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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$freq = 1
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98 }
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99 }
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100
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101
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102
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103 #write the complete dataset that is left over, will be the input if 'none' for clonaltype and 'no' for filterproductive
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104 write.table(PRODF, "allUnique.csv", sep=",",quote=F,row.names=F,col.names=T)
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105 write.table(UNPROD, "allUnproductive.csv", sep=",",quote=F,row.names=F,col.names=T)
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106
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107 #write the samples to a file
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108 sampleFile <- file("samples.txt")
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109 un = unique(inputdata$Sample)
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110 un = paste(un, sep="\n")
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111 writeLines(un, sampleFile)
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112 close(sampleFile)
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113
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114 # ---------------------- Counting the productive/unproductive and unique sequences ----------------------
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115
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116 inputdata.dt = data.table(inputdata) #for speed
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117
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118 if(clonaltype == "none"){
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119 ct = c("clonaltype")
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120 }
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121
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122 inputdata.dt$samples_replicates = paste(inputdata.dt$Sample, inputdata.dt$Replicate, sep="_")
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123 samples_replicates = c(unique(inputdata.dt$samples_replicates), unique(as.character(inputdata.dt$Sample)))
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124 frequency_table = data.frame(ID = samples_replicates[order(samples_replicates)])
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125
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126
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127 sample_productive_count = inputdata.dt[, list(All=.N,
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128 Productive = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",]),
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129 perc_prod = 1,
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130 Productive_unique = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",list(count=.N),by=ct]),
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131 perc_prod_un = 1,
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132 Unproductive= nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",]),
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133 perc_unprod = 1,
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134 Unproductive_unique =nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",list(count=.N),by=ct]),
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135 perc_unprod_un = 1),
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136 by=c("Sample")]
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137
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138 sample_productive_count$perc_prod = round(sample_productive_count$Productive / sample_productive_count$All * 100)
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139 sample_productive_count$perc_prod_un = round(sample_productive_count$Productive_unique / sample_productive_count$All * 100)
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140
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141 sample_productive_count$perc_unprod = round(sample_productive_count$Unproductive / sample_productive_count$All * 100)
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142 sample_productive_count$perc_unprod_un = round(sample_productive_count$Unproductive_unique / sample_productive_count$All * 100)
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143
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144
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145 sample_replicate_productive_count = inputdata.dt[, list(All=.N,
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146 Productive = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",]),
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147 perc_prod = 1,
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148 Productive_unique = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",list(count=.N),by=ct]),
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149 perc_prod_un = 1,
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150 Unproductive= nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",]),
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151 perc_unprod = 1,
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152 Unproductive_unique =nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",list(count=.N),by=ct]),
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153 perc_unprod_un = 1),
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154 by=c("samples_replicates")]
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155
