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26
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1 # ---------------------- load/install packages ----------------------
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2
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3 if (!("gridExtra" %in% rownames(installed.packages()))) {
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4 install.packages("gridExtra", repos="http://cran.xl-mirror.nl/")
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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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8 install.packages("ggplot2", repos="http://cran.xl-mirror.nl/")
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9 }
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10 library(ggplot2)
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11 if (!("plyr" %in% rownames(installed.packages()))) {
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12 install.packages("plyr", repos="http://cran.xl-mirror.nl/")
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13 }
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14 library(plyr)
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15
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16 if (!("data.table" %in% rownames(installed.packages()))) {
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17 install.packages("data.table", repos="http://cran.xl-mirror.nl/")
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18 }
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19 library(data.table)
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20
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21 if (!("reshape2" %in% rownames(installed.packages()))) {
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22 install.packages("reshape2", repos="http://cran.xl-mirror.nl/")
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23 }
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24 library(reshape2)
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25
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26 if (!("lymphclon" %in% rownames(installed.packages()))) {
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27 install.packages("lymphclon", repos="http://cran.xl-mirror.nl/")
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28 }
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29 library(lymphclon)
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30
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31 # ---------------------- parameters ----------------------
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32
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33 args <- commandArgs(trailingOnly = TRUE)
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34
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35 infile = args[1] #path to input file
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36 outfile = args[2] #path to output file
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37 outdir = args[3] #path to output folder (html/images/data)
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38 clonaltype = args[4] #clonaltype definition, or 'none' for no unique filtering
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39 ct = unlist(strsplit(clonaltype, ","))
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40 species = args[5] #human or mouse
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41 locus = args[6] # IGH, IGK, IGL, TRB, TRA, TRG or TRD
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42 filterproductive = ifelse(args[7] == "yes", T, F) #should unproductive sequences be filtered out? (yes/no)
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43 clonality_method = args[8]
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44
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45 # ---------------------- Data preperation ----------------------
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46
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47 inputdata = read.table(infile, sep="\t", header=TRUE, fill=T, comment.char="")
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48
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49 setwd(outdir)
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50
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51 # remove weird rows
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52 inputdata = inputdata[inputdata$Sample != "",]
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53
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54 #remove the allele from the V,D and J genes
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55 inputdata$Top.V.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.V.Gene)
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56 inputdata$Top.D.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.D.Gene)
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57 inputdata$Top.J.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.J.Gene)
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58
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59 inputdata$clonaltype = 1:nrow(inputdata)
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60
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61 PRODF = inputdata
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62 UNPROD = inputdata
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63 if(filterproductive){
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64 if("Functionality" %in% colnames(inputdata)) { # "Functionality" is an IMGT column
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65 PRODF = inputdata[inputdata$Functionality == "productive" | inputdata$Functionality == "productive (see comment)", ]
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66 UNPROD = inputdata[!(inputdata$Functionality == "productive" | inputdata$Functionality == "productive (see comment)"), ]
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67 } else {
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68 PRODF = inputdata[inputdata$VDJ.Frame != "In-frame with stop codon" & inputdata$VDJ.Frame != "Out-of-frame" & inputdata$CDR3.Found.How != "NOT_FOUND" , ]
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69 UNPROD = inputdata[!(inputdata$VDJ.Frame != "In-frame with stop codon" & inputdata$VDJ.Frame != "Out-of-frame" & inputdata$CDR3.Found.How != "NOT_FOUND" ), ]
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70 }
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71 }
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72
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73 clonalityFrame = PRODF
