = "16278_26402_26759_Left"
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700 triplets[grepl("26759_Left", triplets$Sample),]$uniqueID = "16278_26402_26759_Left"
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|
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701
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702 triplets[grepl("16278_Right", triplets$Sample),]$uniqueID = "16278_26402_26759_Right"
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703 triplets[grepl("26402_Right", triplets$Sample),]$uniqueID = "16278_26402_26759_Right"
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704 triplets[grepl("26759_Right", triplets$Sample),]$uniqueID = "16278_26402_26759_Right"
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705
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706 triplets[grepl("14696", triplets$Patient),]$uniqueID = "14696"
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|
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707
|
|
|
708 triplets$locus_V = substring(triplets$V_Segment_Major_Gene, 0, 4)
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709 triplets$locus_J = substring(triplets$J_Segment_Major_Gene, 0, 4)
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710 min_cell_count = data.frame(data.table(triplets)[, list(min_cell_count=min(.SD$Cell_Count)), by=c("uniqueID", "locus_V", "locus_J")])
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711
|
|
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712 triplets$min_cell_paste = paste(triplets$uniqueID, triplets$locus_V, triplets$locus_J)
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713 min_cell_count$min_cell_paste = paste(min_cell_count$uniqueID, min_cell_count$locus_V, min_cell_count$locus_J)
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|
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714
|
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|
715 min_cell_count = min_cell_count[,c("min_cell_paste", "min_cell_count")]
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716
|
|
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717 triplets = merge(triplets, min_cell_count, by="min_cell_paste")
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718
|
|
|
719 triplets$normalized_read_count = round(triplets$Clone_Molecule_Count_From_Spikes / triplets$Cell_Count * triplets$min_cell_count / 2, digits=2) #??????????????????????????????????? wel of geen / 2
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720
|
|
|
721 triplets = triplets[triplets$normalized_read_count >= min_cells,]
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|
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722
|
|
|
723 column_drops = c("locus_V", "locus_J", "min_cell_count", "min_cell_paste")
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|
|
724
|
|
|
725 triplets = triplets[,!(colnames(triplets) %in% column_drops)]
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726
|
|
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727 #remove duplicate V+J+CDR3, add together numerical values
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|
|
728 triplets = data.frame(data.table(triplets)[, list(Receptor=unique(.SD$Receptor),
|
|
|
729 Cell_Count=unique(.SD$Cell_Count),
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|
730 Clone_Molecule_Count_From_Spikes=sum(.SD$Clone_Molecule_Count_From_Spikes),
|
|
|
731 Total_Read_Count=sum(.SD$Total_Read_Count),
|
|
|
732 dsPerM=ifelse("dsPerM" %in% names(dat), sum(.SD$dsPerM), 0),
|
|
|
733 Related_to_leukemia_clone=all(.SD$Related_to_leukemia_clone),
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|
|
734 Frequency=sum(.SD$Frequency),
|
|
|
735 normalized_read_count=sum(.SD$normalized_read_count),
|
|
|
736 Log10_Frequency=sum(.SD$Log10_Frequency),
|
|
|
737 Clone_Sequence=.SD$Clone_Sequence[1]), by=c("Patient", "Sample", "V_Segment_Major_Gene", "J_Segment_Major_Gene", "CDR3_Sense_Sequence")])
|
|
|
738
|
|
|
739
|
|
|
740 interval = intervalReads
|
|
|
741 intervalOrder = data.frame("interval"=paste(">", interval, sep=""), "intervalOrder"=1:length(interval))
|
|
|
742 product = data.frame("Titles"=rep(Titles, each=length(interval)), "interval"=rep(interval, times=10), "V_Segments"=rep(V_Segments, each=length(interval)), "J_Segments"=rep(J_Segments, each=length(interval)))
|
|
|
743
|
|
|
744 one = triplets[triplets$Sample == "14696_reg_BM",]
|
|
|
745 two = triplets[triplets$Sample == "24536_reg_BM",]
|
|
|
746 three = triplets[triplets$Sample == "24062_reg_BM",]
|
|
53
|
747 tripletAnalysis(one, "14696_1_Trio", two, "14696_2_Trio", three, "14696_3_Trio", product=product, interval=interval, on="normalized_read_count", T)
|
|
33
|
748
|
|
|
749 one = triplets[triplets$Sample == "16278_Left",]
|
|
|
750 two = triplets[triplets$Sample == "26402_Left",]
|
|
|
751 three = triplets[triplets$Sample == "26759_Left",]
|
|
53
|
752 tripletAnalysis(one, "16278_Left_Trio", two, "26402_Left_Trio", three, "26759_Left_Trio", product=product, interval=interval, on="normalized_read_count", T)
|
|
33
|
753
|
|
|
754 one = triplets[triplets$Sample == "16278_Right",]
|
|
|
755 two = triplets[triplets$Sample == "26402_Right",]
|
|
|
756 three = triplets[triplets$Sample == "26759_Right",]
|
|
53
|
757 tripletAnalysis(one, "16278_Right_Trio", two, "26402_Right_Trio", three, "26759_Right_Trio", product=product, interval=interval, on="normalized_read_count", T)
|
|
33
|
758
|
|
|
759
|
|
|
760 interval = intervalFreq
|
|
|
761 intervalOrder = data.frame("interval"=paste(">", interval, sep=""), "intervalOrder"=1:length(interval))
|
|
|
762 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)))
|
|
|
763
|
|
|
764 one = triplets[triplets$Sample == "14696_reg_BM",]
|
|
|
765 two = triplets[triplets$Sample == "24536_reg_BM",]
|
|
|
766 three = triplets[triplets$Sample == "24062_reg_BM",]
|
|
53
|
767 tripletAnalysis(one, "14696_1_Trio", two, "14696_2_Trio", three, "14696_3_Trio", product=product, interval=interval, on="Frequency", F)
|
|
33
|
768
|
|
|
769 one = triplets[triplets$Sample == "16278_Left",]
|
|
|
770 two = triplets[triplets$Sample == "26402_Left",]
|
|
|
771 three = triplets[triplets$Sample == "26759_Left",]
|
|
53
|
772 tripletAnalysis(one, "16278_Left_Trio", two, "26402_Left_Trio", three, "26759_Left_Trio", product=product, interval=interval, on="Frequency", F)
|
|
33
|
773
|
|
|
774 one = triplets[triplets$Sample == "16278_Right",]
|
|
|
775 two = triplets[triplets$Sample == "26402_Right",]
|
|
|
776 three = triplets[triplets$Sample == "26759_Right",]
|
|
53
|
777 tripletAnalysis(one, "16278_Right_Trio", two, "26402_Right_Trio", three, "26759_Right_Trio", product=product, interval=interval, on="Frequency", F)
|
|
33
|
778 } else {
|
|
|
779 cat("", file="triplets.txt")
|
|
|
780 }
|