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156 sample_replicate_productive_count$perc_prod = round(sample_replicate_productive_count$Productive / sample_replicate_productive_count$All * 100)
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157 sample_replicate_productive_count$perc_prod_un = round(sample_replicate_productive_count$Productive_unique / sample_replicate_productive_count$All * 100)
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158
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159 sample_replicate_productive_count$perc_unprod = round(sample_replicate_productive_count$Unproductive / sample_replicate_productive_count$All * 100)
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160 sample_replicate_productive_count$perc_unprod_un = round(sample_replicate_productive_count$Unproductive_unique / sample_replicate_productive_count$All * 100)
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161
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162 setnames(sample_replicate_productive_count, colnames(sample_productive_count))
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163
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164 counts = rbind(sample_replicate_productive_count, sample_productive_count)
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165 counts = counts[order(counts$Sample),]
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166
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167 write.table(x=counts, file="productive_counting.txt", sep=",",quote=F,row.names=F,col.names=F)
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168
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169 # ---------------------- Frequency calculation for V, D and J ----------------------
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170
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171 PRODFV = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.V.Gene")])
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172 Total = ddply(PRODFV, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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173 PRODFV = merge(PRODFV, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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174 PRODFV = ddply(PRODFV, c("Sample", "Top.V.Gene"), summarise, relFreq= (Length*100 / Total))
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175
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176 PRODFD = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.D.Gene")])
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177 Total = ddply(PRODFD, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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178 PRODFD = merge(PRODFD, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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179 PRODFD = ddply(PRODFD, c("Sample", "Top.D.Gene"), summarise, relFreq= (Length*100 / Total))
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180
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181 PRODFJ = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.J.Gene")])
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182 Total = ddply(PRODFJ, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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183 PRODFJ = merge(PRODFJ, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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184 PRODFJ = ddply(PRODFJ, c("Sample", "Top.J.Gene"), summarise, relFreq= (Length*100 / Total))
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185
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186 # ---------------------- Setting up the gene names for the different species/loci ----------------------
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187
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188 Vchain = ""
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189 Dchain = ""
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190 Jchain = ""
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191
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192 if(species == "custom"){
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193 print("Custom genes: ")
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194 splt = unlist(strsplit(locus, ";"))
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195 print(paste("V:", splt[1]))
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196 print(paste("D:", splt[2]))
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197 print(paste("J:", splt[3]))
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198
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199 Vchain = unlist(strsplit(splt[1], ","))
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200 Vchain = data.frame(v.name = Vchain, chr.orderV = 1:length(Vchain))
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201
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202 Dchain = unlist(strsplit(splt[2], ","))
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203 if(length(Dchain) > 0){
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204 Dchain = data.frame(v.name = Dchain, chr.orderD = 1:length(Dchain))
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205 } else {
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206 Dchain = data.frame(v.name = character(0), chr.orderD = numeric(0))
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207 }
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208
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209 Jchain = unlist(strsplit(splt[3], ","))
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210 Jchain = data.frame(v.name = Jchain, chr.orderJ = 1:length(Jchain))
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211