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74
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75 #remove duplicates based on the clonaltype
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76 if(clonaltype != "none"){
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77 clonaltype = paste(clonaltype, ",Sample", sep="") #add sample column to clonaltype, unique within samples
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78 PRODF$clonaltype = do.call(paste, c(PRODF[unlist(strsplit(clonaltype, ","))], sep = ":"))
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79 PRODF = PRODF[!duplicated(PRODF$clonaltype), ]
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80
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81 UNPROD$clonaltype = do.call(paste, c(UNPROD[unlist(strsplit(clonaltype, ","))], sep = ":"))
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82 UNPROD = UNPROD[!duplicated(UNPROD$clonaltype), ]
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83
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84 #again for clonalityFrame but with sample+replicate
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85 clonalityFrame$clonaltype = do.call(paste, c(clonalityFrame[unlist(strsplit(clonaltype, ","))], sep = ":"))
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86 clonalityFrame$clonality_clonaltype = do.call(paste, c(clonalityFrame[unlist(strsplit(paste(clonaltype, ",Replicate", sep=""), ","))], sep = ":"))
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87 clonalityFrame = clonalityFrame[!duplicated(clonalityFrame$clonality_clonaltype), ]
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88 }
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89
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90 PRODF$freq = 1
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91
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92 if(any(grepl(pattern="_", x=PRODF$ID))){ #the frequency can be stored in the ID with the pattern ".*_freq_.*"
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93 PRODF$freq = gsub("^[0-9]+_", "", PRODF$ID)
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94 PRODF$freq = gsub("_.*", "", PRODF$freq)
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95 PRODF$freq = as.numeric(PRODF$freq)
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96 if(any(is.na(PRODF$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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97 PRODF$freq = 1
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98 }
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99 }
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100
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101
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102
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103 #write the complete dataset that is left over, will be the input if 'none' for clonaltype and 'no' for filterproductive
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104 write.table(PRODF, "allUnique.txt", sep=",",quote=F,row.names=F,col.names=T)
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105 write.table(PRODF, "allUnique.csv", sep="\t",quote=F,row.names=F,col.names=T)
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106 write.table(UNPROD, "allUnproductive.csv", sep=",",quote=F,row.names=F,col.names=T)
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107
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108 #write the samples to a file
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109 sampleFile <- file("samples.txt")
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110 un = unique(inputdata$Sample)
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111 un = paste(un, sep="\n")
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112 writeLines(un, sampleFile)
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113 close(sampleFile)
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114
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115 # ---------------------- Counting the productive/unproductive and unique sequences ----------------------
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116
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117 if(!("Functionality" %in% inputdata)){ #add a functionality column to the igblast data
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118 inputdata$Functionality = "unproductive"
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119 search = (inputdata$VDJ.Frame != "In-frame with stop codon" & inputdata$VDJ.Frame != "Out-of-frame" & inputdata$CDR3.Found.How != "NOT_FOUND")
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120 if(sum(search) > 0){
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121 inputdata[search,]$Functionality = "productive"
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122 }
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123 }
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124
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125 inputdata.dt = data.table(inputdata) #for speed
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126
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127 if(clonaltype == "none"){
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128 ct = c("clonaltype")
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129 }
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130
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131 inputdata.dt$samples_replicates = paste(inputdata.dt$Sample, inputdata.dt$Replicate, sep="_")
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132 samples_replicates = c(unique(inputdata.dt$samples_replicates), unique(as.character(inputdata.dt$Sample)))
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133 frequency_table = data.frame(ID = samples_replicates[order(samples_replicates)])
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134
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135
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136 sample_productive_count = inputdata.dt[, list(All=.N,
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137 Productive = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",]),
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138 perc_prod = 1,
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139 Productive_unique = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",list(count=.N),by=ct]),
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140 perc_prod_un = 1,
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141 Unproductive= nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",]),
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142 perc_unprod = 1,
