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1 library(ggplot2)
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2 library(reshape2)
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3 library(scales)
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4
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5 args <- commandArgs(trailingOnly = TRUE)
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6
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7 input.file = args[1] #the data that's get turned into the "SHM overview" table in the html report "data_sum.txt"
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8
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9 plot1.path = args[2]
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10 plot1.png = paste(plot1.path, ".png", sep="")
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11 plot1.txt = paste(plot1.path, ".txt", sep="")
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12
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13 plot2.path = args[3]
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14 plot2.png = paste(plot2.path, ".png", sep="")
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15 plot2.txt = paste(plot2.path, ".txt", sep="")
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16
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17 plot3.path = args[4]
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18 plot3.png = paste(plot3.path, ".png", sep="")
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19 plot3.txt = paste(plot3.path, ".txt", sep="")
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20
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21 dat = read.table(input.file, header=F, sep=",", quote="", stringsAsFactors=F, fill=T, row.names=1)
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22
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23
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24
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25 classes = c("ca", "ca1", "ca2", "cg", "cg1", "cg2", "cg3", "cg4", "cm")
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26 xyz = c("x", "y", "z")
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27 new.names = c(paste(rep(classes, each=3), xyz, sep="."), paste("un", xyz, sep="."), paste("all", xyz, sep="."))
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28
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29 names(dat) = new.names
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30
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31 dat["RGYW.WRCY",] = colSums(dat[c(13,14),])
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32 dat["TW.WA",] = colSums(dat[c(15,16),])
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33
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34 data1 = dat[c("RGYW.WRCY", "TW.WA"),]
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35
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36 data1 = data1[,names(data1)[grepl(".z", names(data1))]]
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37 names(data1) = gsub("\\..*", "", names(data1))
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38
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39 data1 = melt(t(data1))
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40
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41 names(data1) = c("Class", "Type", "value")
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42
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43 write.table(data1, plot1.txt, quote=F, sep="\t", na="", row.names=F, col.names=T)
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44
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45 p = ggplot(data1, aes(Class, value)) + geom_bar(aes(fill=Type), stat="identity", position="dodge") + ylab("% of mutations") + guides(fill=guide_legend(title=NULL))
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46 png(filename=plot1.png)
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47 print(p)
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48 dev.off()
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49
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50 data2 = dat[5:8,]
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51
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52 data2["sum",] = colSums(data2)
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53
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54 data2 = data2[,names(data2)[grepl("\\.x", names(data2))]]
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55 names(data2) = gsub(".x", "", names(data2))
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56
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57 data2["A/T",] = round(colSums(data2[3:4,]) / data2["sum",] * 100, 1)
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58 data2["A/T",is.nan(unlist(data2["A/T",]))] = 0
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59
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60 data2["G/C transversions",] = round(data2[2,] / data2["sum",] * 100, 1)
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61 data2["G/C transitions",] = round(data2[1,] / data2["sum",] * 100, 1)
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62
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63
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64 data2["G/C transversions",is.nan(unlist(data2["G/C transversions",]))] = 0
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65 data2["G/C transversions",is.infinite(unlist(data2["G/C transversions",]))] = 0
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66 data2["G/C transitions",is.nan(unlist(data2["G/C transitions",]))] = 0
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67 data2["G/C transitions",is.infinite(unlist(data2["G/C transitions",]))] = 0
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68
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69 data2 = melt(t(data2[6:8,]))
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70
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71 names(data2) = c("Class", "Type", "value")
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72
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73 write.table(data2, plot2.txt, quote=F, sep="\t", na="", row.names=F, col.names=T)
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74
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75 p = ggplot(data2, aes(x=Class, y=value, fill=Type)) + geom_bar(position="fill", stat="identity") + scale_y_continuous(labels=percent_format()) + guides(fill=guide_legend(title=NULL)) + ylab("% of mutations")
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76 png(filename=plot2.png)
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77 print(p)
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78 dev.off()
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79
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80 data3 = dat[c(5, 6, 8, 17:20),]
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81 data3 = data3[,names(data3)[grepl("\\.x", names(data3))]]
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82 names(data3) = gsub(".x", "", names(data3))
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83
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84 data3["G/C transitions",] = round(data3[1,] / (data3[5,] + data3[7,]) * 100, 1)
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85
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86 data3["G/C transversions",] = round(data3[2,] / (data3[5,] + data3[7,]) * 100, 1)
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87
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88 data3["A/T",] = round(data3[3,] / (data3[4,] + data3[6,]) * 100, 1)
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89
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90 data3["G/C transitions",is.nan(unlist(data3["G/C transitions",]))] = 0
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91 data3["G/C transitions",is.infinite(unlist(data3["G/C transitions",]))] = 0
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92
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93 data3["G/C transversions",is.nan(unlist(data3["G/C transversions",]))] = 0
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94 data3["G/C transversions",is.infinite(unlist(data3["G/C transversions",]))] = 0
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95
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96 data3["A/T",is.nan(unlist(data3["A/T",]))] = 0
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97 data3["A/T",is.infinite(unlist(data3["A/T",]))] = 0
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98
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99 data3 = melt(t(data3[8:10,]))
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100 names(data3) = c("Class", "Type", "value")
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101
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102 write.table(data3, plot3.txt, quote=F, sep="\t", na="", row.names=F, col.names=T)
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103
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104 p = ggplot(data3, aes(Class, value)) + geom_bar(aes(fill=Type), stat="identity", position="dodge") + ylab("% of nucleotides") + guides(fill=guide_legend(title=NULL))
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105 png(filename=plot3.png)
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106 print(p)
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107 dev.off()
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108
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