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212 } else {
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213 genes = read.table("genes.txt", sep="\t", header=TRUE, fill=T, comment.char="")
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214
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215 Vchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "V",c("IMGT.GENE.DB", "chr.order")]
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216 colnames(Vchain) = c("v.name", "chr.orderV")
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217 Dchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "D",c("IMGT.GENE.DB", "chr.order")]
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218 colnames(Dchain) = c("v.name", "chr.orderD")
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219 Jchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "J",c("IMGT.GENE.DB", "chr.order")]
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220 colnames(Jchain) = c("v.name", "chr.orderJ")
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221 }
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222 useD = TRUE
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223 if(nrow(Dchain) == 0){
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224 useD = FALSE
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225 cat("No D Genes in this species/locus")
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226 }
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227 print(paste("useD:", useD))
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228
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229 # ---------------------- merge with the frequency count ----------------------
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230
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231 PRODFV = merge(PRODFV, Vchain, by.x='Top.V.Gene', by.y='v.name', all.x=TRUE)
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232
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233 PRODFD = merge(PRODFD, Dchain, by.x='Top.D.Gene', by.y='v.name', all.x=TRUE)
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234
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235 PRODFJ = merge(PRODFJ, Jchain, by.x='Top.J.Gene', by.y='v.name', all.x=TRUE)
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236
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237 # ---------------------- Create the V, D and J frequency plots and write the data.frame for every plot to a file ----------------------
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238
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239 pV = ggplot(PRODFV)
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240 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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241 pV = pV + xlab("Summary of V gene") + ylab("Frequency") + ggtitle("Relative frequency of V gene usage")
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242 write.table(x=PRODFV, file="VFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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243
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244 png("VPlot.png",width = 1280, height = 720)
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245 pV
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246 dev.off();
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247
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248 if(useD){
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249 pD = ggplot(PRODFD)
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250 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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251 pD = pD + xlab("Summary of D gene") + ylab("Frequency") + ggtitle("Relative frequency of D gene usage")
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252 write.table(x=PRODFD, file="DFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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253
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254 png("DPlot.png",width = 800, height = 600)
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255 print(pD)
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256 dev.off();
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257 }
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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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262 write.table(x=PRODFJ, file="JFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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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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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 # ---------------------- Now the frequency plots of the V, D and J families ----------------------
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278
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279 VGenes = PRODF[,c("Sample", "Top.V.Gene")]
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280 VGenes$Top.V.Gene = gsub("-.*", "", VGenes$Top.V.Gene)
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281 VGenes = data.frame(data.table(VGenes)[, list(Count=.N), by=c("Sample", "Top.V.Gene")])
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282 TotalPerSample = data.frame(data.table(VGenes)[, list(total=sum(.SD$Count)), by=Sample])
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283 VGenes = merge(VGenes, TotalPerSample, by="Sample")
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284 VGenes$Frequency = VGenes$Count * 100 / VGenes$total
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285 VPlot = ggplot(VGenes)
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286 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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287 ggtitle("Distribution of V gene families") +
