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11
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1 # ---------------------- load/install packages ----------------------
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0
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2
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3 if (!("gridExtra" %in% rownames(installed.packages()))) {
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11
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4 install.packages("gridExtra", repos="http://cran.xl-mirror.nl/")
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0
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5 }
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6 library(gridExtra)
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7 if (!("ggplot2" %in% rownames(installed.packages()))) {
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11
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8 install.packages("ggplot2", repos="http://cran.xl-mirror.nl/")
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0
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9 }
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11
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10 library(ggplot2)
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0
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11 if (!("plyr" %in% rownames(installed.packages()))) {
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11
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12 install.packages("plyr", repos="http://cran.xl-mirror.nl/")
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0
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13 }
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11
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14 library(plyr)
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0
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15
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16 if (!("data.table" %in% rownames(installed.packages()))) {
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11
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17 install.packages("data.table", repos="http://cran.xl-mirror.nl/")
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0
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18 }
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19 library(data.table)
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20
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6
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21 if (!("reshape2" %in% rownames(installed.packages()))) {
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11
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22 install.packages("reshape2", repos="http://cran.xl-mirror.nl/")
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6
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23 }
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24 library(reshape2)
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25
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11
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26 # ---------------------- parameters ----------------------
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0
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27
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11
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28 args <- commandArgs(trailingOnly = TRUE)
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0
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29
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11
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30 infile = args[1] #path to input file
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31 outfile = args[2] #path to output file
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32 outdir = args[3] #path to output folder (html/images/data)
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33 clonaltype = args[4] #clonaltype definition, or 'none' for no unique filtering
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21
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34 ct = unlist(strsplit(clonaltype, ","))
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11
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35 species = args[5] #human or mouse
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36 locus = args[6] # IGH, IGK, IGL, TRB, TRA, TRG or TRD
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37 filterproductive = ifelse(args[7] == "yes", T, F) #should unproductive sequences be filtered out? (yes/no)
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38
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39 # ---------------------- Data preperation ----------------------
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40
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41 inputdata = read.table(infile, sep="\t", header=TRUE, fill=T, comment.char="")
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0
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42
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11
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43 setwd(outdir)
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44
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45 # remove weird rows
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21
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46 inputdata = inputdata[inputdata$Sample != "",]
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0
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47
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11
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48 #remove the allele from the V,D and J genes
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49 inputdata$Top.V.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.V.Gene)
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50 inputdata$Top.D.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.D.Gene)
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51 inputdata$Top.J.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.J.Gene)
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21
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52
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11
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53 inputdata$clonaltype = 1:nrow(inputdata)
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21
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54
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11
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55 PRODF = inputdata
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20
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56 UNPROD = inputdata
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11
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57 if(filterproductive){