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143 Unproductive_unique =nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",list(count=.N),by=ct]),
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144 perc_unprod_un = 1),
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145 by=c("Sample")]
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146
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147 sample_productive_count$perc_prod = round(sample_productive_count$Productive / sample_productive_count$All * 100)
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148 sample_productive_count$perc_prod_un = round(sample_productive_count$Productive_unique / sample_productive_count$All * 100)
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149
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150 sample_productive_count$perc_unprod = round(sample_productive_count$Unproductive / sample_productive_count$All * 100)
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151 sample_productive_count$perc_unprod_un = round(sample_productive_count$Unproductive_unique / sample_productive_count$All * 100)
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152
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153
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154 sample_replicate_productive_count = inputdata.dt[, list(All=.N,
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155 Productive = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",]),
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156 perc_prod = 1,
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157 Productive_unique = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",list(count=.N),by=ct]),
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158 perc_prod_un = 1,
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159 Unproductive= nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",]),
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160 perc_unprod = 1,
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161 Unproductive_unique =nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",list(count=.N),by=ct]),
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162 perc_unprod_un = 1),
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163 by=c("samples_replicates")]
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164
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165 sample_replicate_productive_count$perc_prod = round(sample_replicate_productive_count$Productive / sample_replicate_productive_count$All * 100)
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166 sample_replicate_productive_count$perc_prod_un = round(sample_replicate_productive_count$Productive_unique / sample_replicate_productive_count$All * 100)
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167
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168 sample_replicate_productive_count$perc_unprod = round(sample_replicate_productive_count$Unproductive / sample_replicate_productive_count$All * 100)
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169 sample_replicate_productive_count$perc_unprod_un = round(sample_replicate_productive_count$Unproductive_unique / sample_replicate_productive_count$All * 100)
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170
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171 setnames(sample_replicate_productive_count, colnames(sample_productive_count))
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172
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173 counts = rbind(sample_replicate_productive_count, sample_productive_count)
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174 counts = counts[order(counts$Sample),]
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175
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176 write.table(x=counts, file="productive_counting.txt", sep=",",quote=F,row.names=F,col.names=F)
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177
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178 # ---------------------- Frequency calculation for V, D and J ----------------------
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179
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180 PRODFV = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.V.Gene")])
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181 Total = ddply(PRODFV, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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182 PRODFV = merge(PRODFV, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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183 PRODFV = ddply(PRODFV, c("Sample", "Top.V.Gene"), summarise, relFreq= (Length*100 / Total))
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184
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185 PRODFD = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.D.Gene")])
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186 Total = ddply(PRODFD, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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187 PRODFD = merge(PRODFD, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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188 PRODFD = ddply(PRODFD, c("Sample", "Top.D.Gene"), summarise, relFreq= (Length*100 / Total))
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189
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190 PRODFJ = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.J.Gene")])
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191 Total = ddply(PRODFJ, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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192 PRODFJ = merge(PRODFJ, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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193 PRODFJ = ddply(PRODFJ, c("Sample", "Top.J.Gene"), summarise, relFreq= (Length*100 / Total))
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194
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195 # ---------------------- Setting up the gene names for the different species/loci ----------------------
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196
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197 Vchain = ""
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198 Dchain = ""
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199 Jchain = ""
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200
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201 if(species == "custom"){
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202 print("Custom genes: ")
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203 splt = unlist(strsplit(locus, ";"))