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288 ylab("Percentage of sequences")
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289 png("VFPlot.png")
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290 VPlot
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291 dev.off();
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292 write.table(x=VGenes, file="VFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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293
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294 if(useD){
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295 DGenes = PRODF[,c("Sample", "Top.D.Gene")]
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296 DGenes$Top.D.Gene = gsub("-.*", "", DGenes$Top.D.Gene)
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297 DGenes = data.frame(data.table(DGenes)[, list(Count=.N), by=c("Sample", "Top.D.Gene")])
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298 TotalPerSample = data.frame(data.table(DGenes)[, list(total=sum(.SD$Count)), by=Sample])
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|
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299 DGenes = merge(DGenes, TotalPerSample, by="Sample")
|
|
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300 DGenes$Frequency = DGenes$Count * 100 / DGenes$total
|
|
|
301 DPlot = ggplot(DGenes)
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302 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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303 ggtitle("Distribution of D gene families") +
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304 ylab("Percentage of sequences")
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305 png("DFPlot.png")
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306 print(DPlot)
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307 dev.off();
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308 write.table(x=DGenes, file="DFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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309 }
|
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310
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311 JGenes = PRODF[,c("Sample", "Top.J.Gene")]
|
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312 JGenes$Top.J.Gene = gsub("-.*", "", JGenes$Top.J.Gene)
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313 JGenes = data.frame(data.table(JGenes)[, list(Count=.N), by=c("Sample", "Top.J.Gene")])
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314 TotalPerSample = data.frame(data.table(JGenes)[, list(total=sum(.SD$Count)), by=Sample])
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315 JGenes = merge(JGenes, TotalPerSample, by="Sample")
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316 JGenes$Frequency = JGenes$Count * 100 / JGenes$total
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317 JPlot = ggplot(JGenes)
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318 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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319 ggtitle("Distribution of J gene families") +
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320 ylab("Percentage of sequences")
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321 png("JFPlot.png")
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322 JPlot
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323 dev.off();
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324 write.table(x=JGenes, file="JFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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325
|
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326 # ---------------------- Plotting the cdr3 length ----------------------
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327
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328 CDR3Length = data.frame(data.table(PRODF)[, list(Count=.N), by=c("Sample", "CDR3.Length.DNA")])
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329 TotalPerSample = data.frame(data.table(CDR3Length)[, list(total=sum(.SD$Count)), by=Sample])
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330 CDR3Length = merge(CDR3Length, TotalPerSample, by="Sample")
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331 CDR3Length$Frequency = CDR3Length$Count * 100 / CDR3Length$total
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332 CDR3LengthPlot = ggplot(CDR3Length)
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333 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)) +
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334 ggtitle("Length distribution of CDR3") +
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335 xlab("CDR3 Length") +
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336 ylab("Percentage of sequences")
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337 png("CDR3LengthPlot.png",width = 1280, height = 720)
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338 CDR3LengthPlot
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339 dev.off()
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340 write.table(x=CDR3Length, file="CDR3LengthPlot.csv", sep=",",quote=F,row.names=F,col.names=T)
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341
|
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|
342 # ---------------------- Plot the heatmaps ----------------------
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343
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344
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345 #get the reverse order for the V and D genes
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346 revVchain = Vchain
