diff shm_csr.r @ 0:c33d93683a09 draft

Uploaded
author davidvanzessen
date Thu, 13 Oct 2016 10:52:24 -0400
parents
children faae21ba5c63
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/shm_csr.r	Thu Oct 13 10:52:24 2016 -0400
@@ -0,0 +1,493 @@
+library(data.table)
+library(ggplot2)
+library(reshape2)
+
+args <- commandArgs(trailingOnly = TRUE)
+
+input = args[1]
+genes = unlist(strsplit(args[2], ","))
+outputdir = args[3]
+include_fr1 = ifelse(args[4] == "yes", T, F)
+setwd(outputdir)
+
+dat = read.table(input, header=T, sep="\t", fill=T, stringsAsFactors=F)
+
+if(length(dat$Sequence.ID) == 0){
+  setwd(outputdir)
+  result = data.frame(x = rep(0, 5), y = rep(0, 5), z = rep(NA, 5))
+  row.names(result) = c("Number of Mutations (%)", "Transition (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of C G (%)")
+  write.table(x=result, file="mutations.txt", sep=",",quote=F,row.names=T,col.names=F)
+  transitionTable = data.frame(A=rep(0, 4),C=rep(0, 4),G=rep(0, 4),T=rep(0, 4))
+  row.names(transitionTable) = c("A", "C", "G", "T")
+  transitionTable["A","A"] = NA
+  transitionTable["C","C"] = NA
+  transitionTable["G","G"] = NA
+  transitionTable["T","T"] = NA
+  write.table(x=transitionTable, file="transitions.txt", sep=",",quote=F,row.names=T,col.names=NA)
+  cat("0", file="n.txt")
+  stop("No data")
+}
+
+cleanup_columns = c("FR1.IMGT.c.a",
+                    "FR2.IMGT.g.t",
+                    "CDR1.IMGT.Nb.of.nucleotides",
+                    "CDR2.IMGT.t.a",
+                    "FR1.IMGT.c.g",
+                    "CDR1.IMGT.c.t",
+                    "FR2.IMGT.a.c",
+                    "FR2.IMGT.Nb.of.mutations",
+                    "FR2.IMGT.g.c",
+                    "FR2.IMGT.a.g",
+                    "FR3.IMGT.t.a",
+                    "FR3.IMGT.t.c",
+                    "FR2.IMGT.g.a",
+                    "FR3.IMGT.c.g",
+                    "FR1.IMGT.Nb.of.mutations",
+                    "CDR1.IMGT.g.a",
+                    "CDR1.IMGT.t.g",
+                    "CDR1.IMGT.g.c",
+                    "CDR2.IMGT.Nb.of.nucleotides",
+                    "FR2.IMGT.a.t",
+                    "CDR1.IMGT.Nb.of.mutations",
+                    "CDR3.IMGT.Nb.of.nucleotides",
+                    "CDR1.IMGT.a.g",
+                    "FR3.IMGT.a.c",
+                    "FR1.IMGT.g.a",
+                    "FR3.IMGT.a.g",
+                    "FR1.IMGT.a.t",
+                    "CDR2.IMGT.a.g",
+                    "CDR2.IMGT.Nb.of.mutations",
+                    "CDR2.IMGT.g.t",
+                    "CDR2.IMGT.a.c",
+                    "CDR1.IMGT.t.c",
+                    "FR3.IMGT.g.c",
+                    "FR1.IMGT.g.t",
+                    "FR3.IMGT.g.t",
+                    "CDR1.IMGT.a.t",
+                    "FR1.IMGT.a.g",
+                    "FR3.IMGT.a.t",
+                    "FR3.IMGT.Nb.of.nucleotides",
+                    "FR2.IMGT.t.c",
+                    "CDR2.IMGT.g.a",
+                    "FR2.IMGT.t.a",
+                    "CDR1.IMGT.t.a",
+                    "FR2.IMGT.t.g",
+                    "FR3.IMGT.t.g",
+                    "FR2.IMGT.Nb.of.nucleotides",
+                    "FR1.IMGT.t.a",
+                    "FR1.IMGT.t.g",
+                    "FR3.IMGT.c.t",
+                    "FR1.IMGT.t.c",
+                    "CDR2.IMGT.a.t",
+                    "FR2.IMGT.c.t",
+                    "CDR1.IMGT.g.t",
+                    "CDR2.IMGT.t.g",
+                    "FR1.IMGT.Nb.of.nucleotides",
+                    "CDR1.IMGT.c.g",
+                    "CDR2.IMGT.t.c",
+                    "FR3.IMGT.g.a",
+                    "CDR1.IMGT.a.c",
+                    "FR2.IMGT.c.a",
+                    "FR3.IMGT.Nb.of.mutations",
+                    "FR2.IMGT.c.g",