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204 print(paste("V:", splt[1]))
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205 print(paste("D:", splt[2]))
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206 print(paste("J:", splt[3]))
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207
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208 Vchain = unlist(strsplit(splt[1], ","))
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209 Vchain = data.frame(v.name = Vchain, chr.orderV = 1:length(Vchain))
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210
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211 Dchain = unlist(strsplit(splt[2], ","))
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212 if(length(Dchain) > 0){
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213 Dchain = data.frame(v.name = Dchain, chr.orderD = 1:length(Dchain))
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214 } else {
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215 Dchain = data.frame(v.name = character(0), chr.orderD = numeric(0))
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216 }
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217
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218 Jchain = unlist(strsplit(splt[3], ","))
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219 Jchain = data.frame(v.name = Jchain, chr.orderJ = 1:length(Jchain))
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220
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221 } else {
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222 genes = read.table("genes.txt", sep="\t", header=TRUE, fill=T, comment.char="")
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223
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224 Vchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "V",c("IMGT.GENE.DB", "chr.order")]
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225 colnames(Vchain) = c("v.name", "chr.orderV")
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226 Dchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "D",c("IMGT.GENE.DB", "chr.order")]
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227 colnames(Dchain) = c("v.name", "chr.orderD")
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228 Jchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "J",c("IMGT.GENE.DB", "chr.order")]
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229 colnames(Jchain) = c("v.name", "chr.orderJ")
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230 }
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231 useD = TRUE
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232 if(nrow(Dchain) == 0){
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233 useD = FALSE
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234 cat("No D Genes in this species/locus")
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235 }
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236 print(paste("useD:", useD))
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237
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238 # ---------------------- merge with the frequency count ----------------------
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239
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240 PRODFV = merge(PRODFV, Vchain, by.x='Top.V.Gene', by.y='v.name', all.x=TRUE)
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241
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242 PRODFD = merge(PRODFD, Dchain, by.x='Top.D.Gene', by.y='v.name', all.x=TRUE)
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243
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244 PRODFJ = merge(PRODFJ, Jchain, by.x='Top.J.Gene', by.y='v.name', all.x=TRUE)
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245
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246 # ---------------------- Create the V, D and J frequency plots and write the data.frame for every plot to a file ----------------------
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247
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248 pV = ggplot(PRODFV)
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249 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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250 pV = pV + xlab("Summary of V gene") + ylab("Frequency") + ggtitle("Relative frequency of V gene usage")
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251 write.table(x=PRODFV, file="VFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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252
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253 png("VPlot.png",width = 1280, height = 720)
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254 pV
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255 dev.off();
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256
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257 if(useD){
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258 pD = ggplot(PRODFD)
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259 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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260 pD = pD + xlab("Summary of D gene") + ylab("Frequency") + ggtitle("Relative frequency of D gene usage")
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261 write.table(x=PRODFD, file="DFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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262
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263 png("DPlot.png",width = 800, height = 600)
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264 print(pD)
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265 dev.off();
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266 }
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267
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268 pJ = ggplot(PRODFJ)
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269 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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270 pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage")
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271 write.table(x=PRODFJ, file="JFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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272
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273 png("JPlot.png",width = 800, height = 600)
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274 pJ
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275 dev.off();
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276
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277 pJ = ggplot(PRODFJ)