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58 if("Functionality" %in% colnames(inputdata)) { # "Functionality" is an IMGT column
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59 PRODF = inputdata[inputdata$Functionality == "productive" | inputdata$Functionality == "productive (see comment)", ]
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20
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60 UNPROD = inputdata[!(inputdata$Functionality == "productive" | inputdata$Functionality == "productive (see comment)"), ]
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11
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61 } else {
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62 PRODF = inputdata[inputdata$VDJ.Frame != "In-frame with stop codon" & inputdata$VDJ.Frame != "Out-of-frame" & inputdata$CDR3.Found.How != "NOT_FOUND" , ]
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20
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63 UNPROD = inputdata[!(inputdata$VDJ.Frame != "In-frame with stop codon" & inputdata$VDJ.Frame != "Out-of-frame" & inputdata$CDR3.Found.How != "NOT_FOUND" ), ]
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11
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64 }
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0
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65 }
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66
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25
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67 clonalityFrame = PRODF
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68
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11
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69 #remove duplicates based on the clonaltype
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70 if(clonaltype != "none"){
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21
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71 clonaltype = paste(clonaltype, ",Sample", sep="") #add sample column to clonaltype, unique within samples
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17
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72 PRODF$clonaltype = do.call(paste, c(PRODF[unlist(strsplit(clonaltype, ","))], sep = ":"))
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11
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73 PRODF = PRODF[!duplicated(PRODF$clonaltype), ]
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25
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74
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75 #again for clonalityFrame but with sample+replicate
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76 clonalityFrame$clonaltype = do.call(paste, c(clonalityFrame[unlist(strsplit(clonaltype, ","))], sep = ":"))
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77 clonalityFrame$clonality_clonaltype = do.call(paste, c(clonalityFrame[unlist(strsplit(paste(clonaltype, ",Replicate", sep=""), ","))], sep = ":"))
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78 clonalityFrame = clonalityFrame[!duplicated(clonalityFrame$clonality_clonaltype), ]
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11
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79 }
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0
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80
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11
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81 PRODF$freq = 1
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0
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82
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11
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83 if(any(grepl(pattern="_", x=PRODF$ID))){ #the frequency can be stored in the ID with the pattern ".*_freq_.*"
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84 PRODF$freq = gsub("^[0-9]+_", "", PRODF$ID)
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85 PRODF$freq = gsub("_.*", "", PRODF$freq)
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86 PRODF$freq = as.numeric(PRODF$freq)
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87 if(any(is.na(PRODF$freq))){ #if there was an "_" in the ID, but not the frequency, go back to frequency of 1 for every sequence
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88 PRODF$freq = 1
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89 }
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90 }
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10
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91
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92
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93
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11
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94 #write the complete dataset that is left over, will be the input if 'none' for clonaltype and 'no' for filterproductive
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95 write.table(PRODF, "allUnique.csv", sep=",",quote=F,row.names=F,col.names=T)
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0
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96
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11
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97 #write the samples to a file
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98 sampleFile <- file("samples.txt")
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99 un = unique(inputdata$Sample)
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100 un = paste(un, sep="\n")
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101 writeLines(un, sampleFile)
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102 close(sampleFile)
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103
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15
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104 # ---------------------- Counting the productive/unproductive and unique sequences ----------------------
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105
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106 inputdata.dt = data.table(inputdata) #for speed
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107
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108 if(clonaltype == "none"){
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21
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109 ct = c("clonaltype")
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15
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110 }
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111
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112 inputdata.dt$samples_replicates = paste(inputdata.dt$Sample, inputdata.dt$Replicate, sep="_")
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113 samples_replicates = c(unique(inputdata.dt$samples_replicates), unique(as.character(inputdata.dt$Sample)))
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114 frequency_table = data.frame(ID = samples_replicates[order(samples_replicates)])
