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0
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1 #options( show.error.messages=F, error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
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2
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3 args <- commandArgs(trailingOnly = TRUE)
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4
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5 inFile = args[1]
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6 outFile = args[2]
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7 outDir = args[3]
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8 clonalType = args[4]
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10
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9 species = args[5]
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10 locus = args[6]
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11 selection = args[7]
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12
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13
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0
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14
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15 if (!("gridExtra" %in% rownames(installed.packages()))) {
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16 install.packages("gridExtra", repos="http://cran.xl-mirror.nl/")
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17 }
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18 library(gridExtra)
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19 if (!("ggplot2" %in% rownames(installed.packages()))) {
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20 install.packages("ggplot2", repos="http://cran.xl-mirror.nl/")
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21 }
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22 require(ggplot2)
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23 if (!("plyr" %in% rownames(installed.packages()))) {
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24 install.packages("plyr", repos="http://cran.xl-mirror.nl/")
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25 }
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26 require(plyr)
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27
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28 if (!("data.table" %in% rownames(installed.packages()))) {
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29 install.packages("data.table", repos="http://cran.xl-mirror.nl/")
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30 }
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31 library(data.table)
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32
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6
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33 if (!("reshape2" %in% rownames(installed.packages()))) {
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34 install.packages("reshape2", repos="http://cran.xl-mirror.nl/")
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35 }
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36 library(reshape2)
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37
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0
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38
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39 test = read.table(inFile, sep="\t", header=TRUE, fill=T, comment.char="")
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40
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41 test = test[test$Sample != "",]
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42
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43 test$Top.V.Gene = gsub("[*]([0-9]+)", "", test$Top.V.Gene)
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44 test$Top.D.Gene = gsub("[*]([0-9]+)", "", test$Top.D.Gene)
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45 test$Top.J.Gene = gsub("[*]([0-9]+)", "", test$Top.J.Gene)
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46
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47 #test$VDJCDR3 = do.call(paste, c(test[c("Top.V.Gene", "Top.D.Gene", "Top.J.Gene","CDR3.Seq.DNA")], sep = ":"))
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48 test$VDJCDR3 = do.call(paste, c(test[unlist(strsplit(clonalType, ","))], sep = ":"))
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49
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50 PROD = test[test$VDJ.Frame != "In-frame with stop codon" & test$VDJ.Frame != "Out-of-frame" & test$CDR3.Found.How != "NOT_FOUND" , ]
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51 if("Functionality" %in% colnames(test)) {
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52 PROD = test[test$Functionality == "productive" | test$Functionality == "productive (see comment)", ]
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53 }
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54
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55 NONPROD = test[test$VDJ.Frame == "In-frame with stop codon" | test$VDJ.Frame == "Out-of-frame" | test$CDR3.Found.How == "NOT_FOUND" , ]
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56
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57 #PRODF = PROD[ -1]
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58
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59 PRODF = PROD
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60
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61 #PRODF = unique(PRODF)
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10
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62
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63