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347 revDchain = Dchain
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|
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348 revVchain$chr.orderV = rev(revVchain$chr.orderV)
|
|
|
349 revDchain$chr.orderD = rev(revDchain$chr.orderD)
|
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350
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|
|
351 if(useD){
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|
352 plotVD <- function(dat){
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|
|
353 if(length(dat[,1]) == 0){
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|
354 return()
|
|
|
355 }
|
|
|
356 img = ggplot() +
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|
|
357 geom_tile(data=dat, aes(x=factor(reorder(Top.D.Gene, chr.orderD)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
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|
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358 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
359 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
360 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
361 xlab("D genes") +
|
|
|
362 ylab("V Genes")
|
|
|
363
|
|
|
364 png(paste("HeatmapVD_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Dchain$v.name)), height=100+(15*length(Vchain$v.name)))
|
|
|
365 print(img)
|
|
|
366 dev.off()
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|
|
367 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)
|
|
|
368 }
|
|
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369
|
|
|
370 VandDCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.D.Gene", "Sample")])
|
|
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371
|
|
|
372 VandDCount$l = log(VandDCount$Length)
|
|
|
373 maxVD = data.frame(data.table(VandDCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
374 VandDCount = merge(VandDCount, maxVD, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
375 VandDCount$relLength = VandDCount$l / VandDCount$max
|
|
|
376
|
|
|
377 cartegianProductVD = expand.grid(Top.V.Gene = Vchain$v.name, Top.D.Gene = Dchain$v.name, Sample = unique(inputdata$Sample))
|
|
|
378
|
|
|
379 completeVD = merge(VandDCount, cartegianProductVD, all.y=TRUE)
|
|
|
380 completeVD = merge(completeVD, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
|
|
|
381 completeVD = merge(completeVD, Dchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
|
|
|
382 VDList = split(completeVD, f=completeVD[,"Sample"])
|
|
|
383
|
|
|
384 lapply(VDList, FUN=plotVD)
|
|
|
385 }
|
|
|
386
|
|
|
387 plotVJ <- function(dat){
|
|
|
388 if(length(dat[,1]) == 0){
|
|
|
389 return()
|
|
|
390 }
|
|
|
391 cat(paste(unique(dat[3])[1,1]))
|
|
|
392 img = ggplot() +
|
|
|
393 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
|
|
|
394 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
395 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
396 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
397 xlab("J genes") +
|
|
|
398 ylab("V Genes")
|
|
|
399
|
|
|
400 png(paste("HeatmapVJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Vchain$v.name)))
|
|
|
401 print(img)
|
|
|
402 dev.off()
|
|
|
403 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)
|
|
|
404 }
|
|
|
405
|
|
|
406 VandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.J.Gene", "Sample")])
|
|
|
407
|
|
|
408 VandJCount$l = log(VandJCount$Length)
|
|
|
409 maxVJ = data.frame(data.table(VandJCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
410 VandJCount = merge(VandJCount, maxVJ, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
411 VandJCount$relLength = VandJCount$l / VandJCount$max
|
|
|
412
|
|
|
413 cartegianProductVJ = expand.grid(Top.V.Gene = Vchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(inputdata$Sample))
|
|
|
414
|
|
|
415 completeVJ = merge(VandJCount, cartegianProductVJ, all.y=TRUE)
|
|
|
416 completeVJ = merge(completeVJ, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
|
|
|
417 completeVJ = merge(completeVJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
|
|
|
418 VJList = split(completeVJ, f=completeVJ[,"Sample"])
|
|
|
419 lapply(VJList, FUN=plotVJ)
|
|
|
420
|
|
|
421 if(useD){
|
|
|
422 plotDJ <- function(dat){
|
|
|
423 if(length(dat[,1]) == 0){
|
|
|
424 return()
|
|
|
425 }
|
|
|
426 img = ggplot() +
|
|
|
427 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.D.Gene, chr.orderD)), fill=relLength)) +
|
|
|
428 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
429 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
430 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
431 xlab("J genes") +
|
|
|
432 ylab("D Genes")
|
|
|
433
|
|
|
434 png(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Dchain$v.name)))
|
|
|
435 print(img)
|
|
|
436 dev.off()
|
|
|
437 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)
|
|
|
438 }
|
|
|
439
|
|
|
440
|
|
|
441 DandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.D.Gene", "Top.J.Gene", "Sample")])
|
|
|
442
|
|
|
443 DandJCount$l = log(DandJCount$Length)
|
|
|
444 maxDJ = data.frame(data.table(DandJCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
445 DandJCount = merge(DandJCount, maxDJ, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
446 DandJCount$relLength = DandJCount$l / DandJCount$max
|
|
|
447
|
|
|
448 cartegianProductDJ = expand.grid(Top.D.Gene = Dchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(inputdata$Sample))
|
|
|
449
|
|
|
450 completeDJ = merge(DandJCount, cartegianProductDJ, all.y=TRUE)
|
|
|
451 completeDJ = merge(completeDJ, revDchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
|
|
|
452 completeDJ = merge(completeDJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
|
|
|
453 DJList = split(completeDJ, f=completeDJ[,"Sample"])
|
|
|
454 lapply(DJList, FUN=plotDJ)
|
|
|
455 }
|
|
|
456