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115
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116
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117 sample_productive_count = inputdata.dt[, list(All=.N,
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118 Productive = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",]),
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119 perc_prod = 1,
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120 Productive_unique = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",list(count=.N),by=ct]),
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121 perc_prod_un = 1,
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122 Unproductive= nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",]),
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123 perc_unprod = 1,
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124 Unproductive_unique =nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",list(count=.N),by=ct]),
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125 perc_unprod_un = 1),
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126 by=c("Sample")]
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127
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128 sample_productive_count$perc_prod = round(sample_productive_count$Productive / sample_productive_count$All * 100)
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129 sample_productive_count$perc_prod_un = round(sample_productive_count$Productive_unique / sample_productive_count$All * 100)
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130
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131 sample_productive_count$perc_unprod = round(sample_productive_count$Unproductive / sample_productive_count$All * 100)
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132 sample_productive_count$perc_unprod_un = round(sample_productive_count$Unproductive_unique / sample_productive_count$All * 100)
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133
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134
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135 sample_replicate_productive_count = inputdata.dt[, list(All=.N,
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136 Productive = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",]),
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137 perc_prod = 1,
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138 Productive_unique = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",list(count=.N),by=ct]),
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139 perc_prod_un = 1,
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140 Unproductive= nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",]),
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141 perc_unprod = 1,
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142 Unproductive_unique =nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",list(count=.N),by=ct]),
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143 perc_unprod_un = 1),
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144 by=c("samples_replicates")]
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145
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146 sample_replicate_productive_count$perc_prod = round(sample_replicate_productive_count$Productive / sample_replicate_productive_count$All * 100)
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147 sample_replicate_productive_count$perc_prod_un = round(sample_replicate_productive_count$Productive_unique / sample_replicate_productive_count$All * 100)
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148
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149 sample_replicate_productive_count$perc_unprod = round(sample_replicate_productive_count$Unproductive / sample_replicate_productive_count$All * 100)
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150 sample_replicate_productive_count$perc_unprod_un = round(sample_replicate_productive_count$Unproductive_unique / sample_replicate_productive_count$All * 100)
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151
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152 setnames(sample_replicate_productive_count, colnames(sample_productive_count))
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153
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154 counts = rbind(sample_replicate_productive_count, sample_productive_count)
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155 counts = counts[order(counts$Sample),]
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156
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157 write.table(x=counts, file="productive_counting.txt", sep=",",quote=F,row.names=F,col.names=F)
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158
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11
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159 # ---------------------- Frequency calculation for V, D and J ----------------------
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160
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161 PRODFV = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.V.Gene")])
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0
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162 Total = ddply(PRODFV, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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163 PRODFV = merge(PRODFV, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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164 PRODFV = ddply(PRODFV, c("Sample", "Top.V.Gene"), summarise, relFreq= (Length*100 / Total))
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165
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11
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166 PRODFD = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.D.Gene")])
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0
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167 Total = ddply(PRODFD, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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168 PRODFD = merge(PRODFD, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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169 PRODFD = ddply(PRODFD, c("Sample", "Top.D.Gene"), summarise, relFreq= (Length*100 / Total))
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170
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11
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171 PRODFJ = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.J.Gene")])
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0
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172 Total = ddply(PRODFJ, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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173 PRODFJ = merge(PRODFJ, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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174 PRODFJ = ddply(PRODFJ, c("Sample", "Top.J.Gene"), summarise, relFreq= (Length*100 / Total))