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64
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65 if(selection == "unique"){
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66 PRODF = PRODF[!duplicated(PRODF$VDJCDR3), ]
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67 }
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0
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68
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69 PRODFV = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Sample", "Top.V.Gene")])
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70 PRODFV$Length = as.numeric(PRODFV$Length)
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71 Total = 0
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72 Total = ddply(PRODFV, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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73 PRODFV = merge(PRODFV, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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74 PRODFV = ddply(PRODFV, c("Sample", "Top.V.Gene"), summarise, relFreq= (Length*100 / Total))
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75
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76 PRODFD = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Sample", "Top.D.Gene")])
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77 PRODFD$Length = as.numeric(PRODFD$Length)
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78 Total = 0
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79 Total = ddply(PRODFD, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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80 PRODFD = merge(PRODFD, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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81 PRODFD = ddply(PRODFD, c("Sample", "Top.D.Gene"), summarise, relFreq= (Length*100 / Total))
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82
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83 PRODFJ = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Sample", "Top.J.Gene")])
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84 PRODFJ$Length = as.numeric(PRODFJ$Length)
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85 Total = 0
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86 Total = ddply(PRODFJ, .(Sample), function(x) data.frame(Total = sum(x$Length)))
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87 PRODFJ = merge(PRODFJ, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
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88 PRODFJ = ddply(PRODFJ, c("Sample", "Top.J.Gene"), summarise, relFreq= (Length*100 / Total))
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89
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10
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90 V = c("v.name\tchr.orderV")
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91 D = c("v.name\tchr.orderD")
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92 J = c("v.name\tchr.orderJ")
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93
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94 if(species == "human"){
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95 if(locus == "igh"){
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96 V = c("v.name\tchr.orderV\nIGHV3-74\t1\nIGHV3-73\t2\nIGHV3-72\t3\nIGHV2-70\t4\nIGHV1-69D\t5\nIGHV1-69-2\t6\nIGHV2-70D\t7\nIGHV1-69\t8\nIGHV3-66\t9\nIGHV3-64\t10\nIGHV4-61\t11\nIGHV4-59\t12\nIGHV1-58\t13\nIGHV3-53\t14\nIGHV5-51\t15\nIGHV3-49\t16\nIGHV3-48\t17\nIGHV1-46\t18\nIGHV1-45\t19\nIGHV3-43\t20\nIGHV4-39\t21\nIGHV3-43D\t22\nIGHV4-38-2\t23\nIGHV4-34\t24\nIGHV3-33\t25\nIGHV4-31\t26\nIGHV3-30-5\t27\nIGHV4-30-4\t28\nIGHV3-30-3\t29\nIGHV4-30-2\t30\nIGHV4-30-1\t31\nIGHV3-30\t32\nIGHV4-28\t33\nIGHV2-26\t34\nIGHV1-24\t35\nIGHV3-23D\t36\nIGHV3-23\t37\nIGHV3-21\t38\nIGHV3-20\t39\nIGHV1-18\t40\nIGHV3-15\t41\nIGHV3-13\t42\nIGHV3-11\t43\nIGHV5-10-1\t44\nIGHV3-9\t45\nIGHV1-8\t46\nIGHV3-64D\t47\nIGHV3-7\t48\nIGHV2-5\t49\nIGHV7-4-1\t50\nIGHV4-4\t51\nIGHV1-3\t52\nIGHV1-2\t53\nIGHV6-1\t54")
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97 D = c("v.name\tchr.orderD\nIGHD1-7\t1\nIGHD2-8\t2\nIGHD3-9\t3\nIGHD3-10\t4\nIGHD5-12\t5\nIGHD6-13\t6\nIGHD2-15\t7\nIGHD3-16\t8\nIGHD4-17\t9\nIGHD5-18\t10\nIGHD6-19\t11\nIGHD1-20\t12\nIGHD2-21\t13\nIGHD3-22\t14\nIGHD5-24\t15\nIGHD6-25\t16\nIGHD1-26\t17\nIGHD7-27\t18")
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98 J = c("v.name\tchr.orderJ\nIGHJ1\t1\nIGHJ2\t2\nIGHJ3\t3\nIGHJ4\t4\nIGHJ5\t5\nIGHJ6\t6")
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99 } else if (locus == "igk"){
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100 V = c("v.name\tchr.orderV\nIGKV3D-7\t1\nIGKV1D-8\t2\nIGKV1D-43\t3\nIGKV3D-11\t4\nIGKV1D-12\t5\nIGKV1D-13\t6\nIGKV3D-15\t7\nIGKV1D-16\t8\nIGKV1D-17\t9\nIGKV3D-20\t10\nIGKV2D-26\t11\nIGKV2D-28\t12\nIGKV2D-29\t13\nIGKV2D-30\t14\nIGKV1D-33\t15\nIGKV1D-39\t16\nIGKV2D-40\t17\nIGKV2-40\t18\nIGKV1-39\t19\nIGKV1-33\t20\nIGKV2-30\t21\nIGKV2-29\t22\nIGKV2-28\t23\nIGKV1-27\t24\nIGKV2-24\t25\nIGKV3-20\t26\nIGKV1-17\t27\nIGKV1-16\t28\nIGKV3-15\t29\nIGKV1-13\t30\nIGKV1-12\t31\nIGKV3-11\t32\nIGKV1-9\t33\nIGKV1-8\t34\nIGKV1-6\t35\nIGKV1-5\t36\nIGKV5-2\t37\nIGKV4-1\t38")
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101 D = c("v.name\tchr.orderD\n")
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102 J = c("v.name\tchr.orderJ\nIGKJ1\t1\nIGKJ2\t2\nIGKJ3\t3\nIGKJ4\t4\nIGKJ5\t5")
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103 } else if (locus == "igl"){
