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12
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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 inputdata.dt = data.table(inputdata) #for speed
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118
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119 if(clonaltype == "none"){
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120 ct = c("clonaltype")
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121 }
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122
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123 inputdata.dt$samples_replicates = paste(inputdata.dt$Sample, inputdata.dt$Replicate, sep="_")
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124 samples_replicates = c(unique(inputdata.dt$samples_replicates), unique(as.character(inputdata.dt$Sample)))
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125 frequency_table = data.frame(ID = samples_replicates[order(samples_replicates)])
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126
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127
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128 sample_productive_count = inputdata.dt[, list(All=.N,
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129 Productive = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",]),
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130 perc_prod = 1,
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131 Productive_unique = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",list(count=.N),by=ct]),
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132 perc_prod_un = 1,
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133 Unproductive= nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",]),
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134 perc_unprod = 1,
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135 Unproductive_unique =nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",list(count=.N),by=ct]),
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136 perc_unprod_un = 1),
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137 by=c("Sample")]
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138
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139 sample_productive_count$perc_prod = round(sample_productive_count$Productive / sample_productive_count$All * 100)
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140 sample_productive_count$perc_prod_un = round(sample_productive_count$Productive_unique / sample_productive_count$All * 100)
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141
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142 sample_productive_count$perc_unprod = round(sample_productive_count$Unproductive / sample_productive_count$All * 100)
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143 sample_productive_count$perc_unprod_un = round(sample_productive_count$Unproductive_unique / sample_productive_count$All * 100)
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144
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145
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146 sample_replicate_productive_count = inputdata.dt[, list(All=.N,
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147 Productive = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",]),
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148 perc_prod = 1,
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149 Productive_unique = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",list(count=.N),by=ct]),
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150 perc_prod_un = 1,
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151 Unproductive= nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",]),
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152 perc_unprod = 1,
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153 Unproductive_unique =nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",list(count=.N),by=ct]),
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154 perc_unprod_un = 1),
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155 by=c("samples_replicates")]
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156
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157 sample_replicate_productive_count$perc_prod = round(sample_replicate_productive_count$Productive / sample_replicate_productive_count$All * 100)
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158 sample_replicate_productive_count$perc_prod_un = round(sample_replicate_productive_count$Productive_unique / sample_replicate_productive_count$All * 100)
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159
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160 sample_replicate_productive_count$perc_unprod = round(sample_replicate_productive_count$Unproductive / sample_replicate_productive_count$All * 100)
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161 sample_replicate_productive_count$perc_unprod_un = round(sample_replicate_productive_count$Unproductive_unique / sample_replicate_productive_count$All * 100)
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162
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163 setnames(sample_replicate_productive_count, colnames(sample_productive_count))
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164
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165 counts = rbind(sample_replicate_productive_count, sample_productive_count)
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166 counts = counts[order(counts$Sample),]
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167
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168 write.table(x=counts, file="productive_counting.txt", sep=",",quote=F,row.names=F,col.names=F)
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169
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170 # ---------------------- Frequency calculation for V, D and J ----------------------
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171
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172 PRODFV = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.V.Gene")])