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175
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23
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176 # ---------------------- Setting up the gene names for the different species/loci ----------------------
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11
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177
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23
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178 Vchain = ""
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179 Dchain = ""
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180 Jchain = ""
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10
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181
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23
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182 if(species == "custom"){
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183 print("Custom genes: ")
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184 splt = unlist(strsplit(locus, ";"))
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185 print(paste("V:", splt[1]))
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186 print(paste("D:", splt[2]))
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187 print(paste("J:", splt[3]))
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188
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189 Vchain = unlist(strsplit(splt[1], ","))
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190 Vchain = data.frame(v.name = Vchain, chr.orderV = 1:length(Vchain))
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191
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192 Dchain = unlist(strsplit(splt[2], ","))
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193 if(length(Dchain) > 0){
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194 Dchain = data.frame(v.name = Dchain, chr.orderD = 1:length(Dchain))
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195 } else {
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196 Dchain = data.frame(v.name = character(0), chr.orderD = numeric(0))
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197 }
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198
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199 Jchain = unlist(strsplit(splt[3], ","))
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200 Jchain = data.frame(v.name = Jchain, chr.orderJ = 1:length(Jchain))
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10
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201
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23
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202 } else {
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203 genes = read.table("genes.txt", sep="\t", header=TRUE, fill=T, comment.char="")
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204
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205 Vchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "V",c("IMGT.GENE.DB", "chr.order")]
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206 colnames(Vchain) = c("v.name", "chr.orderV")
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207 Dchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "D",c("IMGT.GENE.DB", "chr.order")]
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208 colnames(Dchain) = c("v.name", "chr.orderD")
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209 Jchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "J",c("IMGT.GENE.DB", "chr.order")]
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210 colnames(Jchain) = c("v.name", "chr.orderJ")
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211 }
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10
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212 useD = TRUE
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22
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213 if(nrow(Dchain) == 0){
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11
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214 useD = FALSE
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215 cat("No D Genes in this species/locus")
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10
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216 }
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217
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23
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218 print(paste("useD:", useD))
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219
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22
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220 # ---------------------- merge with the frequency count ----------------------
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11
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221
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0
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222 PRODFV = merge(PRODFV, Vchain, by.x='Top.V.Gene', by.y='v.name', all.x=TRUE)
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223
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224 PRODFD = merge(PRODFD, Dchain, by.x='Top.D.Gene', by.y='v.name', all.x=TRUE)
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225
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226 PRODFJ = merge(PRODFJ, Jchain, by.x='Top.J.Gene', by.y='v.name', all.x=TRUE)
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227
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11
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228 # ---------------------- Create the V, D and J frequency plots and write the data.frame for every plot to a file ----------------------
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0
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229
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230 pV = ggplot(PRODFV)
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231 pV = pV + geom_bar( aes( x=factor(reorder(Top.V.Gene, chr.orderV)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
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232 pV = pV + xlab("Summary of V gene") + ylab("Frequency") + ggtitle("Relative frequency of V gene usage")
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6
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233 write.table(x=PRODFV, file="VFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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0
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234
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235 png("VPlot.png",width = 1280, height = 720)
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236 pV
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237 dev.off();
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238
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11
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239 if(useD){
