--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/pattern_plots.r	Thu Oct 13 10:52:24 2016 -0400
@@ -0,0 +1,154 @@
+library(ggplot2)
+library(reshape2)
+library(scales)
+
+args <- commandArgs(trailingOnly = TRUE)
+
+input.file = args[1] #the data that's get turned into the "SHM overview" table in the html report "data_sum.txt"
+
+plot1.path = args[2]
+plot1.png = paste(plot1.path, ".png", sep="")
+plot1.txt = paste(plot1.path, ".txt", sep="")
+
+plot2.path = args[3]
+plot2.png = paste(plot2.path, ".png", sep="")
+plot2.txt = paste(plot2.path, ".txt", sep="")
+
+plot3.path = args[4]
+plot3.png = paste(plot3.path, ".png", sep="")
+plot3.txt = paste(plot3.path, ".txt", sep="")
+
+dat = read.table(input.file, header=F, sep=",", quote="", stringsAsFactors=F, fill=T, row.names=1)
+
+
+
+classes = c("IGA", "IGA1", "IGA2", "IGG", "IGG1", "IGG2", "IGG3", "IGG4", "IGM")
+xyz = c("x", "y", "z")
+new.names = c(paste(rep(classes, each=3), xyz, sep="."), paste("un", xyz, sep="."), paste("all", xyz, sep="."))
+
+names(dat) = new.names
+
+dat["RGYW.WRCY",] = colSums(dat[c(13,14),], na.rm=T)
+dat["TW.WA",] = colSums(dat[c(15,16),], na.rm=T)
+
+data1 = dat[c("RGYW.WRCY", "TW.WA"),]
+
+data1 = data1[,names(data1)[grepl(".z", names(data1))]]
+names(data1) = gsub("\\..*", "", names(data1))
+
+data1 = melt(t(data1))
+
+names(data1) = c("Class", "Type", "value")
+
+data1 = data1[order(data1$Type),]
+
+write.table(data1, plot1.txt, quote=F, sep="\t", na="", row.names=F, col.names=T)
+
+p = ggplot(data1, aes(Class, value)) + geom_bar(aes(fill=Type), stat="identity", position="dodge", colour = "black") + ylab("% of mutations") + guides(fill=guide_legend(title=NULL))
+p = p + theme(panel.background = element_rect(fill = "white", colour="black")) + scale_fill_manual(values=c("RGYW.WRCY" = "white", "TW.WA" = "blue4"))
+#p = p + scale_colour_manual(values=c("RGYW.WRCY" = "black", "TW.WA" = "blue4"))
+png(filename=plot1.png, width=480, height=300)
+print(p)
+dev.off()
+
+data2 = dat[5:8,]
+
+data2["sum",] = colSums(data2, na.rm=T)
+
+data2 = data2[,names(data2)[grepl("\\.x", names(data2))]]
+names(data2) = gsub(".x", "", names(data2))
+
+data2["A/T",] = round(colSums(data2[3:4,]) / data2["sum",] * 100, 1)
+data2["A/T",is.nan(unlist(data2["A/T",]))] = 0
+
+data2["G/C transversions",] = round(data2[2,] / data2["sum",] * 100, 1)
+data2["G/C transitions",] = round(data2[1,] / data2["sum",] * 100, 1)
+
+
+data2["G/C transversions",is.nan(unlist(data2["G/C transversions",]))] = 0
+data2["G/C transversions",is.infinite(unlist(data2["G/C transversions",]))] = 0
+data2["G/C transitions",is.nan(unlist(data2["G/C transitions",]))] = 0
+data2["G/C transitions",is.infinite(unlist(data2["G/C transitions",]))] = 0
+
+data2 = melt(t(data2[6:8,]))
+
+names(data2) = c("Class", "Type", "value")
+
+data2 = data2[order(data2$Type),]
+
+write.table(data2, plot2.txt, quote=F, sep="\t", na="", row.names=F, col.names=T)
+
+p = ggplot(data2, aes(x=Class, y=value, fill=Type)) + geom_bar(position="fill", stat="identity", colour = "black") + scale_y_continuous(labels=percent_format()) + guides(fill=guide_legend(title=NULL)) + ylab("% of mutations")
+p = p + theme(panel.background = element_rect(fill = "white", colour="black")) + scale_fill_manual(values=c("A/T" = "blue4", "G/C transversions" = "gray74", "G/C transitions" = "white"))
+#p = p + scale_colour_manual(values=c("A/T" = "blue4", "G/C transversions" = "gray74", "G/C transitions" = "black"))
+png(filename=plot2.png, width=480, height=300)
+print(p)
+dev.off()
+
+data3 = dat[c(5, 6, 8, 17:20),]
+data3 = data3[,names(data3)[grepl("\\.x", names(data3))]]
+names(data3) = gsub(".x", "", names(data3))
+
+data3[is.na(data3)] = 0
+#data3[is.infinite(data3)] = 0
+
+data3["G/C transitions",] = round(data3[1,] / (data3[5,] + data3[7,]) * 100, 1)
+
+data3["G/C transversions",] = round(data3[2,] / (data3[5,] + data3[7,]) * 100, 1)
+
+data3["A/T",] = round(data3[3,] / (data3[4,] + data3[6,]) * 100, 1)
+
+data3["G/C transitions",is.nan(unlist(data3["G/C transitions",]))] = 0
+data3["G/C transitions",is.infinite(unlist(data3["G/C transitions",]))] = 0
+
+data3["G/C transversions",is.nan(unlist(data3["G/C transversions",]))] = 0
+data3["G/C transversions",is.infinite(unlist(data3["G/C transversions",]))] = 0
+
+data3["A/T",is.nan(unlist(data3["A/T",]))] = 0
+data3["A/T",is.infinite(unlist(data3["A/T",]))] = 0
+
+data3 = melt(t(data3[8:10,]))
+names(data3) = c("Class", "Type", "value")
+
+data3 = data3[order(data3$Type),]
+
+write.table(data3, plot3.txt, quote=F, sep="\t", na="", row.names=F, col.names=T)
+
+p = ggplot(data3, aes(Class, value)) + geom_bar(aes(fill=Type), stat="identity", position="dodge", colour = "black") + ylab("% of nucleotides") + guides(fill=guide_legend(title=NULL))
+p = p + theme(panel.background = element_rect(fill = "white", colour="black")) + scale_fill_manual(values=c("A/T" = "blue4", "G/C transversions" = "gray74", "G/C transitions" = "white"))
+#p = p + scale_colour_manual(values=c("A/T" = "blue4", "G/C transversions" = "gray74", "G/C transitions" = "black"))
+png(filename=plot3.png, width=480, height=300)
+print(p)
+dev.off()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+