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104 V = c("v.name\tchr.orderV\nIGLV4-69\t1\nIGLV8-61\t2\nIGLV4-60\t3\nIGLV6-57\t4\nIGLV5-52\t5\nIGLV1-51\t6\nIGLV9-49\t7\nIGLV1-47\t8\nIGLV7-46\t9\nIGLV5-45\t10\nIGLV1-44\t11\nIGLV7-43\t12\nIGLV1-41\t13\nIGLV1-40\t14\nIGLV5-39\t15\nIGLV5-37\t16\nIGLV1-36\t17\nIGLV3-27\t18\nIGLV3-25\t19\nIGLV2-23\t20\nIGLV3-22\t21\nIGLV3-21\t22\nIGLV3-19\t23\nIGLV2-18\t24\nIGLV3-16\t25\nIGLV2-14\t26\nIGLV3-12\t27\nIGLV2-11\t28\nIGLV3-10\t29\nIGLV3-9\t30\nIGLV2-8\t31\nIGLV4-3\t32\nIGLV3-1\t33")
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105 D = c("v.name\tchr.orderD\n")
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106 J = c("v.name\tchr.orderJ\nIGLJ1\t1\nIGLJ2\t2\nIGLJ3\t3\nIGLJ6\t4\nIGLJ7\t5")
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107 }
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108 } else if (species == "mouse"){
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109 if(locus == "igh"){
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110 cat("mouse igh not yet implemented")
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111 } else if (locus == "igk"){
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112 cat("mouse igk not yet implemented")
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113 } else if (locus == "igl"){
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114 cat("mouse igl not yet implemented")
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115 }
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116 }
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117
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118 useD = TRUE
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119 if(species == "human" && (locus == "igk" || locus == "igl")){
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120 useD = FALSE
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121 }
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122
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0
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123 tcV = textConnection(V)
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124 Vchain = read.table(tcV, sep="\t", header=TRUE)
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125 PRODFV = merge(PRODFV, Vchain, by.x='Top.V.Gene', by.y='v.name', all.x=TRUE)
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126 close(tcV)
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127
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128 tcD = textConnection(D)
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129 Dchain = read.table(tcD, sep="\t", header=TRUE)
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130 PRODFD = merge(PRODFD, Dchain, by.x='Top.D.Gene', by.y='v.name', all.x=TRUE)
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131 close(tcD)
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132
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133 tcJ = textConnection(J)
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134 Jchain = read.table(tcJ, sep="\t", header=TRUE)
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135 PRODFJ = merge(PRODFJ, Jchain, by.x='Top.J.Gene', by.y='v.name', all.x=TRUE)
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136 close(tcJ)
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137
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138 setwd(outDir)
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139
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6
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140 write.table(PRODF, "allUnique.csv", sep=",",quote=F,row.names=F,col.names=T)
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0
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141
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142 pV = ggplot(PRODFV)
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143 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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144 pV = pV + xlab("Summary of V gene") + ylab("Frequency") + ggtitle("Relative frequency of V gene usage")
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6
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145 write.table(x=PRODFV, file="VFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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0
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146
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147 png("VPlot.png",width = 1280, height = 720)
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148 pV
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149 dev.off();
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150
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151 pD = ggplot(PRODFD)
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152 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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153 pD = pD + xlab("Summary of D gene") + ylab("Frequency") + ggtitle("Relative frequency of D gene usage")
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6
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154 write.table(x=PRODFD, file="DFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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0
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155
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156 png("DPlot.png",width = 800, height = 600)
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157 pD
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158 dev.off();
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159
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160 pJ = ggplot(PRODFJ)
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161 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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162 pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage")