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240 pD = ggplot(PRODFD)
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241 pD = pD + geom_bar( aes( x=factor(reorder(Top.D.Gene, chr.orderD)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
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242 pD = pD + xlab("Summary of D gene") + ylab("Frequency") + ggtitle("Relative frequency of D gene usage")
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243 write.table(x=PRODFD, file="DFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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244
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245 png("DPlot.png",width = 800, height = 600)
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246 print(pD)
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247 dev.off();
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248 }
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0
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249
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11
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250 pJ = ggplot(PRODFJ)
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251 pJ = pJ + geom_bar( aes( x=factor(reorder(Top.J.Gene, chr.orderJ)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
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252 pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage")
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253 write.table(x=PRODFJ, file="JFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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254
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255 png("JPlot.png",width = 800, height = 600)
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256 pJ
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0
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257 dev.off();
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258
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259 pJ = ggplot(PRODFJ)
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260 pJ = pJ + geom_bar( aes( x=factor(reorder(Top.J.Gene, chr.orderJ)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
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261 pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage")
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6
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262 write.table(x=PRODFJ, file="JFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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0
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263
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264 png("JPlot.png",width = 800, height = 600)
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265 pJ
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266 dev.off();
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267
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11
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268 # ---------------------- Now the frequency plots of the V, D and J families ----------------------
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269
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6
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270 VGenes = PRODF[,c("Sample", "Top.V.Gene")]
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271 VGenes$Top.V.Gene = gsub("-.*", "", VGenes$Top.V.Gene)
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272 VGenes = data.frame(data.table(VGenes)[, list(Count=.N), by=c("Sample", "Top.V.Gene")])
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273 TotalPerSample = data.frame(data.table(VGenes)[, list(total=sum(.SD$Count)), by=Sample])
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274 VGenes = merge(VGenes, TotalPerSample, by="Sample")
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275 VGenes$Frequency = VGenes$Count * 100 / VGenes$total
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276 VPlot = ggplot(VGenes)
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8
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277 VPlot = VPlot + geom_bar(aes( x = Top.V.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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11
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278 ggtitle("Distribution of V gene families") +
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279 ylab("Percentage of sequences")
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6
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280 png("VFPlot.png")
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281 VPlot
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282 dev.off();
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283 write.table(x=VGenes, file="VFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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284
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11
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285 if(useD){
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286 DGenes = PRODF[,c("Sample", "Top.D.Gene")]
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287 DGenes$Top.D.Gene = gsub("-.*", "", DGenes$Top.D.Gene)
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288 DGenes = data.frame(data.table(DGenes)[, list(Count=.N), by=c("Sample", "Top.D.Gene")])
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289 TotalPerSample = data.frame(data.table(DGenes)[, list(total=sum(.SD$Count)), by=Sample])
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|
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290 DGenes = merge(DGenes, TotalPerSample, by="Sample")
|
|
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291 DGenes$Frequency = DGenes$Count * 100 / DGenes$total
|
|
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292 DPlot = ggplot(DGenes)
|
|
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293 DPlot = DPlot + geom_bar(aes( x = Top.D.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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|
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294 ggtitle("Distribution of D gene families") +
|
|
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295 ylab("Percentage of sequences")
|
|
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296 png("DFPlot.png")
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|
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297 print(DPlot)
|
|
|
298 dev.off();
|
|
|
299 write.table(x=DGenes, file="DFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
|
|
|
300 }
|
|
6
|
301
|
|
|
302 JGenes = PRODF[,c("Sample", "Top.J.Gene")]
|
|
|
303 JGenes$Top.J.Gene = gsub("-.*", "", JGenes$Top.J.Gene)
|
|
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304 JGenes = data.frame(data.table(JGenes)[, list(Count=.N), by=c("Sample", "Top.J.Gene")])
|
|
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305 TotalPerSample = data.frame(data.table(JGenes)[, list(total=sum(.SD$Count)), by=Sample])