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173 Total = ddply(PRODFV, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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174 PRODFV = merge(PRODFV, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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175 PRODFV = ddply(PRODFV, c("Sample", "Top.V.Gene"), summarise, relFreq= (Length*100 / Total))
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176
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177 PRODFD = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.D.Gene")])
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178 Total = ddply(PRODFD, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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179 PRODFD = merge(PRODFD, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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180 PRODFD = ddply(PRODFD, c("Sample", "Top.D.Gene"), summarise, relFreq= (Length*100 / Total))
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181
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182 PRODFJ = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.J.Gene")])
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183 Total = ddply(PRODFJ, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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184 PRODFJ = merge(PRODFJ, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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185 PRODFJ = ddply(PRODFJ, c("Sample", "Top.J.Gene"), summarise, relFreq= (Length*100 / Total))
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186
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187 # ---------------------- Setting up the gene names for the different species/loci ----------------------
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188
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189 Vchain = ""
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190 Dchain = ""
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191 Jchain = ""
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192
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193 if(species == "custom"){
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194 print("Custom genes: ")
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195 splt = unlist(strsplit(locus, ";"))
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196 print(paste("V:", splt[1]))
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197 print(paste("D:", splt[2]))
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198 print(paste("J:", splt[3]))
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199
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200 Vchain = unlist(strsplit(splt[1], ","))
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201 Vchain = data.frame(v.name = Vchain, chr.orderV = 1:length(Vchain))
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202
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203 Dchain = unlist(strsplit(splt[2], ","))
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204 if(length(Dchain) > 0){
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205 Dchain = data.frame(v.name = Dchain, chr.orderD = 1:length(Dchain))
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206 } else {
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207 Dchain = data.frame(v.name = character(0), chr.orderD = numeric(0))
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208 }
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209
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210 Jchain = unlist(strsplit(splt[3], ","))
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211 Jchain = data.frame(v.name = Jchain, chr.orderJ = 1:length(Jchain))
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212
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213 } else {
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214 genes = read.table("genes.txt", sep="\t", header=TRUE, fill=T, comment.char="")
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215
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216 Vchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "V",c("IMGT.GENE.DB", "chr.order")]
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217 colnames(Vchain) = c("v.name", "chr.orderV")
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218 Dchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "D",c("IMGT.GENE.DB", "chr.order")]
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219 colnames(Dchain) = c("v.name", "chr.orderD")
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220 Jchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "J",c("IMGT.GENE.DB", "chr.order")]
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221 colnames(Jchain) = c("v.name", "chr.orderJ")
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222 }
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223 useD = TRUE
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224 if(nrow(Dchain) == 0){
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225 useD = FALSE
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226 cat("No D Genes in this species/locus")
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227 }
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228 print(paste("useD:", useD))
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229
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230 # ---------------------- merge with the frequency count ----------------------
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231
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232 PRODFV = merge(PRODFV, Vchain, by.x='Top.V.Gene', by.y='v.name', all.x=TRUE)
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233
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234 PRODFD = merge(PRODFD, Dchain, by.x='Top.D.Gene', by.y='v.name', all.x=TRUE)
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235
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236 PRODFJ = merge(PRODFJ, Jchain, by.x='Top.J.Gene', by.y='v.name', all.x=TRUE)