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278 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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279 pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage")
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280 write.table(x=PRODFJ, file="JFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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281
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282 png("JPlot.png",width = 800, height = 600)
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283 pJ
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284 dev.off();
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285
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286 # ---------------------- Now the frequency plots of the V, D and J families ----------------------
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287
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288 VGenes = PRODF[,c("Sample", "Top.V.Gene")]
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289 VGenes$Top.V.Gene = gsub("-.*", "", VGenes$Top.V.Gene)
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290 VGenes = data.frame(data.table(VGenes)[, list(Count=.N), by=c("Sample", "Top.V.Gene")])
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291 TotalPerSample = data.frame(data.table(VGenes)[, list(total=sum(.SD$Count)), by=Sample])
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292 VGenes = merge(VGenes, TotalPerSample, by="Sample")
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293 VGenes$Frequency = VGenes$Count * 100 / VGenes$total
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294 VPlot = ggplot(VGenes)
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295 VPlot = VPlot + geom_bar(aes( x = Top.V.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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296 ggtitle("Distribution of V gene families") +
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297 ylab("Percentage of sequences")
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298 png("VFPlot.png")
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299 VPlot
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300 dev.off();
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|
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301 write.table(x=VGenes, file="VFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
|
|
|
302
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|
|
303 if(useD){
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|
|
304 DGenes = PRODF[,c("Sample", "Top.D.Gene")]
|
|
|
305 DGenes$Top.D.Gene = gsub("-.*", "", DGenes$Top.D.Gene)
|
|
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306 DGenes = data.frame(data.table(DGenes)[, list(Count=.N), by=c("Sample", "Top.D.Gene")])
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307 TotalPerSample = data.frame(data.table(DGenes)[, list(total=sum(.SD$Count)), by=Sample])
|
|
|
308 DGenes = merge(DGenes, TotalPerSample, by="Sample")
|
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309 DGenes$Frequency = DGenes$Count * 100 / DGenes$total
|
|
|
310 DPlot = ggplot(DGenes)
|
|
|
311 DPlot = DPlot + geom_bar(aes( x = Top.D.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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312 ggtitle("Distribution of D gene families") +
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|
|
313 ylab("Percentage of sequences")
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|
314 png("DFPlot.png")
|
|
|
315 print(DPlot)
|
|
|
316 dev.off();
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|
317 write.table(x=DGenes, file="DFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
|
|
|
318 }
|
|
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319
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|
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320 JGenes = PRODF[,c("Sample", "Top.J.Gene")]
|
|
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321 JGenes$Top.J.Gene = gsub("-.*", "", JGenes$Top.J.Gene)
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322 JGenes = data.frame(data.table(JGenes)[, list(Count=.N), by=c("Sample", "Top.J.Gene")])
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323 TotalPerSample = data.frame(data.table(JGenes)[, list(total=sum(.SD$Count)), by=Sample])
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324 JGenes = merge(JGenes, TotalPerSample, by="Sample")
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325 JGenes$Frequency = JGenes$Count * 100 / JGenes$total
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326 JPlot = ggplot(JGenes)
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327 JPlot = JPlot + geom_bar(aes( x = Top.J.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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328 ggtitle("Distribution of J gene families") +
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329 ylab("Percentage of sequences")
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330 png("JFPlot.png")
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331 JPlot
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332 dev.off();
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333 write.table(x=JGenes, file="JFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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334
|
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|
335 # ---------------------- Plotting the cdr3 length ----------------------
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336
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337 CDR3Length = data.frame(data.table(PRODF)[, list(Count=.N), by=c("Sample", "CDR3.Length.DNA")])
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338 TotalPerSample = data.frame(data.table(CDR3Length)[, list(total=sum(.SD$Count)), by=Sample])
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339 CDR3Length = merge(CDR3Length, TotalPerSample, by="Sample")
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340 CDR3Length$Frequency = CDR3Length$Count * 100 / CDR3Length$total
|
|
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341 CDR3LengthPlot = ggplot(CDR3Length)
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342 CDR3LengthPlot = CDR3LengthPlot + geom_bar(aes( x = CDR3.Length.DNA, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
343 ggtitle("Length distribution of CDR3") +
|
|
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344 xlab("CDR3 Length") +
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|
|
345 ylab("Percentage of sequences")
|
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|
346 png("CDR3LengthPlot.png",width = 1280, height = 720)