|
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306 JGenes = merge(JGenes, TotalPerSample, by="Sample")
|
|
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307 JGenes$Frequency = JGenes$Count * 100 / JGenes$total
|
|
|
308 JPlot = ggplot(JGenes)
|
|
8
|
309 JPlot = JPlot + geom_bar(aes( x = Top.J.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
11
|
310 ggtitle("Distribution of J gene families") +
|
|
|
311 ylab("Percentage of sequences")
|
|
6
|
312 png("JFPlot.png")
|
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313 JPlot
|
|
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314 dev.off();
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315 write.table(x=JGenes, file="JFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
|
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316
|
|
11
|
317 # ---------------------- Plotting the cdr3 length ----------------------
|
|
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318
|
|
8
|
319 CDR3Length = data.frame(data.table(PRODF)[, list(Count=.N), by=c("Sample", "CDR3.Length.DNA")])
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320 TotalPerSample = data.frame(data.table(CDR3Length)[, list(total=sum(.SD$Count)), by=Sample])
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321 CDR3Length = merge(CDR3Length, TotalPerSample, by="Sample")
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322 CDR3Length$Frequency = CDR3Length$Count * 100 / CDR3Length$total
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323 CDR3LengthPlot = ggplot(CDR3Length)
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324 CDR3LengthPlot = CDR3LengthPlot + geom_bar(aes( x = CDR3.Length.DNA, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
11
|
325 ggtitle("Length distribution of CDR3") +
|
|
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326 xlab("CDR3 Length") +
|
|
|
327 ylab("Percentage of sequences")
|
|
8
|
328 png("CDR3LengthPlot.png",width = 1280, height = 720)
|
|
|
329 CDR3LengthPlot
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330 dev.off()
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331 write.table(x=CDR3Length, file="CDR3LengthPlot.csv", sep=",",quote=F,row.names=F,col.names=T)
|
|
|
332
|
|
11
|
333 # ---------------------- Plot the heatmaps ----------------------
|
|
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334
|
|
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335
|
|
|
336 #get the reverse order for the V and D genes
|
|
0
|
337 revVchain = Vchain
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|
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338 revDchain = Dchain
|
|
|
339 revVchain$chr.orderV = rev(revVchain$chr.orderV)
|
|
|
340 revDchain$chr.orderD = rev(revDchain$chr.orderD)
|
|
|
341
|
|
11
|
342 if(useD){
|
|
|
343 plotVD <- function(dat){
|
|
|
344 if(length(dat[,1]) == 0){
|
|
|
345 return()
|
|
|
346 }
|
|
|
347 img = ggplot() +
|
|
|
348 geom_tile(data=dat, aes(x=factor(reorder(Top.D.Gene, chr.orderD)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
|
|
|
349 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
350 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
351 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
352 xlab("D genes") +
|
|
|
353 ylab("V Genes")
|
|
|
354
|
|
|
355 png(paste("HeatmapVD_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Dchain$v.name)), height=100+(15*length(Vchain$v.name)))
|
|
|
356 print(img)
|
|
|
357 dev.off()
|
|
|
358 write.table(x=acast(dat, Top.V.Gene~Top.D.Gene, value.var="Length"), file=paste("HeatmapVD_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
|
|
|
359 }
|
|
|
360
|
|
|
361 VandDCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.D.Gene", "Sample")])
|
|
|
362
|
|
|
363 VandDCount$l = log(VandDCount$Length)
|
|
|
364 maxVD = data.frame(data.table(VandDCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
365 VandDCount = merge(VandDCount, maxVD, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
366 VandDCount$relLength = VandDCount$l / VandDCount$max
|
|
|
367
|
|
|
368 cartegianProductVD = expand.grid(Top.V.Gene = Vchain$v.name, Top.D.Gene = Dchain$v.name, Sample = unique(inputdata$Sample))
|
|
|
369
|
|
|
370 completeVD = merge(VandDCount, cartegianProductVD, all.y=TRUE)
|
|
|
371 completeVD = merge(completeVD, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
|
|
|
372 completeVD = merge(completeVD, Dchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
|
|
|
373 VDList = split(completeVD, f=completeVD[,"Sample"])
|
|
|
374
|
|
|
375 lapply(VDList, FUN=plotVD)
|
|
0
|
376 }
|
|
|
377
|
|
|
378 plotVJ <- function(dat){
|
|
11
|
379 if(length(dat[,1]) == 0){
|
|
|
380 return()
|
|
|
381 }
|
|
|
382 cat(paste(unique(dat[3])[1,1]))
|
|
|
383 img = ggplot() +
|
|
|
384 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
|
|
|
385 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
386 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
387 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
388 xlab("J genes") +
|
|
|
389 ylab("V Genes")
|
|
|
390
|
|
|
391 png(paste("HeatmapVJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Vchain$v.name)))
|
|
|
392 print(img)
|
|
|
393 dev.off()
|
|
|
394 write.table(x=acast(dat, Top.V.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapVJ_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
|
|
0
|
395 }
|
|
|
396
|
|
|
397 VandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.J.Gene", "Sample")])
|
|
|
398
|
|
|
399 VandJCount$l = log(VandJCount$Length)
|
|
|
400 maxVJ = data.frame(data.table(VandJCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
401 VandJCount = merge(VandJCount, maxVJ, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
402 VandJCount$relLength = VandJCount$l / VandJCount$max
|
|
|
403
|
|
11
|
404 cartegianProductVJ = expand.grid(Top.V.Gene = Vchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(inputdata$Sample))
|
|
0
|
405
|
|
|
406 completeVJ = merge(VandJCount, cartegianProductVJ, all.y=TRUE)
|
|
|
407 completeVJ = merge(completeVJ, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
|
|
|
408 completeVJ = merge(completeVJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
|
|
|
409 VJList = split(completeVJ, f=completeVJ[,"Sample"])
|
|
|
410 lapply(VJList, FUN=plotVJ)
|
|
|
411
|
|
11
|
412 if(useD){
|
|
|
413 plotDJ <- function(dat){
|
|
|
414 if(length(dat[,1]) == 0){
|
|
|
415 return()
|
|
|
416 }
|
|
|
417 img = ggplot() +
|
|
|
418 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.D.Gene, chr.orderD)), fill=relLength)) +
|
|
|
419 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
420 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
421 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
422 xlab("J genes") +
|
|
|
423 ylab("D Genes")
|
|
|
424
|
|
|
425 png(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Dchain$v.name)))
|
|
|
426 print(img)
|
|
|
427 dev.off()
|
|
|