+                    "CDR2.IMGT.g.c",
+                    "FR1.IMGT.g.c",
+                    "CDR2.IMGT.c.t",
+                    "FR3.IMGT.c.a",
+                    "CDR1.IMGT.c.a",
+                    "CDR2.IMGT.c.g",
+                    "CDR2.IMGT.c.a",
+                    "FR1.IMGT.c.t",
+                    "FR1.IMGT.Nb.of.silent.mutations",
+                    "FR2.IMGT.Nb.of.silent.mutations",
+                    "FR3.IMGT.Nb.of.silent.mutations",
+                    "FR1.IMGT.Nb.of.nonsilent.mutations",
+                    "FR2.IMGT.Nb.of.nonsilent.mutations",
+                    "FR3.IMGT.Nb.of.nonsilent.mutations")
+
+
+print("Cleaning up columns")
+for(col in cleanup_columns){
+  dat[,col] = gsub("\\(.*\\)", "", dat[,col])
+  #dat[dat[,col] == "",] = "0"
+  dat[,col] = as.numeric(dat[,col])
+  dat[is.na(dat[,col]),col] = 0
+}
+
+regions = c("FR1", "CDR1", "FR2", "CDR2", "FR3")
+if(!include_fr1){
+	regions = c("CDR1", "FR2", "CDR2", "FR3")
+}
+
+sum_by_row = function(x, columns) { sum(as.numeric(x[columns]), na.rm=T) }
+
+print("aggregating data into new columns")
+
+VRegionMutations_columns = paste(regions, ".IMGT.Nb.of.mutations", sep="")
+dat$VRegionMutations =  apply(dat, FUN=sum_by_row, 1, columns=VRegionMutations_columns)
+
+VRegionNucleotides_columns = paste(regions, ".IMGT.Nb.of.nucleotides", sep="")
+dat$FR3.IMGT.Nb.of.nucleotides = nchar(dat$FR3.IMGT.seq)
+dat$VRegionNucleotides =  apply(dat, FUN=sum_by_row, 1, columns=VRegionNucleotides_columns)
+
+transitionMutations_columns = paste(rep(regions, each=4), c(".IMGT.a.g", ".IMGT.g.a", ".IMGT.c.t", ".IMGT.t.c"), sep="")
+dat$transitionMutations = apply(dat, FUN=sum_by_row, 1, columns=transitionMutations_columns)
+
+transversionMutations_columns = paste(rep(regions, each=8), c(".IMGT.a.c",".IMGT.c.a",".IMGT.a.t",".IMGT.t.a",".IMGT.g.c",".IMGT.c.g",".IMGT.g.t",".IMGT.t.g"), sep="")
+dat$transversionMutations = apply(dat, FUN=sum_by_row, 1, columns=transversionMutations_columns)
+
+
+transitionMutationsAtGC_columns = paste(rep(regions, each=2), c(".IMGT.g.a",".IMGT.c.t"), sep="")
+dat$transitionMutationsAtGC = apply(dat, FUN=sum_by_row, 1, columns=transitionMutationsAtGC_columns)
+
+
+totalMutationsAtGC_columns = paste(rep(regions, each=6), c(".IMGT.c.g",".IMGT.c.t",".IMGT.c.a",".IMGT.g.c",".IMGT.g.a",".IMGT.g.t"), sep="")
+#totalMutationsAtGC_columns = paste(rep(regions, each=6), c(".IMGT.g.a",".IMGT.c.t",".IMGT.c.a",".IMGT.c.g",".IMGT.g.t"), sep="")
+dat$totalMutationsAtGC = apply(dat, FUN=sum_by_row, 1, columns=totalMutationsAtGC_columns)
+
+transitionMutationsAtAT_columns = paste(rep(regions, each=2), c(".IMGT.a.g",".IMGT.t.c"), sep="")
+dat$transitionMutationsAtAT = apply(dat, FUN=sum_by_row, 1, columns=transitionMutationsAtAT_columns)
+
+totalMutationsAtAT_columns = paste(rep(regions, each=6), c(".IMGT.a.g",".IMGT.a.c",".IMGT.a.t",".IMGT.t.g",".IMGT.t.c",".IMGT.t.a"), sep="")
+#totalMutationsAtAT_columns = paste(rep(regions, each=5), c(".IMGT.a.g",".IMGT.t.c",".IMGT.a.c",".IMGT.g.c",".IMGT.t.g"), sep="")
+dat$totalMutationsAtAT = apply(dat, FUN=sum_by_row, 1, columns=totalMutationsAtAT_columns)
+
+
+FRRegions = regions[grepl("FR", regions)]
+CDRRegions = regions[grepl("CDR", regions)]
+
+FR_silentMutations_columns = paste(FRRegions, ".IMGT.Nb.of.silent.mutations", sep="")
+dat$silentMutationsFR = apply(dat, FUN=sum_by_row, 1, columns=FR_silentMutations_columns)
+
+CDR_silentMutations_columns = paste(CDRRegions, ".IMGT.Nb.of.silent.mutations", sep="")
+dat$silentMutationsCDR = apply(dat, FUN=sum_by_row, 1, columns=CDR_silentMutations_columns)
+
+FR_nonSilentMutations_columns = paste(FRRegions, ".IMGT.Nb.of.nonsilent.mutations", sep="")
+dat$nonSilentMutationsFR = apply(dat, FUN=sum_by_row, 1, columns=FR_nonSilentMutations_columns)
+