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237
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238 # ---------------------- Create the V, D and J frequency plots and write the data.frame for every plot to a file ----------------------
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239
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240 pV = ggplot(PRODFV)
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241 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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242 pV = pV + xlab("Summary of V gene") + ylab("Frequency") + ggtitle("Relative frequency of V gene usage")
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243 write.table(x=PRODFV, file="VFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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244
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245 png("VPlot.png",width = 1280, height = 720)
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246 pV
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247 dev.off();
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248
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249 if(useD){
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250 pD = ggplot(PRODFD)
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251 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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252 pD = pD + xlab("Summary of D gene") + ylab("Frequency") + ggtitle("Relative frequency of D gene usage")
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253 write.table(x=PRODFD, file="DFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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254
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255 png("DPlot.png",width = 800, height = 600)
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256 print(pD)
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257 dev.off();
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258 }
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259
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260 pJ = ggplot(PRODFJ)
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261 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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262 pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage")
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263 write.table(x=PRODFJ, file="JFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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264
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265 png("JPlot.png",width = 800, height = 600)
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266 pJ
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267 dev.off();
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268
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269 pJ = ggplot(PRODFJ)
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270 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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271 pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage")
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272 write.table(x=PRODFJ, file="JFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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273
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274 png("JPlot.png",width = 800, height = 600)
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275 pJ
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276 dev.off();
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277
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278 # ---------------------- Now the frequency plots of the V, D and J families ----------------------
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279
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280 VGenes = PRODF[,c("Sample", "Top.V.Gene")]
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281 VGenes$Top.V.Gene = gsub("-.*", "", VGenes$Top.V.Gene)
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282 VGenes = data.frame(data.table(VGenes)[, list(Count=.N), by=c("Sample", "Top.V.Gene")])
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283 TotalPerSample = data.frame(data.table(VGenes)[, list(total=sum(.SD$Count)), by=Sample])
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284 VGenes = merge(VGenes, TotalPerSample, by="Sample")
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285 VGenes$Frequency = VGenes$Count * 100 / VGenes$total
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286 VPlot = ggplot(VGenes)
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287 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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288 ggtitle("Distribution of V gene families") +
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289 ylab("Percentage of sequences")
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290 png("VFPlot.png")
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291 VPlot
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292 dev.off();
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293 write.table(x=VGenes, file="VFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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294
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295 if(useD){
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296 DGenes = PRODF[,c("Sample", "Top.D.Gene")]
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297 DGenes$Top.D.Gene = gsub("-.*", "", DGenes$Top.D.Gene)
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298 DGenes = data.frame(data.table(DGenes)[, list(Count=.N), by=c("Sample", "Top.D.Gene")])
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|
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299 TotalPerSample = data.frame(data.table(DGenes)[, list(total=sum(.SD$Count)), by=Sample])
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|
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300 DGenes = merge(DGenes, TotalPerSample, by="Sample")