428 write.table(x=acast(dat, Top.D.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapDJ_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
|
|
|
429 }
|
|
|
430
|
|
|
431
|
|
|
432 DandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.D.Gene", "Top.J.Gene", "Sample")])
|
|
|
433
|
|
|
434 DandJCount$l = log(DandJCount$Length)
|
|
|
435 maxDJ = data.frame(data.table(DandJCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
436 DandJCount = merge(DandJCount, maxDJ, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
437 DandJCount$relLength = DandJCount$l / DandJCount$max
|
|
|
438
|
|
|
439 cartegianProductDJ = expand.grid(Top.D.Gene = Dchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(inputdata$Sample))
|
|
|
440
|
|
|
441 completeDJ = merge(DandJCount, cartegianProductDJ, all.y=TRUE)
|
|
|
442 completeDJ = merge(completeDJ, revDchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
|
|
|
443 completeDJ = merge(completeDJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
|
|
|
444 DJList = split(completeDJ, f=completeDJ[,"Sample"])
|
|
|
445 lapply(DJList, FUN=plotDJ)
|
|
0
|
446 }
|
|
|
447
|
|
10
|
448
|
|
11
|
449 # ---------------------- calculating the clonality score ----------------------
|
|
0
|
450
|
|
11
|
451 if("Replicate" %in% colnames(inputdata)) #can only calculate clonality score when replicate information is available
|
|
|
452 {
|
|
|
453 write.table(clonalityFrame, "clonalityComplete.csv", sep=",",quote=F,row.names=F,col.names=T)
|
|
|
454
|
|
|
455 ClonalitySampleReplicatePrint <- function(dat){
|
|
|
456 write.table(dat, paste("clonality_", unique(inputdata$Sample) , "_", unique(dat$Replicate), ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=T)
|
|
|
457 }
|
|
|
458
|
|
|
459 clonalityFrameSplit = split(clonalityFrame, f=clonalityFrame[,c("Sample", "Replicate")])
|
|
|
460 #lapply(clonalityFrameSplit, FUN=ClonalitySampleReplicatePrint)
|
|
|
461
|
|
|
462 ClonalitySamplePrint <- function(dat){
|
|
|
463 write.table(dat, paste("clonality_", unique(inputdata$Sample) , ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=T)
|
|
|
464 }
|
|
|
465
|
|
|
466 clonalityFrameSplit = split(clonalityFrame, f=clonalityFrame[,"Sample"])
|
|
|
467 #lapply(clonalityFrameSplit, FUN=ClonalitySamplePrint)
|
|
|
468
|
|
|
469 clonalFreq = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "clonaltype")])
|
|
|
470 clonalFreqCount = data.frame(data.table(clonalFreq)[, list(Count=.N), by=c("Sample", "Type")])
|
|
|
471 clonalFreqCount$realCount = clonalFreqCount$Type * clonalFreqCount$Count
|
|
|
472 clonalSum = data.frame(data.table(clonalFreqCount)[, list(Reads=sum(realCount)), by=c("Sample")])
|
|
|
473 clonalFreqCount = merge(clonalFreqCount, clonalSum, by.x="Sample", by.y="Sample")
|
|
|
474
|
|
|
475 ct = c('Type\tWeight\n2\t1\n3\t3\n4\t6\n5\t10\n6\t15')
|
|
|
476 tcct = textConnection(ct)
|
|
|
477 CT = read.table(tcct, sep="\t", header=TRUE)
|
|
|
478 close(tcct)
|
|
|
479 clonalFreqCount = merge(clonalFreqCount, CT, by.x="Type", by.y="Type", all.x=T)
|
|
|
480 clonalFreqCount$WeightedCount = clonalFreqCount$Count * clonalFreqCount$Weight
|
|
|
481
|
|
25
|
482 ReplicateReads = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "Replicate", "clonality_clonaltype")])
|
|
11
|
483 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(Reads=.N), by=c("Sample", "Replicate")])
|
|
|
484 clonalFreqCount$Reads = as.numeric(clonalFreqCount$Reads)
|
|
|
485 ReplicateReads$squared = ReplicateReads$Reads * ReplicateReads$Reads
|
|
|
486
|
|
|
487 ReplicatePrint <- function(dat){
|
|
|
488 write.table(dat[-1], paste("ReplicateReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
489 }
|
|
|
490
|
|
|
491 ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
|
|
|
492 lapply(ReplicateSplit, FUN=ReplicatePrint)
|
|
|
493
|
|
21
|
494 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(as.numeric(Reads)), ReadsSquaredSum=sum(as.numeric(squared))), by=c("Sample")])
|
|
11
|
495 clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
496
|
|
|
497
|
|
|
498 ReplicateSumPrint <- function(dat){
|
|
|
499 write.table(dat[-1], paste("ReplicateSumReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
500 }
|
|
|
501
|
|
|
502 ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
|
|
|
503 lapply(ReplicateSumSplit, FUN=ReplicateSumPrint)
|
|
|
504
|
|
|
505 clonalFreqCountSum = data.frame(data.table(clonalFreqCount)[, list(Numerator=sum(WeightedCount, na.rm=T)), by=c("Sample")])
|
|
|
506 clonalFreqCount = merge(clonalFreqCount, clonalFreqCountSum, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
507 clonalFreqCount$ReadsSum = as.numeric(clonalFreqCount$ReadsSum) #prevent integer overflow
|
|
|
508 clonalFreqCount$Denominator = (((clonalFreqCount$ReadsSum * clonalFreqCount$ReadsSum) - clonalFreqCount$ReadsSquaredSum) / 2)
|
|
|
509 clonalFreqCount$Result = (clonalFreqCount$Numerator + 1) / (clonalFreqCount$Denominator + 1)
|
|
|
510
|
|
|
511 ClonalityScorePrint <- function(dat){
|
|
|
512 write.table(dat$Result, paste("ClonalityScore_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
513 }
|
|
|
514
|
|
|
515 clonalityScore = clonalFreqCount[c("Sample", "Result")]
|
|
|
516 clonalityScore = unique(clonalityScore)
|
|
|
517
|
|
|
518 clonalityScoreSplit = split(clonalityScore, f=clonalityScore[,"Sample"])
|
|
|
519 lapply(clonalityScoreSplit, FUN=ClonalityScorePrint)
|
|
|
520
|
|
|
521 clonalityOverview = clonalFreqCount[c("Sample", "Type", "Count", "Weight", "WeightedCount")]
|
|
|
522
|
|
|
523
|
|
|
524
|
|
|
525 ClonalityOverviewPrint <- function(dat){
|
|
|
526 write.table(dat[-1], paste("ClonalityOverView_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
527 }
|
|
|
528
|
|
|
529 clonalityOverviewSplit = split(clonalityOverview, f=clonalityOverview$Sample)
|
|
|
530 lapply(clonalityOverviewSplit, FUN=ClonalityOverviewPrint)
|
|
0
|
531 }
|
|
1
|
532
|
|
11
|
533 imgtcolumns = c("X3V.REGION.trimmed.nt.nb","P3V.nt.nb", "N1.REGION.nt.nb", "P5D.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "P3D.nt.nb", "N2.REGION.nt.nb", "P5J.nt.nb", "X5J.REGION.trimmed.nt.nb", "X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb")
|
|
|
534 if(all(imgtcolumns %in% colnames(inputdata)))
|
|
1
|
535 {
|
|
19
|
536 newData = data.frame(data.table(PRODF)[,list(unique=.N,
|
|
21
|
537 VH.DEL=mean(X3V.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
538 P1=mean(P3V.nt.nb, na.rm=T),
|
|
|
539 N1=mean(N1.REGION.nt.nb, na.rm=T),
|
|
|
540 P2=mean(P5D.nt.nb, na.rm=T),
|
|
|
541 DEL.DH=mean(X5D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
542 DH.DEL=mean(X3D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
543 P3=mean(P3D.nt.nb, na.rm=T),
|
|
|
544 N2=mean(N2.REGION.nt.nb, na.rm=T),
|
|
|
545 P4=mean(P5J.nt.nb, na.rm=T),
|
|
|
546 DEL.JH=mean(X5J.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
547 Total.Del=( mean(X3V.REGION.trimmed.nt.nb, na.rm=T) +
|
|
|
548 mean(X5D.REGION.trimmed.nt.nb, na.rm=T) +
|
|
|
549 mean(X3D.REGION.trimmed.nt.nb, na.rm=T) +
|
|
|
550 mean(X5J.REGION.trimmed.nt.nb, na.rm=T)),
|
|
|
551
|
|
|
552 Total.N=( mean(N1.REGION.nt.nb, na.rm=T) +