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6
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163 write.table(x=PRODFJ, file="JFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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0
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164
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165 png("JPlot.png",width = 800, height = 600)
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166 pJ
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167 dev.off();
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168
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6
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169 VGenes = PRODF[,c("Sample", "Top.V.Gene")]
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170 VGenes$Top.V.Gene = gsub("-.*", "", VGenes$Top.V.Gene)
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171 VGenes = data.frame(data.table(VGenes)[, list(Count=.N), by=c("Sample", "Top.V.Gene")])
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172 TotalPerSample = data.frame(data.table(VGenes)[, list(total=sum(.SD$Count)), by=Sample])
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173 VGenes = merge(VGenes, TotalPerSample, by="Sample")
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174 VGenes$Frequency = VGenes$Count * 100 / VGenes$total
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175 VPlot = ggplot(VGenes)
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8
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176 VPlot = VPlot + geom_bar(aes( x = Top.V.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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177 ggtitle("Distribution of V gene families") +
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178 ylab("Percentage of sequences")
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6
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179 png("VFPlot.png")
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180 VPlot
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181 dev.off();
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182 write.table(x=VGenes, file="VFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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183
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184 DGenes = PRODF[,c("Sample", "Top.D.Gene")]
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185 DGenes$Top.D.Gene = gsub("-.*", "", DGenes$Top.D.Gene)
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186 DGenes = data.frame(data.table(DGenes)[, list(Count=.N), by=c("Sample", "Top.D.Gene")])
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187 TotalPerSample = data.frame(data.table(DGenes)[, list(total=sum(.SD$Count)), by=Sample])
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188 DGenes = merge(DGenes, TotalPerSample, by="Sample")
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189 DGenes$Frequency = DGenes$Count * 100 / DGenes$total
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190 DPlot = ggplot(DGenes)
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8
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191 DPlot = DPlot + geom_bar(aes( x = Top.D.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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192 ggtitle("Distribution of D gene families") +
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193 ylab("Percentage of sequences")
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6
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194 png("DFPlot.png")
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195 DPlot
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196 dev.off();
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197 write.table(x=DGenes, file="DFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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198
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199 JGenes = PRODF[,c("Sample", "Top.J.Gene")]
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200 JGenes$Top.J.Gene = gsub("-.*", "", JGenes$Top.J.Gene)
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201 JGenes = data.frame(data.table(JGenes)[, list(Count=.N), by=c("Sample", "Top.J.Gene")])
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202 TotalPerSample = data.frame(data.table(JGenes)[, list(total=sum(.SD$Count)), by=Sample])
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203 JGenes = merge(JGenes, TotalPerSample, by="Sample")
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204 JGenes$Frequency = JGenes$Count * 100 / JGenes$total
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205 JPlot = ggplot(JGenes)
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8
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206 JPlot = JPlot + geom_bar(aes( x = Top.J.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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207 ggtitle("Distribution of J gene families") +
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208 ylab("Percentage of sequences")
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6
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209 png("JFPlot.png")
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210 JPlot
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211 dev.off();
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212 write.table(x=JGenes, file="JFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
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213
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8
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214 CDR3Length = data.frame(data.table(PRODF)[, list(Count=.N), by=c("Sample", "CDR3.Length.DNA")])