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301 DGenes$Frequency = DGenes$Count * 100 / DGenes$total
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|
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302 DPlot = ggplot(DGenes)
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303 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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304 ggtitle("Distribution of D gene families") +
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305 ylab("Percentage of sequences")
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306 png("DFPlot.png")
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307 print(DPlot)
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308 dev.off();
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309 write.table(x=DGenes, file="DFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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|
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310 }
|
|
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311
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|
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312 JGenes = PRODF[,c("Sample", "Top.J.Gene")]
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313 JGenes$Top.J.Gene = gsub("-.*", "", JGenes$Top.J.Gene)
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314 JGenes = data.frame(data.table(JGenes)[, list(Count=.N), by=c("Sample", "Top.J.Gene")])
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315 TotalPerSample = data.frame(data.table(JGenes)[, list(total=sum(.SD$Count)), by=Sample])
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316 JGenes = merge(JGenes, TotalPerSample, by="Sample")
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317 JGenes$Frequency = JGenes$Count * 100 / JGenes$total
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318 JPlot = ggplot(JGenes)
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319 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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320 ggtitle("Distribution of J gene families") +
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321 ylab("Percentage of sequences")
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322 png("JFPlot.png")
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323 JPlot
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324 dev.off();
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325 write.table(x=JGenes, file="JFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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326
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327 # ---------------------- Plotting the cdr3 length ----------------------
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328
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329 CDR3Length = data.frame(data.table(PRODF)[, list(Count=.N), by=c("Sample", "CDR3.Length.DNA")])
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330 TotalPerSample = data.frame(data.table(CDR3Length)[, list(total=sum(.SD$Count)), by=Sample])
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331 CDR3Length = merge(CDR3Length, TotalPerSample, by="Sample")
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332 CDR3Length$Frequency = CDR3Length$Count * 100 / CDR3Length$total
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333 CDR3LengthPlot = ggplot(CDR3Length)
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334 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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335 ggtitle("Length distribution of CDR3") +
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336 xlab("CDR3 Length") +
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337 ylab("Percentage of sequences")
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338 png("CDR3LengthPlot.png",width = 1280, height = 720)
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339 CDR3LengthPlot
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340 dev.off()
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341 write.table(x=CDR3Length, file="CDR3LengthPlot.csv", sep=",",quote=F,row.names=F,col.names=T)
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342
|
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|
343 # ---------------------- Plot the heatmaps ----------------------
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344
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345
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346 #get the reverse order for the V and D genes
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347 revVchain = Vchain
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348 revDchain = Dchain
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|
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349 revVchain$chr.orderV = rev(revVchain$chr.orderV)
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|
|
350 revDchain$chr.orderD = rev(revDchain$chr.orderD)
|
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351
|
|
|
352 if(useD){
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|
353 plotVD <- function(dat){
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|
|
354 if(length(dat[,1]) == 0){
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355 return()
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|
|
356 }
|
|
|
357 img = ggplot() +
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|
|
358 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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359 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
360 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
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|
361 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
362 xlab("D genes") +
|
|
|
363 ylab("V Genes")
|
|
|
364
|
|
|
365 png(paste("HeatmapVD_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Dchain$v.name)), height=100+(15*length(Vchain$v.name)))
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|