|
|
|
553 mean(N2.REGION.nt.nb, na.rm=T)),
|
|
|
554
|
|
|
555 Total.P=( mean(P3V.nt.nb, na.rm=T) +
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556 mean(P5D.nt.nb, na.rm=T) +
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557 mean(P3D.nt.nb, na.rm=T) +
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558 mean(P5J.nt.nb, na.rm=T))),
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559 by=c("Sample")])
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20
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560 write.table(newData, "junctionAnalysisProd.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F)
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561
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21
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562 newData = data.frame(data.table(UNPROD)[,list(unique=.N,
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563 VH.DEL=mean(X3V.REGION.trimmed.nt.nb, na.rm=T),
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564 P1=mean(P3V.nt.nb, na.rm=T),
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565 N1=mean(N1.REGION.nt.nb, na.rm=T),
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566 P2=mean(P5D.nt.nb, na.rm=T),
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567 DEL.DH=mean(X5D.REGION.trimmed.nt.nb, na.rm=T),
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568 DH.DEL=mean(X3D.REGION.trimmed.nt.nb, na.rm=T),
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569 P3=mean(P3D.nt.nb, na.rm=T),
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570 N2=mean(N2.REGION.nt.nb, na.rm=T),
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571 P4=mean(P5J.nt.nb, na.rm=T),
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572 DEL.JH=mean(X5J.REGION.trimmed.nt.nb, na.rm=T),
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573 Total.Del=( mean(X3V.REGION.trimmed.nt.nb, na.rm=T) +
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574 mean(X5D.REGION.trimmed.nt.nb, na.rm=T) +
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575 mean(X3D.REGION.trimmed.nt.nb, na.rm=T) +
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576 mean(X5J.REGION.trimmed.nt.nb, na.rm=T)),
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577
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578 Total.N=( mean(N1.REGION.nt.nb, na.rm=T) +
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579 mean(N2.REGION.nt.nb, na.rm=T)),
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580
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581 Total.P=( mean(P3V.nt.nb, na.rm=T) +
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582 mean(P5D.nt.nb, na.rm=T) +
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583 mean(P3D.nt.nb, na.rm=T) +
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584 mean(P5J.nt.nb, na.rm=T))),
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585 by=c("Sample")])
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|
20
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586 write.table(newData, "junctionAnalysisUnProd.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F)
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5
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587 }
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|
24
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588
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589 # ---------------------- AA composition in CDR3 ----------------------
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590
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591 AACDR3 = PRODF[,c("Sample", "CDR3.Seq")]
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592
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593 TotalPerSample = data.frame(data.table(AACDR3)[, list(total=sum(nchar(as.character(.SD$CDR3.Seq)))), by=Sample])
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594
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595 AAfreq = list()
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596
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597 for(i in 1:nrow(TotalPerSample)){
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598 sample = TotalPerSample$Sample[i]
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599 AAfreq[[i]] = data.frame(table(unlist(strsplit(as.character(AACDR3[AACDR3$Sample == sample,c("CDR3.Seq")]), ""))))
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600 AAfreq[[i]]$Sample = sample
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601 }
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602
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603 AAfreq = ldply(AAfreq, data.frame)
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|
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604 AAfreq = merge(AAfreq, TotalPerSample, by="Sample", all.x = T)
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605 AAfreq$freq_perc = as.numeric(AAfreq$Freq / AAfreq$total * 100)
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|
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606
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|
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607
|
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|
608 AAorder = read.table(sep="\t", header=TRUE, text="order.aa\tAA\n1\tR\n2\tK\n3\tN\n4\tD\n5\tQ\n6\tE\n7\tH\n8\tP\n9\tY\n10\tW\n11\tS\n12\tT\n13\tG\n14\tA\n15\tM\n16\tC\n17\tF\n18\tL\n19\tV\n20\tI")
|
|
|
609 AAfreq = merge(AAfreq, AAorder, by.x='Var1', by.y='AA', all.x=TRUE)
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|
|
610
|
|
|
611 AAfreq = AAfreq[!is.na(AAfreq$order.aa),]
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|
|
612
|
|
|
613 AAfreqplot = ggplot(AAfreq)
|
|
|
614 AAfreqplot = AAfreqplot + geom_bar(aes( x=factor(reorder(Var1, order.aa)), y = freq_perc, fill = Sample), stat='identity', position='dodge' )
|
|
|
615 AAfreqplot = AAfreqplot + annotate("rect", xmin = 0.5, xmax = 2.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2)
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616 AAfreqplot = AAfreqplot + annotate("rect", xmin = 3.5, xmax = 4.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2)
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617 AAfreqplot = AAfreqplot + annotate("rect", xmin = 5.5, xmax = 6.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2)
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|
|
618 AAfreqplot = AAfreqplot + annotate("rect", xmin = 6.5, xmax = 7.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2)
|
|
|
619 AAfreqplot = AAfreqplot + ggtitle("Amino Acid Composition in the CDR3") + xlab("Amino Acid, from Hydrophilic (left) to Hydrophobic (right)") + ylab("Percentage")
|
|
|
620
|
|
|
621 png("AAComposition.png",width = 1280, height = 720)
|
|
|
622 AAfreqplot
|
|
|
623 dev.off()
|
|
|
624 write.table(AAfreq, "AAComposition.csv" , sep=",",quote=F,na="-",row.names=F,col.names=T)
|
|
|
625
|
|
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626
|