|
|
|
457
|
|
|
458 # ---------------------- calculating the clonality score ----------------------
|
|
|
459
|
|
|
460 if("Replicate" %in% colnames(inputdata)) #can only calculate clonality score when replicate information is available
|
|
|
461 {
|
|
|
462 if(clonality_method == "boyd"){
|
|
|
463 samples = split(clonalityFrame, clonalityFrame$Sample, drop=T)
|
|
|
464
|
|
|
465 for (sample in samples){
|
|
|
466 res = data.frame(paste=character(0))
|
|
|
467 sample_id = unique(sample$Sample)[[1]]
|
|
|
468 for(replicate in unique(sample$Replicate)){
|
|
|
469 tmp = sample[sample$Replicate == replicate,]
|
|
|
470 clone_table = data.frame(table(tmp$clonaltype))
|
|
|
471 clone_col_name = paste("V", replicate, sep="")
|
|
|
472 colnames(clone_table) = c("paste", clone_col_name)
|
|
|
473 res = merge(res, clone_table, by="paste", all=T)
|
|
|
474 }
|
|
|
475
|
|
|
476 res[is.na(res)] = 0
|
|
|
477 infer.result = infer.clonality(as.matrix(res[,2:ncol(res)]))
|
|
|
478
|
|
|
479 write.table(data.table(infer.result[[12]]), file=paste("lymphclon_clonality_", sample_id, ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=F)
|
|
|
480
|
|
|
481 res$type = rowSums(res[,2:ncol(res)])
|
|
|
482
|
|
|
483 coincidence.table = data.frame(table(res$type))
|
|
|
484 colnames(coincidence.table) = c("Coincidence Type", "Raw Coincidence Freq")
|
|
|
485 write.table(coincidence.table, file=paste("lymphclon_coincidences_", sample_id, ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=T)
|
|
|
486 }
|
|
|
487 } else {
|
|
|
488 write.table(clonalityFrame, "clonalityComplete.csv", sep=",",quote=F,row.names=F,col.names=T)
|
|
|
489
|
|
|
490 clonalFreq = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "clonaltype")])
|
|
|
491 clonalFreqCount = data.frame(data.table(clonalFreq)[, list(Count=.N), by=c("Sample", "Type")])
|
|
|
492 clonalFreqCount$realCount = clonalFreqCount$Type * clonalFreqCount$Count
|
|
|
493 clonalSum = data.frame(data.table(clonalFreqCount)[, list(Reads=sum(realCount)), by=c("Sample")])
|
|
|
494 clonalFreqCount = merge(clonalFreqCount, clonalSum, by.x="Sample", by.y="Sample")
|
|
|
495
|
|
|
496 ct = c('Type\tWeight\n2\t1\n3\t3\n4\t6\n5\t10\n6\t15')
|
|
|
497 tcct = textConnection(ct)
|
|
|
498 CT = read.table(tcct, sep="\t", header=TRUE)
|
|
|
499 close(tcct)
|
|
|
500 clonalFreqCount = merge(clonalFreqCount, CT, by.x="Type", by.y="Type", all.x=T)
|
|
|
501 clonalFreqCount$WeightedCount = clonalFreqCount$Count * clonalFreqCount$Weight
|
|
|
502
|
|
|
503 ReplicateReads = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "Replicate", "clonaltype")])
|
|
|
504 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(Reads=.N), by=c("Sample", "Replicate")])
|
|
|
505 clonalFreqCount$Reads = as.numeric(clonalFreqCount$Reads)
|
|
|
506 ReplicateReads$squared = ReplicateReads$Reads * ReplicateReads$Reads
|
|
|
507
|
|
|
508 ReplicatePrint <- function(dat){
|
|
|
509 write.table(dat[-1], paste("ReplicateReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
510 }
|
|
|
511
|
|
|
512 ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
|
|
|
513 lapply(ReplicateSplit, FUN=ReplicatePrint)
|
|
|
514
|
|
|
515 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(as.numeric(Reads)), ReadsSquaredSum=sum(as.numeric(squared))), by=c("Sample")])
|
|
|
516 clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
517
|
|
|
518 ReplicateSumPrint <- function(dat){
|
|
|
519 write.table(dat[-1], paste("ReplicateSumReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
520 }
|
|
|
521
|
|
|
522 ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
|
|
|
523 lapply(ReplicateSumSplit, FUN=ReplicateSumPrint)
|
|
|
524
|
|
|
525 clonalFreqCountSum = data.frame(data.table(clonalFreqCount)[, list(Numerator=sum(WeightedCount, na.rm=T)), by=c("Sample")])
|
|
|
526 clonalFreqCount = merge(clonalFreqCount, clonalFreqCountSum, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
527 clonalFreqCount$ReadsSum = as.numeric(clonalFreqCount$ReadsSum) #prevent integer overflow
|
|
|
528 clonalFreqCount$Denominator = (((clonalFreqCount$ReadsSum * clonalFreqCount$ReadsSum) - clonalFreqCount$ReadsSquaredSum) / 2)
|
|
|
529 clonalFreqCount$Result = (clonalFreqCount$Numerator + 1) / (clonalFreqCount$Denominator + 1)
|
|
|
530
|
|
|
531 ClonalityScorePrint <- function(dat){
|
|
|
532 write.table(dat$Result, paste("ClonalityScore_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
533 }
|
|
|
534
|
|
|
535 clonalityScore = clonalFreqCount[c("Sample", "Result")]
|
|
|
536 clonalityScore = unique(clonalityScore)
|
|
|
537
|
|
|
538 clonalityScoreSplit = split(clonalityScore, f=clonalityScore[,"Sample"])
|
|
|
539 lapply(clonalityScoreSplit, FUN=ClonalityScorePrint)
|
|
|
540
|
|
|
541 clonalityOverview = clonalFreqCount[c("Sample", "Type", "Count", "Weight", "WeightedCount")]
|
|
|
542
|
|
|
543
|
|
|
544
|
|
|
545 ClonalityOverviewPrint <- function(dat){
|
|
|
546 write.table(dat[-1], paste("ClonalityOverView_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
547 }
|
|
|
548
|
|
|
549 clonalityOverviewSplit = split(clonalityOverview, f=clonalityOverview$Sample)
|
|
|
550 lapply(clonalityOverviewSplit, FUN=ClonalityOverviewPrint)
|
|
|
551 }
|
|
|
552 }
|
|
|
553
|
|
|
554 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")
|
|
|
555 if(all(imgtcolumns %in% colnames(inputdata)))
|
|
|
556 {
|
|
|
557 newData = data.frame(data.table(PRODF)[,list(unique=.N,
|
|
|
558 VH.DEL=mean(X3V.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
559 P1=mean(P3V.nt.nb, na.rm=T),
|
|
|
560 N1=mean(N1.REGION.nt.nb, na.rm=T),
|
|
|
561 P2=mean(P5D.nt.nb, na.rm=T),
|
|
|
562 DEL.DH=mean(X5D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
563 DH.DEL=mean(X3D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
564 P3=mean(P3D.nt.nb, na.rm=T),
|
|
|
565 N2=mean(N2.REGION.nt.nb, na.rm=T),