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215 TotalPerSample = data.frame(data.table(CDR3Length)[, list(total=sum(.SD$Count)), by=Sample])
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216 CDR3Length = merge(CDR3Length, TotalPerSample, by="Sample")
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217 CDR3Length$Frequency = CDR3Length$Count * 100 / CDR3Length$total
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218 CDR3LengthPlot = ggplot(CDR3Length)
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219 CDR3LengthPlot = CDR3LengthPlot + geom_bar(aes( x = CDR3.Length.DNA, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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220 ggtitle("Length distribution of CDR3") +
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221 xlab("CDR3 Length") +
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222 ylab("Percentage of sequences")
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223 png("CDR3LengthPlot.png",width = 1280, height = 720)
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224 CDR3LengthPlot
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225 dev.off()
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226 write.table(x=CDR3Length, file="CDR3LengthPlot.csv", sep=",",quote=F,row.names=F,col.names=T)
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227
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0
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228 revVchain = Vchain
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229 revDchain = Dchain
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230 revVchain$chr.orderV = rev(revVchain$chr.orderV)
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231 revDchain$chr.orderD = rev(revDchain$chr.orderD)
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232
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233 plotVD <- function(dat){
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234 if(length(dat[,1]) == 0){
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235 return()
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236 }
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237 img = ggplot() +
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238 geom_tile(data=dat, aes(x=factor(reorder(Top.D.Gene, chr.orderD)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
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239 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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240 scale_fill_gradient(low="gold", high="blue", na.value="white") +
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241 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
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242 xlab("D genes") +
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243 ylab("V Genes")
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244
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245 png(paste("HeatmapVD_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Dchain$v.name)), height=100+(15*length(Vchain$v.name)))
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246 print(img)
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6
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247
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0
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248 dev.off()
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6
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249 write.table(x=acast(dat, Top.V.Gene~Top.D.Gene, value.var="Length"), file=paste("HeatmapVD_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
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0
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250 }
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251
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252 VandDCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.D.Gene", "Sample")])
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253
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254 VandDCount$l = log(VandDCount$Length)
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255 maxVD = data.frame(data.table(VandDCount)[, list(max=max(l)), by=c("Sample")])
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256 VandDCount = merge(VandDCount, maxVD, by.x="Sample", by.y="Sample", all.x=T)
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257 VandDCount$relLength = VandDCount$l / VandDCount$max
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258
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259 cartegianProductVD = expand.grid(Top.V.Gene = Vchain$v.name, Top.D.Gene = Dchain$v.name, Sample = unique(test$Sample))
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260
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261 completeVD = merge(VandDCount, cartegianProductVD, all.y=TRUE)
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262 completeVD = merge(completeVD, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
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263 completeVD = merge(completeVD, Dchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
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264 VDList = split(completeVD, f=completeVD[,"Sample"])
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265
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266 lapply(VDList, FUN=plotVD)
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267
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268 plotVJ <- function(dat){
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269 if(length(dat[,1]) == 0){
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270 return()