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366 print(img)
|
|
|
367 dev.off()
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|
368 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)
|
|
|
369 }
|
|
|
370
|
|
|
371 VandDCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.D.Gene", "Sample")])
|
|
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372
|
|
|
373 VandDCount$l = log(VandDCount$Length)
|
|
|
374 maxVD = data.frame(data.table(VandDCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
375 VandDCount = merge(VandDCount, maxVD, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
376 VandDCount$relLength = VandDCount$l / VandDCount$max
|
|
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377
|
|
|
378 cartegianProductVD = expand.grid(Top.V.Gene = Vchain$v.name, Top.D.Gene = Dchain$v.name, Sample = unique(inputdata$Sample))
|
|
|
379
|
|
|
380 completeVD = merge(VandDCount, cartegianProductVD, all.y=TRUE)
|
|
|
381 completeVD = merge(completeVD, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
|
|
|
382 completeVD = merge(completeVD, Dchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
|
|
|
383 VDList = split(completeVD, f=completeVD[,"Sample"])
|
|
|
384
|
|
|
385 lapply(VDList, FUN=plotVD)
|
|
|
386 }
|
|
|
387
|
|
|
388 plotVJ <- function(dat){
|
|
|
389 if(length(dat[,1]) == 0){
|
|
|
390 return()
|
|
|
391 }
|
|
|
392 cat(paste(unique(dat[3])[1,1]))
|
|
|
393 img = ggplot() +
|
|
|
394 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
|
|
|
395 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
396 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
397 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
398 xlab("J genes") +
|
|
|
399 ylab("V Genes")
|
|
|
400
|
|
|
401 png(paste("HeatmapVJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Vchain$v.name)))
|
|
|
402 print(img)
|
|
|
403 dev.off()
|
|
|
404 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)
|
|
|
405 }
|
|
|
406
|
|
|
407 VandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.J.Gene", "Sample")])
|
|
|
408
|
|
|
409 VandJCount$l = log(VandJCount$Length)
|
|
|
410 maxVJ = data.frame(data.table(VandJCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
411 VandJCount = merge(VandJCount, maxVJ, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
412 VandJCount$relLength = VandJCount$l / VandJCount$max
|
|
|
413
|
|
|
414 cartegianProductVJ = expand.grid(Top.V.Gene = Vchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(inputdata$Sample))
|
|
|
415
|
|
|
416 completeVJ = merge(VandJCount, cartegianProductVJ, all.y=TRUE)
|
|
|
417 completeVJ = merge(completeVJ, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
|
|
|
418 completeVJ = merge(completeVJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
|
|
|
419 VJList = split(completeVJ, f=completeVJ[,"Sample"])
|
|
|
420 lapply(VJList, FUN=plotVJ)
|
|
|
421
|
|
|
422 if(useD){
|
|
|
423 plotDJ <- function(dat){
|
|
|
424 if(length(dat[,1]) == 0){
|
|
|
425 return()
|
|
|
426 }
|
|
|
427 img = ggplot() +
|
|
|
428 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.D.Gene, chr.orderD)), fill=relLength)) +
|
|
|
429 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
|
|
430 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
|
|
431 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
|
|
432 xlab("J genes") +
|
|
|
433 ylab("D Genes")
|
|
|
434
|
|
|
435 png(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Dchain$v.name)))
|
|
|
436 print(img)
|
|
|
437 dev.off()
|
|
|
438 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)
|
|
|
439 }
|
|
|
440
|
|
|
441
|
|
|
442 DandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.D.Gene", "Top.J.Gene", "Sample")])
|
|
|
443
|
|
|
444 DandJCount$l = log(DandJCount$Length)
|
|
|
445 maxDJ = data.frame(data.table(DandJCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
446 DandJCount = merge(DandJCount, maxDJ, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
447 DandJCount$relLength = DandJCount$l / DandJCount$max
|
|
|
448
|
|
|
449 cartegianProductDJ = expand.grid(Top.D.Gene = Dchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(inputdata$Sample))
|
|
|
450
|
|
|
451 completeDJ = merge(DandJCount, cartegianProductDJ, all.y=TRUE)
|
|
|
452 completeDJ = merge(completeDJ, revDchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
|
|
|
453 completeDJ = merge(completeDJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
|
|
|
454 DJList = split(completeDJ, f=completeDJ[,"Sample"])
|
|
|
455 lapply(DJList, FUN=plotDJ)
|
|
|
456 }
|
|
|
457
|
|
|
458
|
|
|
459 # ---------------------- calculating the clonality score ----------------------
|
|
|
460
|
|
|
461 if("Replicate" %in% colnames(inputdata)) #can only calculate clonality score when replicate information is available
|
|
|
462 {
|
|
|
463 if(clonality_method == "boyd"){
|
|
|
464 samples = split(clonalityFrame, clonalityFrame$Sample, drop=T)
|
|
|
465
|
|
|
466 for (sample in samples){
|
|
|
467 res = data.frame(paste=character(0))
|
|
|
468 sample_id = unique(sample$Sample)[[1]]
|
|
|
469 for(replicate in unique(sample$Replicate)){
|
|
|
470 tmp = sample[sample$Replicate == replicate,]
|
|
|
471 clone_table = data.frame(table(tmp$clonaltype))
|
|
|
472 clone_col_name = paste("V", replicate, sep="")
|
|
|
473 colnames(clone_table) = c("paste", clone_col_name)
|
|
|
474 res = merge(res, clone_table, by="paste", all=T)
|
|
|
475 }
|
|
|
476
|
|
|
477 res[is.na(res)] = 0
|
|
|
478 infer.result = infer.clonality(as.matrix(res[,2:ncol(res)]))
|
|
|
479
|
|
|
480 write.table(data.table(infer.result[[12]]), file=paste("lymphclon_clonality_", sample_id, ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=F)
|
|
|
481
|
|
|
482 res$type = rowSums(res[,2:ncol(res)])
|
|
|
483
|
|
|
484 coincidence.table = data.frame(table(res$type))
|
|
|
485 colnames(coincidence.table) = c("Coincidence Type", "Raw Coincidence Freq")
|
|
|
486 write.table(coincidence.table, file=paste("lymphclon_coincidences_", sample_id, ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=T)
|
|
|
487 }
|
|
|
488 } else {
|
|
|
489 write.table(clonalityFrame, "clonalityComplete.csv", sep=",",quote=F,row.names=F,col.names=T)