|
|
|
566 P4=mean(P5J.nt.nb, na.rm=T),
|
|
|
567 DEL.JH=mean(X5J.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
568 Total.Del=( mean(X3V.REGION.trimmed.nt.nb, na.rm=T) +
|
|
|
569 mean(X5D.REGION.trimmed.nt.nb, na.rm=T) +
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570 mean(X3D.REGION.trimmed.nt.nb, na.rm=T) +
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571 mean(X5J.REGION.trimmed.nt.nb, na.rm=T)),
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572
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573 Total.N=( mean(N1.REGION.nt.nb, na.rm=T) +
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574 mean(N2.REGION.nt.nb, na.rm=T)),
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575
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576 Total.P=( mean(P3V.nt.nb, na.rm=T) +
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577 mean(P5D.nt.nb, na.rm=T) +
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578 mean(P3D.nt.nb, na.rm=T) +
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579 mean(P5J.nt.nb, na.rm=T))),
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580 by=c("Sample")])
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581 write.table(newData, "junctionAnalysisProd.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F)
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582
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583 newData = data.frame(data.table(UNPROD)[,list(unique=.N,
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584 VH.DEL=mean(X3V.REGION.trimmed.nt.nb, na.rm=T),
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585 P1=mean(P3V.nt.nb, na.rm=T),
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586 N1=mean(N1.REGION.nt.nb, na.rm=T),
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587 P2=mean(P5D.nt.nb, na.rm=T),
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588 DEL.DH=mean(X5D.REGION.trimmed.nt.nb, na.rm=T),
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589 DH.DEL=mean(X3D.REGION.trimmed.nt.nb, na.rm=T),
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590 P3=mean(P3D.nt.nb, na.rm=T),
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591 N2=mean(N2.REGION.nt.nb, na.rm=T),
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592 P4=mean(P5J.nt.nb, na.rm=T),
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593 DEL.JH=mean(X5J.REGION.trimmed.nt.nb, na.rm=T),
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594 Total.Del=( mean(X3V.REGION.trimmed.nt.nb, na.rm=T) +
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595 mean(X5D.REGION.trimmed.nt.nb, na.rm=T) +
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596 mean(X3D.REGION.trimmed.nt.nb, na.rm=T) +
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597 mean(X5J.REGION.trimmed.nt.nb, na.rm=T)),
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|
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598
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599 Total.N=( mean(N1.REGION.nt.nb, na.rm=T) +
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600 mean(N2.REGION.nt.nb, na.rm=T)),
|
|
|
601
|
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602 Total.P=( mean(P3V.nt.nb, na.rm=T) +
|
|
|
603 mean(P5D.nt.nb, na.rm=T) +
|
|
|
604 mean(P3D.nt.nb, na.rm=T) +
|
|
|
605 mean(P5J.nt.nb, na.rm=T))),
|
|
|
606 by=c("Sample")])
|
|
|
607 write.table(newData, "junctionAnalysisUnProd.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
608 }
|
|
|
609
|
|
|
610 # ---------------------- AA composition in CDR3 ----------------------
|
|
|
611
|
|
|
612 AACDR3 = PRODF[,c("Sample", "CDR3.Seq")]
|
|
|
613
|
|
|
614 TotalPerSample = data.frame(data.table(AACDR3)[, list(total=sum(nchar(as.character(.SD$CDR3.Seq)))), by=Sample])
|
|
|
615
|
|
|
616 AAfreq = list()
|
|
|
617
|
|
|
618 for(i in 1:nrow(TotalPerSample)){
|
|
|
619 sample = TotalPerSample$Sample[i]
|
|
|
620 AAfreq[[i]] = data.frame(table(unlist(strsplit(as.character(AACDR3[AACDR3$Sample == sample,c("CDR3.Seq")]), ""))))
|
|
|
621 AAfreq[[i]]$Sample = sample
|
|
|
622 }
|
|
|
623
|
|
|
624 AAfreq = ldply(AAfreq, data.frame)
|
|
|
625 AAfreq = merge(AAfreq, TotalPerSample, by="Sample", all.x = T)
|
|
|
626 AAfreq$freq_perc = as.numeric(AAfreq$Freq / AAfreq$total * 100)
|
|
|
627
|
|
|
628
|
|
|
629 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")
|
|
|
630 AAfreq = merge(AAfreq, AAorder, by.x='Var1', by.y='AA', all.x=TRUE)
|
|
|
631
|
|
|
632 AAfreq = AAfreq[!is.na(AAfreq$order.aa),]
|
|
|
633
|
|
|
634 AAfreqplot = ggplot(AAfreq)
|
|
|
635 AAfreqplot = AAfreqplot + geom_bar(aes( x=factor(reorder(Var1, order.aa)), y = freq_perc, fill = Sample), stat='identity', position='dodge' )
|
|
|
636 AAfreqplot = AAfreqplot + annotate("rect", xmin = 0.5, xmax = 2.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2)
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|
|
637 AAfreqplot = AAfreqplot + annotate("rect", xmin = 3.5, xmax = 4.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2)
|
|
|
638 AAfreqplot = AAfreqplot + annotate("rect", xmin = 5.5, xmax = 6.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2)
|
|
|
639 AAfreqplot = AAfreqplot + annotate("rect", xmin = 6.5, xmax = 7.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2)
|
|
|
640 AAfreqplot = AAfreqplot + ggtitle("Amino Acid Composition in the CDR3") + xlab("Amino Acid, from Hydrophilic (left) to Hydrophobic (right)") + ylab("Percentage")
|
|
|
641
|
|
|
642 png("AAComposition.png",width = 1280, height = 720)
|
|
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643 AAfreqplot
|
|
|
644 dev.off()
|
|
|
645 write.table(AAfreq, "AAComposition.csv" , sep=",",quote=F,na="-",row.names=F,col.names=T)
|
|
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646
|
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647
|