|
|
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347 CDR3LengthPlot
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|
348 dev.off()
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|
349 write.table(x=CDR3Length, file="CDR3LengthPlot.csv", sep=",",quote=F,row.names=F,col.names=T)
|
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350
|
|
|
351 # ---------------------- Plot the heatmaps ----------------------
|
|
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352
|
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353
|
|
|
354 #get the reverse order for the V and D genes
|
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|
355 revVchain = Vchain
|
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|
356 revDchain = Dchain
|
|
|
357 revVchain$chr.orderV = rev(revVchain$chr.orderV)
|
|
|
358 revDchain$chr.orderD = rev(revDchain$chr.orderD)
|
|
|
359
|
|
|
360 if(useD){
|
|
|
361 plotVD <- function(dat){
|
|
|
362 if(length(dat[,1]) == 0){
|
|
|
363 return()
|
|
|
364 }
|
|
|
365 img = ggplot() +
|
|
|
366 geom_tile(data=dat, aes(x=factor(reorder(Top.D.Gene, chr.orderD)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
|
|
|
367 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
368 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
369 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
370 xlab("D genes") +
|
|
|
371 ylab("V Genes")
|
|
|
372
|
|
|
373 png(paste("HeatmapVD_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Dchain$v.name)), height=100+(15*length(Vchain$v.name)))
|
|
|
374 print(img)
|
|
|
375 dev.off()
|
|
|
376 write.table(x=acast(dat, Top.V.Gene~Top.D.Gene, value.var="Length"), file=paste("HeatmapVD_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
|
|
|
377 }
|
|
|
378
|
|
|
379 VandDCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.D.Gene", "Sample")])
|
|
|
380
|
|
|
381 VandDCount$l = log(VandDCount$Length)
|
|
|
382 maxVD = data.frame(data.table(VandDCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
383 VandDCount = merge(VandDCount, maxVD, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
384 VandDCount$relLength = VandDCount$l / VandDCount$max
|
|
|
385
|
|
|
386 cartegianProductVD = expand.grid(Top.V.Gene = Vchain$v.name, Top.D.Gene = Dchain$v.name, Sample = unique(inputdata$Sample))
|
|
|
387
|
|
|
388 completeVD = merge(VandDCount, cartegianProductVD, all.y=TRUE)
|
|
|
389 completeVD = merge(completeVD, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
|
|
|
390 completeVD = merge(completeVD, Dchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
|
|
|
391 VDList = split(completeVD, f=completeVD[,"Sample"])
|
|
|
392
|
|
|
393 lapply(VDList, FUN=plotVD)
|
|
|
394 }
|
|
|
395
|
|
|
396 plotVJ <- function(dat){
|
|
|
397 if(length(dat[,1]) == 0){
|
|
|
398 return()
|
|
|
399 }
|
|
|
400 cat(paste(unique(dat[3])[1,1]))
|
|
|
401 img = ggplot() +
|
|
|
402 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
|
|
|
403 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
404 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
405 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
406 xlab("J genes") +
|
|
|
407 ylab("V Genes")
|
|
|
408
|
|
|
409 png(paste("HeatmapVJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Vchain$v.name)))
|
|
|
410 print(img)
|
|
|
411 dev.off()
|
|
|
412 write.table(x=acast(dat, Top.V.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapVJ_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
|
|
|
413 }
|
|
|
414
|
|
|
415 VandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.J.Gene", "Sample")])
|
|
|
416
|
|
|
417 VandJCount$l = log(VandJCount$Length)
|
|
|
418 maxVJ = data.frame(data.table(VandJCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
419 VandJCount = merge(VandJCount, maxVJ, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
420 VandJCount$relLength = VandJCount$l / VandJCount$max
|
|
|
421
|
|
|
422 cartegianProductVJ = expand.grid(Top.V.Gene = Vchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(inputdata$Sample))
|
|
|
423
|
|
|
424 completeVJ = merge(VandJCount, cartegianProductVJ, all.y=TRUE)
|
|
|
425 completeVJ = merge(completeVJ, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
|
|
|
426 completeVJ = merge(completeVJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
|
|
|
427 VJList = split(completeVJ, f=completeVJ[,"Sample"])
|
|
|
428 lapply(VJList, FUN=plotVJ)
|
|
|
429
|
|
|
430 if(useD){
|
|
|
431 plotDJ <- function(dat){
|
|
|
432 if(length(dat[,1]) == 0){
|
|
|
433 return()
|
|
|
434 }
|
|
|
435 img = ggplot() +
|
|
|
436 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.D.Gene, chr.orderD)), fill=relLength)) +
|
|
|
437 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
438 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
439 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
440 xlab("J genes") +
|
|
|
441 ylab("D Genes")
|
|
|
442
|
|
|
443 png(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Dchain$v.name)))
|
|
|
444 print(img)
|
|
|
445 dev.off()
|
|
|
446 write.table(x=acast(dat, Top.D.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapDJ_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
|
|
|
447 }
|
|
|
448
|
|
|
449
|
|
|
450 DandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.D.Gene", "Top.J.Gene", "Sample")])
|
|
|
451
|
|
|
452 DandJCount$l = log(DandJCount$Length)
|
|
|
453 maxDJ = data.frame(data.table(DandJCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
454 DandJCount = merge(DandJCount, maxDJ, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
455 DandJCount$relLength = DandJCount$l / DandJCount$max
|
|
|
456
|
|
|
457 cartegianProductDJ = expand.grid(Top.D.Gene = Dchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(inputdata$Sample))
|
|
|
458
|
|
|
459 completeDJ = merge(DandJCount, cartegianProductDJ, all.y=TRUE)
|
|
|
460 completeDJ = merge(completeDJ, revDchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
|
|
|
461 completeDJ = merge(completeDJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
|
|
|
462 DJList = split(completeDJ, f=completeDJ[,"Sample"])
|
|
|
463 lapply(DJList, FUN=plotDJ)
|
|
|
464 }
|
|
|
465
|
|
|
466
|
|
|
467 # ---------------------- calculating the clonality score ----------------------
|
|
|
468
|
|
|
469 if("Replicate" %in% colnames(inputdata)) #can only calculate clonality score when replicate information is available
|
|
|
470 {
|
|
|
471 if(clonality_method == "boyd"){
|
|
|
472 samples = split(clonalityFrame, clonalityFrame$Sample, drop=T)
|
|
|
473
|
|
|
474 for (sample in samples){
|
|
|
475 res = data.frame(paste=character(0))
|
|
|
476 sample_id = unique(sample$Sample)[[1]]
|
|
|
477 for(replicate in unique(sample$Replicate)){
|
|
|