|
|
|
490
|
|
|
491 clonalFreq = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "clonaltype")])
|
|
|
492 clonalFreqCount = data.frame(data.table(clonalFreq)[, list(Count=.N), by=c("Sample", "Type")])
|
|
|
493 clonalFreqCount$realCount = clonalFreqCount$Type * clonalFreqCount$Count
|
|
|
494 clonalSum = data.frame(data.table(clonalFreqCount)[, list(Reads=sum(realCount)), by=c("Sample")])
|
|
|
495 clonalFreqCount = merge(clonalFreqCount, clonalSum, by.x="Sample", by.y="Sample")
|
|
|
496
|
|
|
497 ct = c('Type\tWeight\n2\t1\n3\t3\n4\t6\n5\t10\n6\t15')
|
|
|
498 tcct = textConnection(ct)
|
|
|
499 CT = read.table(tcct, sep="\t", header=TRUE)
|
|
|
500 close(tcct)
|
|
|
501 clonalFreqCount = merge(clonalFreqCount, CT, by.x="Type", by.y="Type", all.x=T)
|
|
|
502 clonalFreqCount$WeightedCount = clonalFreqCount$Count * clonalFreqCount$Weight
|
|
|
503
|
|
|
504 ReplicateReads = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "Replicate", "clonaltype")])
|
|
|
505 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(Reads=.N), by=c("Sample", "Replicate")])
|
|
|
506 clonalFreqCount$Reads = as.numeric(clonalFreqCount$Reads)
|
|
|
507 ReplicateReads$squared = ReplicateReads$Reads * ReplicateReads$Reads
|
|
|
508
|
|
|
509 ReplicatePrint <- function(dat){
|
|
|
510 write.table(dat[-1], paste("ReplicateReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
511 }
|
|
|
512
|
|
|
513 ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
|
|
|
514 lapply(ReplicateSplit, FUN=ReplicatePrint)
|
|
|
515
|
|
|
516 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(as.numeric(Reads)), ReadsSquaredSum=sum(as.numeric(squared))), by=c("Sample")])
|
|
|
517 clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
518
|
|
|
519 ReplicateSumPrint <- function(dat){
|
|
|
520 write.table(dat[-1], paste("ReplicateSumReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
521 }
|
|
|
522
|
|
|
523 ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
|
|
|
524 lapply(ReplicateSumSplit, FUN=ReplicateSumPrint)
|
|
|
525
|
|
|
526 clonalFreqCountSum = data.frame(data.table(clonalFreqCount)[, list(Numerator=sum(WeightedCount, na.rm=T)), by=c("Sample")])
|
|
|
527 clonalFreqCount = merge(clonalFreqCount, clonalFreqCountSum, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
528 clonalFreqCount$ReadsSum = as.numeric(clonalFreqCount$ReadsSum) #prevent integer overflow
|
|
|
529 clonalFreqCount$Denominator = (((clonalFreqCount$ReadsSum * clonalFreqCount$ReadsSum) - clonalFreqCount$ReadsSquaredSum) / 2)
|
|
|
530 clonalFreqCount$Result = (clonalFreqCount$Numerator + 1) / (clonalFreqCount$Denominator + 1)
|
|
|
531
|
|
|
532 ClonalityScorePrint <- function(dat){
|
|
|
533 write.table(dat$Result, paste("ClonalityScore_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
534 }
|
|
|
535
|
|
|
536 clonalityScore = clonalFreqCount[c("Sample", "Result")]
|
|
|
537 clonalityScore = unique(clonalityScore)
|
|
|
538
|
|
|
539 clonalityScoreSplit = split(clonalityScore, f=clonalityScore[,"Sample"])
|
|
|
540 lapply(clonalityScoreSplit, FUN=ClonalityScorePrint)
|
|
|
541
|
|
|
542 clonalityOverview = clonalFreqCount[c("Sample", "Type", "Count", "Weight", "WeightedCount")]
|
|
|
543
|
|
|
544
|
|
|
545
|
|
|
546 ClonalityOverviewPrint <- function(dat){
|
|
|
547 write.table(dat[-1], paste("ClonalityOverView_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
548 }
|
|
|
549
|
|
|
550 clonalityOverviewSplit = split(clonalityOverview, f=clonalityOverview$Sample)
|
|
|
551 lapply(clonalityOverviewSplit, FUN=ClonalityOverviewPrint)
|
|
|
552 }
|
|
|
553 }
|
|
|
554
|
|
|
555 imgtcolumns = c("X3V.REGION.trimmed.nt.nb","P3V.nt.nb", "N1.REGION.nt.nb", "P5D.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "P3D.nt.nb", "N2.REGION.nt.nb", "P5J.nt.nb", "X5J.REGION.trimmed.nt.nb", "X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb")
|
|
|
556 if(all(imgtcolumns %in% colnames(inputdata)))
|
|
|
557 {
|
|
|
558 print("MEAN P3V.nt.nb:")
|
|
|
559 print(PRODF$P3V.nt.nb)
|
|
|
560 print(mean(PRODF$P3V.nt.nb, na.rm=T))
|
|
|
561 print(head(PRODF))
|
|
|
562 newData = data.frame(data.table(PRODF)[,list(unique=.N,
|
|
|
563 VH.DEL=mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
564 P1=mean(.SD$P3V.nt.nb, na.rm=T),
|
|
|
565 N1=mean(.SD$N1.REGION.nt.nb, na.rm=T),
|
|
|
566 P2=mean(.SD$P5D.nt.nb, na.rm=T),
|
|
|
567 DEL.DH=mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
568 DH.DEL=mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T),
|
|
|
569 P3=mean(.SD$P3D.nt.nb, na.rm=T),
|
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570 N2=mean(.SD$N2.REGION.nt.nb, na.rm=T),
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571 P4=mean(.SD$P5J.nt.nb, na.rm=T),
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572 DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
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573 Total.Del=( mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T) +
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574 mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T) +
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575 mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T) +
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576 mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T)),
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577
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578 Total.N=( mean(.SD$N1.REGION.nt.nb, na.rm=T) +
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579 mean(.SD$N2.REGION.nt.nb, na.rm=T)),
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580
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581 Total.P=( mean(.SD$P3V.nt.nb, na.rm=T) +
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582 mean(.SD$P5D.nt.nb, na.rm=T) +
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583 mean(.SD$P3D.nt.nb, na.rm=T) +
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584 mean(.SD$P5J.nt.nb, na.rm=T))),
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585 by=c("Sample")])
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586 write.table(newData, "junctionAnalysisProd.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F)