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271 }
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272 img = ggplot() +
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273 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) +
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274 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
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275 scale_fill_gradient(low="gold", high="blue", na.value="white") +
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276 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
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277 xlab("J genes") +
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278 ylab("V Genes")
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279
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280 png(paste("HeatmapVJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Vchain$v.name)))
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281 print(img)
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282 dev.off()
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6
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283 write.table(x=acast(dat, Top.V.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapVJ_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
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0
|
284 }
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285
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286 VandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.J.Gene", "Sample")])
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287
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288 VandJCount$l = log(VandJCount$Length)
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289 maxVJ = data.frame(data.table(VandJCount)[, list(max=max(l)), by=c("Sample")])
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290 VandJCount = merge(VandJCount, maxVJ, by.x="Sample", by.y="Sample", all.x=T)
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291 VandJCount$relLength = VandJCount$l / VandJCount$max
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292
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293 cartegianProductVJ = expand.grid(Top.V.Gene = Vchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(test$Sample))
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294
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295 completeVJ = merge(VandJCount, cartegianProductVJ, all.y=TRUE)
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296 completeVJ = merge(completeVJ, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
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297 completeVJ = merge(completeVJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
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298 VJList = split(completeVJ, f=completeVJ[,"Sample"])
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299 lapply(VJList, FUN=plotVJ)
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300
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301 plotDJ <- function(dat){
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302 if(length(dat[,1]) == 0){
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303 return()
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304 }
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305 img = ggplot() +
|
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306 geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.D.Gene, chr.orderD)), fill=relLength)) +
|
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307 theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
|
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308 scale_fill_gradient(low="gold", high="blue", na.value="white") +
|
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309 ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) +
|
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310 xlab("J genes") +
|
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|
311 ylab("D Genes")
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|
312
|
|
|
313 png(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Dchain$v.name)))
|
|
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314 print(img)
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|
|
315 dev.off()
|
|
6
|
316 write.table(x=acast(dat, Top.D.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapDJ_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
|
|
0
|
317 }
|
|
|
318
|
|
10
|
319
|
|
0
|
320 DandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.D.Gene", "Top.J.Gene", "Sample")])
|
|
|
321
|
|
|
322 DandJCount$l = log(DandJCount$Length)
|
|
|
323 maxDJ = data.frame(data.table(DandJCount)[, list(max=max(l)), by=c("Sample")])
|
|
|
324 DandJCount = merge(DandJCount, maxDJ, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
325 DandJCount$relLength = DandJCount$l / DandJCount$max
|
|
|
326
|
|
|
327 cartegianProductDJ = expand.grid(Top.D.Gene = Dchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(test$Sample))
|
|
|
328
|
|
|
329 completeDJ = merge(DandJCount, cartegianProductDJ, all.y=TRUE)
|
|
|
330 completeDJ = merge(completeDJ, revDchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
|
|
|
331 completeDJ = merge(completeDJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
|
|
|
332 DJList = split(completeDJ, f=completeDJ[,"Sample"])
|
|
|
333 lapply(DJList, FUN=plotDJ)
|
|
|
334
|
|
|
335 sampleFile <- file("samples.txt")
|
|
|
336 un = unique(test$Sample)
|
|
|
337 un = paste(un, sep="\n")
|
|
|
338 writeLines(un, sampleFile)
|
|
|
339 close(sampleFile)
|
|
|
340
|
|
|
341
|
|
|
342 if("Replicate" %in% colnames(test))
|
|
|
343 {