478 tmp = sample[sample$Replicate == replicate,]
|
|
|
479 clone_table = data.frame(table(tmp$clonaltype))
|
|
|
480 clone_col_name = paste("V", replicate, sep="")
|
|
|
481 colnames(clone_table) = c("paste", clone_col_name)
|
|
|
482 res = merge(res, clone_table, by="paste", all=T)
|
|
|
483 }
|
|
|
484
|
|
|
485 res[is.na(res)] = 0
|
|
|
486 infer.result = infer.clonality(as.matrix(res[,2:ncol(res)]))
|
|
|
487
|
|
|
488 write.table(data.table(infer.result[[12]]), file=paste("lymphclon_clonality_", sample_id, ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=F)
|
|
|
489
|
|
|
490 res$type = rowSums(res[,2:ncol(res)])
|
|
|
491
|
|
|
492 coincidence.table = data.frame(table(res$type))
|
|
|
493 colnames(coincidence.table) = c("Coincidence Type", "Raw Coincidence Freq")
|
|
|
494 write.table(coincidence.table, file=paste("lymphclon_coincidences_", sample_id, ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=T)
|
|
|
495 }
|
|
|
496 } else {
|
|
|
497 write.table(clonalityFrame, "clonalityComplete.csv", sep=",",quote=F,row.names=F,col.names=T)
|
|
|
498
|
|
|
499 clonalFreq = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "clonaltype")])
|
|
|
500 clonalFreqCount = data.frame(data.table(clonalFreq)[, list(Count=.N), by=c("Sample", "Type")])
|
|
|
501 clonalFreqCount$realCount = clonalFreqCount$Type * clonalFreqCount$Count
|
|
|
502 clonalSum = data.frame(data.table(clonalFreqCount)[, list(Reads=sum(realCount)), by=c("Sample")])
|
|
|
503 clonalFreqCount = merge(clonalFreqCount, clonalSum, by.x="Sample", by.y="Sample")
|
|
|
504
|
|
|
505 ct = c('Type\tWeight\n2\t1\n3\t3\n4\t6\n5\t10\n6\t15')
|
|
|
506 tcct = textConnection(ct)
|
|
|
507 CT = read.table(tcct, sep="\t", header=TRUE)
|
|
|
508 close(tcct)
|
|
|
509 clonalFreqCount = merge(clonalFreqCount, CT, by.x="Type", by.y="Type", all.x=T)
|
|
|
510 clonalFreqCount$WeightedCount = clonalFreqCount$Count * clonalFreqCount$Weight
|
|
|
511
|
|
|
512 ReplicateReads = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "Replicate", "clonaltype")])
|
|
|
513 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(Reads=.N), by=c("Sample", "Replicate")])
|
|
|
514 clonalFreqCount$Reads = as.numeric(clonalFreqCount$Reads)
|
|
|
515 ReplicateReads$squared = ReplicateReads$Reads * ReplicateReads$Reads
|
|
|
516
|
|
|
517 ReplicatePrint <- function(dat){
|
|
|
518 write.table(dat[-1], paste("ReplicateReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
519 }
|
|
|
520
|
|
|
521 ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
|
|
|
522 lapply(ReplicateSplit, FUN=ReplicatePrint)
|
|
|
523
|
|
|
524 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(as.numeric(Reads)), ReadsSquaredSum=sum(as.numeric(squared))), by=c("Sample")])
|
|
|
525 clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
526
|
|
|
527 ReplicateSumPrint <- function(dat){
|
|
|
528 write.table(dat[-1], paste("ReplicateSumReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
529 }
|
|
|
530
|
|
|
531 ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
|
|
|
532 lapply(ReplicateSumSplit, FUN=ReplicateSumPrint)
|
|
|
533
|
|
|
534 clonalFreqCountSum = data.frame(data.table(clonalFreqCount)[, list(Numerator=sum(WeightedCount, na.rm=T)), by=c("Sample")])
|
|
|
535 clonalFreqCount = merge(clonalFreqCount, clonalFreqCountSum, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
536 clonalFreqCount$ReadsSum = as.numeric(clonalFreqCount$ReadsSum) #prevent integer overflow
|
|
|
537 clonalFreqCount$Denominator = (((clonalFreqCount$ReadsSum * clonalFreqCount$ReadsSum) - clonalFreqCount$ReadsSquaredSum) / 2)
|
|
|
538 clonalFreqCount$Result = (clonalFreqCount$Numerator + 1) / (clonalFreqCount$Denominator + 1)
|
|
|
539
|
|
|
540 ClonalityScorePrint <- function(dat){
|
|
|
541 write.table(dat$Result, paste("ClonalityScore_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
542 }
|
|
|
543
|
|
|
544 clonalityScore = clonalFreqCount[c("Sample", "Result")]
|
|
|
545 clonalityScore = unique(clonalityScore)
|
|
|
546
|
|
|
547 clonalityScoreSplit = split(clonalityScore, f=clonalityScore[,"Sample"])
|
|
|
548 lapply(clonalityScoreSplit, FUN=ClonalityScorePrint)
|
|
|
549
|
|
|
550 clonalityOverview = clonalFreqCount[c("Sample", "Type", "Count", "Weight", "WeightedCount")]
|
|
|
551
|
|
|
552
|
|
|
553
|
|
|
554 ClonalityOverviewPrint <- function(dat){
|
|
|
555 write.table(dat[-1], paste("ClonalityOverView_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
556 }
|
|
|
557
|
|
|
558 clonalityOverviewSplit = split(clonalityOverview, f=clonalityOverview$Sample)
|
|
|
559 lapply(clonalityOverviewSplit, FUN=ClonalityOverviewPrint)
|
|
|
560 }
|
|
|
561 }
|
|
|
562
|
|
|
563 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")
|
|
|
564 if(all(imgtcolumns %in% colnames(inputdata)))
|
|
|
565 {
|
|
|
566 print("found IMGT columns, running junction analysis")
|
|
|
567 newData = data.frame(data.table(PRODF)[,list(unique=.N,
|
|
|
568 VH.DEL=mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
569 P1=mean(.SD$P3V.nt.nb, na.rm=T),
|
|
|
570 N1=mean(.SD$N1.REGION.nt.nb, na.rm=T),
|
|
|
571 P2=mean(.SD$P5D.nt.nb, na.rm=T),
|
|
|
572 DEL.DH=mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T),
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573 DH.DEL=mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T),
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574 P3=mean(.SD$P3D.nt.nb, na.rm=T),
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575 N2=mean(.SD$N2.REGION.nt.nb, na.rm=T),
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576 P4=mean(.SD$P5J.nt.nb, na.rm=T),
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577 DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
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578 Total.Del=( mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T) +
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579 mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T) +
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580 mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T) +
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581 mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T)),
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582
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583 Total.N=( mean(.SD$N1.REGION.nt.nb, na.rm=T) +
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584 mean(.SD$N2.REGION.nt.nb, na.rm=T)),
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585
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586 Total.P=( mean(.SD$P3V.nt.nb, na.rm=T) +
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587 mean(.SD$P5D.nt.nb, na.rm=T) +
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588 mean(.SD$P3D.nt.nb, na.rm=T) +