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587
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588 newData = data.frame(data.table(UNPROD)[,list(unique=.N,
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589 VH.DEL=mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
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590 P1=mean(.SD$P3V.nt.nb, na.rm=T),
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591 N1=mean(.SD$N1.REGION.nt.nb, na.rm=T),
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592 P2=mean(.SD$P5D.nt.nb, na.rm=T),
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593 DEL.DH=mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T),
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594 DH.DEL=mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T),
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595 P3=mean(.SD$P3D.nt.nb, na.rm=T),
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596 N2=mean(.SD$N2.REGION.nt.nb, na.rm=T),
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597 P4=mean(.SD$P5J.nt.nb, na.rm=T),
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598 DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
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599 Total.Del=( mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T) +
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600 mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T) +
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601 mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T) +
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602 mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T)),
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603
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604 Total.N=( mean(.SD$N1.REGION.nt.nb, na.rm=T) +
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605 mean(.SD$N2.REGION.nt.nb, na.rm=T)),
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606
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607 Total.P=( mean(.SD$P3V.nt.nb, na.rm=T) +
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608 mean(.SD$P5D.nt.nb, na.rm=T) +
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609 mean(.SD$P3D.nt.nb, na.rm=T) +
|
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610 mean(.SD$P5J.nt.nb, na.rm=T))),
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611 by=c("Sample")])
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612 write.table(newData, "junctionAnalysisUnProd.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F)
|
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|
613 }
|
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614
|
|
|
615 # ---------------------- AA composition in CDR3 ----------------------
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616
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617 AACDR3 = PRODF[,c("Sample", "CDR3.Seq")]
|
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618
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619 TotalPerSample = data.frame(data.table(AACDR3)[, list(total=sum(nchar(as.character(.SD$CDR3.Seq)))), by=Sample])
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620
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|
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621 AAfreq = list()
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622
|
|
|
623 for(i in 1:nrow(TotalPerSample)){
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|
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624 sample = TotalPerSample$Sample[i]
|
|
|
625 AAfreq[[i]] = data.frame(table(unlist(strsplit(as.character(AACDR3[AACDR3$Sample == sample,c("CDR3.Seq")]), ""))))
|
|
|
626 AAfreq[[i]]$Sample = sample
|
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|
627 }
|
|
|
628
|
|
|
629 AAfreq = ldply(AAfreq, data.frame)
|
|
|
630 AAfreq = merge(AAfreq, TotalPerSample, by="Sample", all.x = T)
|
|
|
631 AAfreq$freq_perc = as.numeric(AAfreq$Freq / AAfreq$total * 100)
|
|
|
632
|
|
|
633
|
|
|
634 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")
|
|
|
635 AAfreq = merge(AAfreq, AAorder, by.x='Var1', by.y='AA', all.x=TRUE)
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|
|
636
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637 AAfreq = AAfreq[!is.na(AAfreq$order.aa),]
|
|
|
638
|
|
|
639 AAfreqplot = ggplot(AAfreq)
|
|
|
640 AAfreqplot = AAfreqplot + geom_bar(aes( x=factor(reorder(Var1, order.aa)), y = freq_perc, fill = Sample), stat='identity', position='dodge' )
|
|
|
641 AAfreqplot = AAfreqplot + annotate("rect", xmin = 0.5, xmax = 2.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2)
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|
|
642 AAfreqplot = AAfreqplot + annotate("rect", xmin = 3.5, xmax = 4.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2)
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643 AAfreqplot = AAfreqplot + annotate("rect", xmin = 5.5, xmax = 6.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2)
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644 AAfreqplot = AAfreqplot + annotate("rect", xmin = 6.5, xmax = 7.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2)
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645 AAfreqplot = AAfreqplot + ggtitle("Amino Acid Composition in the CDR3") + xlab("Amino Acid, from Hydrophilic (left) to Hydrophobic (right)") + ylab("Percentage")
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|
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646
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|
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647 png("AAComposition.png",width = 1280, height = 720)
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648 AAfreqplot
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649 dev.off()
|
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|
650 write.table(AAfreq, "AAComposition.csv" , sep=",",quote=F,na="-",row.names=F,col.names=T)
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651
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652
|