|
|
|
344 clonalityFrame = PROD
|
|
|
345 clonalityFrame$ReplicateConcat = do.call(paste, c(clonalityFrame[c("VDJCDR3", "Sample", "Replicate")], sep = ":"))
|
|
|
346 clonalityFrame = clonalityFrame[!duplicated(clonalityFrame$ReplicateConcat), ]
|
|
6
|
347 write.table(clonalityFrame, "clonalityComplete.csv", sep=",",quote=F,row.names=F,col.names=T)
|
|
0
|
348
|
|
|
349 ClonalitySampleReplicatePrint <- function(dat){
|
|
6
|
350 write.table(dat, paste("clonality_", unique(dat$Sample) , "_", unique(dat$Replicate), ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=T)
|
|
0
|
351 }
|
|
|
352
|
|
|
353 clonalityFrameSplit = split(clonalityFrame, f=clonalityFrame[,c("Sample", "Replicate")])
|
|
5
|
354 #lapply(clonalityFrameSplit, FUN=ClonalitySampleReplicatePrint)
|
|
0
|
355
|
|
|
356 ClonalitySamplePrint <- function(dat){
|
|
6
|
357 write.table(dat, paste("clonality_", unique(dat$Sample) , ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=T)
|
|
0
|
358 }
|
|
|
359
|
|
|
360 clonalityFrameSplit = split(clonalityFrame, f=clonalityFrame[,"Sample"])
|
|
5
|
361 #lapply(clonalityFrameSplit, FUN=ClonalitySamplePrint)
|
|
0
|
362
|
|
|
363 clonalFreq = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "VDJCDR3")])
|
|
|
364 clonalFreqCount = data.frame(data.table(clonalFreq)[, list(Count=.N), by=c("Sample", "Type")])
|
|
|
365 clonalFreqCount$realCount = clonalFreqCount$Type * clonalFreqCount$Count
|
|
|
366 clonalSum = data.frame(data.table(clonalFreqCount)[, list(Reads=sum(realCount)), by=c("Sample")])
|
|
|
367 clonalFreqCount = merge(clonalFreqCount, clonalSum, by.x="Sample", by.y="Sample")
|
|
|
368
|
|
|
369 ct = c('Type\tWeight\n2\t1\n3\t3\n4\t6\n5\t10\n6\t15')
|
|
|
370 tcct = textConnection(ct)
|
|
|
371 CT = read.table(tcct, sep="\t", header=TRUE)
|
|
|
372 close(tcct)
|
|
|
373 clonalFreqCount = merge(clonalFreqCount, CT, by.x="Type", by.y="Type", all.x=T)
|
|
|
374 clonalFreqCount$WeightedCount = clonalFreqCount$Count * clonalFreqCount$Weight
|
|
|
375
|
|
|
376 ReplicateReads = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "Replicate", "VDJCDR3")])
|
|
|
377 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(Reads=.N), by=c("Sample", "Replicate")])
|
|
|
378 clonalFreqCount$Reads = as.numeric(clonalFreqCount$Reads)
|
|
|
379 ReplicateReads$squared = ReplicateReads$Reads * ReplicateReads$Reads
|
|
|
380
|
|
|
381 ReplicatePrint <- function(dat){
|
|
|
382 write.table(dat[-1], paste("ReplicateReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
383 }
|
|
|
384
|
|
|
385 ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
|
|
|
386 lapply(ReplicateSplit, FUN=ReplicatePrint)
|
|
|
387
|
|
|
388 ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(Reads), ReadsSquaredSum=sum(squared)), by=c("Sample")])
|
|
|
389 clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
390
|
|
|
391
|
|
|
392 ReplicateSumPrint <- function(dat){
|
|
|
393 write.table(dat[-1], paste("ReplicateSumReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
394 }
|
|
|
395
|
|
|
396 ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
|
|
|
397 lapply(ReplicateSumSplit, FUN=ReplicateSumPrint)
|
|
|
398
|
|
|
399 clonalFreqCountSum = data.frame(data.table(clonalFreqCount)[, list(Numerator=sum(WeightedCount, na.rm=T)), by=c("Sample")])
|
|
|
400 clonalFreqCount = merge(clonalFreqCount, clonalFreqCountSum, by.x="Sample", by.y="Sample", all.x=T)
|
|
|
401 clonalFreqCount$ReadsSum = as.numeric(clonalFreqCount$ReadsSum) #prevent integer overflow
|
|
|
402 clonalFreqCount$Denominator = (((clonalFreqCount$ReadsSum * clonalFreqCount$ReadsSum) - clonalFreqCount$ReadsSquaredSum) / 2)
|
|
|
403 clonalFreqCount$Result = (clonalFreqCount$Numerator + 1) / (clonalFreqCount$Denominator + 1)
|
|
|
404
|
|
|
405 ClonalityScorePrint <- function(dat){
|
|
|
406 write.table(dat$Result, paste("ClonalityScore_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
407 }
|
|
|
408
|
|
|
409 clonalityScore = clonalFreqCount[c("Sample", "Result")]
|
|
|
410 clonalityScore = unique(clonalityScore)
|
|
|
411
|
|
|
412 clonalityScoreSplit = split(clonalityScore, f=clonalityScore[,"Sample"])
|
|
|
413 lapply(clonalityScoreSplit, FUN=ClonalityScorePrint)
|
|
|
414
|
|
|
415 clonalityOverview = clonalFreqCount[c("Sample", "Type", "Count", "Weight", "WeightedCount")]
|
|
|
416
|
|
|
417
|
|
|
418
|
|
|
419 ClonalityOverviewPrint <- function(dat){
|
|
|
420 write.table(dat[-1], paste("ClonalityOverView_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
|
421 }
|
|
|
422
|
|
|
423 clonalityOverviewSplit = split(clonalityOverview, f=clonalityOverview$Sample)
|
|
|
424 lapply(clonalityOverviewSplit, FUN=ClonalityOverviewPrint)
|
|
|
425 }
|
|
1
|
426
|
|
|
427 if("Functionality" %in% colnames(test))
|
|
|
428 {
|
|
|
429 newData = data.frame(data.table(PROD)[,list(unique=.N,
|
|
5
|
430 VH.DEL=mean(X3V.REGION.trimmed.nt.nb),
|
|
|
431 P1=mean(P3V.nt.nb),
|
|
|
432 N1=mean(N1.REGION.nt.nb),
|
|
|
433 P2=mean(P5D.nt.nb),
|
|
|
434 DEL.DH=mean(X5D.REGION.trimmed.nt.nb),
|
|
|
435 DH.DEL=mean(X3D.REGION.trimmed.nt.nb),
|
|
|
436 P3=mean(P3D.nt.nb),
|
|
|
437 N2=mean(N2.REGION.nt.nb),
|
|
|
438 P4=mean(P5J.nt.nb),
|
|
|
439 DEL.JH=mean(X5J.REGION.trimmed.nt.nb),
|
|
|
440 Total.Del=( mean(X3V.REGION.trimmed.nt.nb) +
|
|
|
441 mean(X5D.REGION.trimmed.nt.nb) +
|
|
|
442 mean(X3D.REGION.trimmed.nt.nb) +
|
|
|
443 mean(X5J.REGION.trimmed.nt.nb)),
|
|
|
444
|
|
|
445 Total.N=( mean(N1.REGION.nt.nb) +
|
|
|
446 mean(N2.REGION.nt.nb)),
|
|
|
447
|
|
|
448 Total.P=( mean(P3V.nt.nb) +
|
|
|
449 mean(P5D.nt.nb) +
|
|
|
450 mean(P3D.nt.nb) +
|
|
|
451 mean(P5J.nt.nb))),
|
|
|
452 by=c("Sample")])
|
|
1
|
453 write.table(newData, "junctionAnalysis.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F)
|
|
5
|
454 }
|