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589 mean(.SD$P5J.nt.nb, na.rm=T))),
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590 by=c("Sample")])
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591 print(newData)
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592 newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1)
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593 write.table(newData, "junctionAnalysisProd.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F)
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594
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595 newData = data.frame(data.table(UNPROD)[,list(unique=.N,
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596 VH.DEL=mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
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597 P1=mean(.SD$P3V.nt.nb, na.rm=T),
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598 N1=mean(.SD$N1.REGION.nt.nb, na.rm=T),
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599 P2=mean(.SD$P5D.nt.nb, na.rm=T),
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600 DEL.DH=mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T),
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601 DH.DEL=mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T),
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602 P3=mean(.SD$P3D.nt.nb, na.rm=T),
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603 N2=mean(.SD$N2.REGION.nt.nb, na.rm=T),
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604 P4=mean(.SD$P5J.nt.nb, na.rm=T),
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605 DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
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|
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606 Total.Del=(mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T) +
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607 mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T) +
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608 mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T) +
|
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609 mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T)),
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610 Total.N=( mean(.SD$N1.REGION.nt.nb, na.rm=T) +
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|
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611 mean(.SD$N2.REGION.nt.nb, na.rm=T)),
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|
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612 Total.P=( mean(.SD$P3V.nt.nb, na.rm=T) +
|
|
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613 mean(.SD$P5D.nt.nb, na.rm=T) +
|
|
|
614 mean(.SD$P3D.nt.nb, na.rm=T) +
|
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615 mean(.SD$P5J.nt.nb, na.rm=T))),
|
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|
616 by=c("Sample")])
|
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|
617 newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1)
|
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|
618 write.table(newData, "junctionAnalysisUnProd.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F)
|
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|
619 }
|
|
|
620
|
|
|
621 # ---------------------- AA composition in CDR3 ----------------------
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622
|
|
|
623 AACDR3 = PRODF[,c("Sample", "CDR3.Seq")]
|
|
|
624
|
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625 TotalPerSample = data.frame(data.table(AACDR3)[, list(total=sum(nchar(as.character(.SD$CDR3.Seq)))), by=Sample])
|
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626
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627 AAfreq = list()
|
|
|
628
|
|
|
629 for(i in 1:nrow(TotalPerSample)){
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|
|
630 sample = TotalPerSample$Sample[i]
|
|
|
631 AAfreq[[i]] = data.frame(table(unlist(strsplit(as.character(AACDR3[AACDR3$Sample == sample,c("CDR3.Seq")]), ""))))
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|
|
632 AAfreq[[i]]$Sample = sample
|
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633 }
|
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|
634
|
|
|
635 AAfreq = ldply(AAfreq, data.frame)
|
|
|
636 AAfreq = merge(AAfreq, TotalPerSample, by="Sample", all.x = T)
|
|
|
637 AAfreq$freq_perc = as.numeric(AAfreq$Freq / AAfreq$total * 100)
|
|
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638
|
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|
639
|
|
|
640 AAorder = read.table(sep="\t", header=TRUE, text="order.aa\tAA\n1\tR\n2\tK\n3\tN\n4\tD\n5\tQ\n6\tE\n7\tH\n8\tP\n9\tY\n10\tW\n11\tS\n12\tT\n13\tG\n14\tA\n15\tM\n16\tC\n17\tF\n18\tL\n19\tV\n20\tI")
|
|
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641 AAfreq = merge(AAfreq, AAorder, by.x='Var1', by.y='AA', all.x=TRUE)
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|
|
642
|
|
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643 AAfreq = AAfreq[!is.na(AAfreq$order.aa),]
|
|
|
644
|
|
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645 AAfreqplot = ggplot(AAfreq)
|
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|
646 AAfreqplot = AAfreqplot + geom_bar(aes( x=factor(reorder(Var1, order.aa)), y = freq_perc, fill = Sample), stat='identity', position='dodge' )
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647 AAfreqplot = AAfreqplot + annotate("rect", xmin = 0.5, xmax = 2.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2)
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648 AAfreqplot = AAfreqplot + annotate("rect", xmin = 3.5, xmax = 4.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2)
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649 AAfreqplot = AAfreqplot + annotate("rect", xmin = 5.5, xmax = 6.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2)
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650 AAfreqplot = AAfreqplot + annotate("rect", xmin = 6.5, xmax = 7.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2)
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651 AAfreqplot = AAfreqplot + ggtitle("Amino Acid Composition in the CDR3") + xlab("Amino Acid, from Hydrophilic (left) to Hydrophobic (right)") + ylab("Percentage")
|
|
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652
|
|
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653 png("AAComposition.png",width = 1280, height = 720)
|
|
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654 AAfreqplot
|
|
|
655 dev.off()
|
|
|
656 write.table(AAfreq, "AAComposition.csv" , sep=",",quote=F,na="-",row.names=F,col.names=T)
|
|
|
657
|
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658
|