+CDR_nonSilentMutations_columns = paste(CDRRegions, ".IMGT.Nb.of.nonsilent.mutations", sep="")
+dat$nonSilentMutationsCDR = apply(dat, FUN=sum_by_row, 1, columns=CDR_nonSilentMutations_columns)
+
+mutation.sum.columns = c("Sequence.ID", "VRegionMutations", "VRegionNucleotides", "transitionMutations", "transversionMutations", "transitionMutationsAtGC", "transitionMutationsAtAT", "silentMutationsFR", "nonSilentMutationsFR", "silentMutationsCDR", "nonSilentMutationsCDR")
+
+write.table(dat[,mutation.sum.columns], "mutation_by_id.txt", sep="\t",quote=F,row.names=F,col.names=T)
+
+setwd(outputdir)
+
+base.order = data.frame(base=c("A", "T", "C", "G"), order=1:4)
+
+calculate_result = function(i, gene, dat, matrx, f, fname, name){
+	tmp = dat[grepl(paste("^", gene, ".*", sep=""), dat$best_match),]
+
+	j = i - 1
+	x = (j * 3) + 1
+	y = (j * 3) + 2
+	z = (j * 3) + 3
+	 
+	if(nrow(tmp) > 0){
+	  
+		if(fname == "sum"){
+		matrx[1,x] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
+		matrx[1,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1)
+		matrx[1,z] = round(f(matrx[1,x] / matrx[1,y]) * 100, digits=1)
+		} else {
+		matrx[1,x] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
+		matrx[1,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1)
+		matrx[1,z] = round(f(tmp$VRegionMutations / tmp$VRegionNucleotides) * 100, digits=1)
+		}
+
+		matrx[2,x] = round(f(tmp$transitionMutations, na.rm=T), digits=1)
+		matrx[2,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
+		matrx[2,z] = round(matrx[2,x] / matrx[2,y] * 100, digits=1)
+
+		matrx[3,x] = round(f(tmp$transversionMutations, na.rm=T), digits=1)
+		matrx[3,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
+		matrx[3,z] = round(matrx[3,x] / matrx[3,y] * 100, digits=1)
+
+		matrx[4,x] = round(f(tmp$transitionMutationsAtGC, na.rm=T), digits=1)
+		matrx[4,y] = round(f(tmp$totalMutationsAtGC, na.rm=T), digits=1)
+		matrx[4,z] = round(matrx[4,x] / matrx[4,y] * 100, digits=1)
+
+		matrx[5,x] = round(f(tmp$totalMutationsAtGC, na.rm=T), digits=1)
+		matrx[5,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
+		matrx[5,z] = round(matrx[5,x] / matrx[5,y] * 100, digits=1)
+
+		matrx[6,x] = round(f(tmp$transitionMutationsAtAT, na.rm=T), digits=1)
+		matrx[6,y] = round(f(tmp$totalMutationsAtAT, na.rm=T), digits=1)
+		matrx[6,z] = round(matrx[6,x] / matrx[6,y] * 100, digits=1)
+
+		matrx[7,x] = round(f(tmp$totalMutationsAtAT, na.rm=T), digits=1)
+		matrx[7,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1)
+		matrx[7,z] = round(matrx[7,x] / matrx[7,y] * 100, digits=1)
+
+		matrx[8,x] = round(f(tmp$nonSilentMutationsFR, na.rm=T), digits=1)
+		matrx[8,y] = round(f(tmp$silentMutationsFR, na.rm=T), digits=1)
+		matrx[8,z] = round(matrx[8,x] / matrx[8,y], digits=1)
+
+		matrx[9,x] = round(f(tmp$nonSilentMutationsCDR, na.rm=T), digits=1)
+		matrx[9,y] = round(f(tmp$silentMutationsCDR, na.rm=T), digits=1)
+		matrx[9,z] = round(matrx[9,x] / matrx[9,y], digits=1)
+
+		if(fname == "sum"){
+			matrx[10,x] = round(f(rowSums(tmp[,c("FR2.IMGT.Nb.of.nucleotides", "FR3.IMGT.Nb.of.nucleotides")], na.rm=T)), digits=1)
+			matrx[10,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1)
+			matrx[10,z] = round(matrx[10,x] / matrx[10,y] * 100, digits=1)
+
+			matrx[11,x] = round(f(rowSums(tmp[,c("CDR1.IMGT.Nb.of.nucleotides", "CDR2.IMGT.Nb.of.nucleotides")], na.rm=T)), digits=1)
+			matrx[11,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1)
+			matrx[11,z] = round(matrx[11,x] / matrx[11,y] * 100, digits=1)
+		}
+	}
+  
+	transitionTable = data.frame(A=zeros,C=zeros,G=zeros,T=zeros)
+	row.names(transitionTable) = c("A", "C", "G", "T")
+	transitionTable["A","A"] = NA
+	transitionTable["C","C"] = NA
+	transitionTable["G","G"] = NA
+	transitionTable["T","T"] = NA
+
+	if(nrow(tmp) > 0){
+		for(nt1 in nts){
+			for(nt2 in nts){
+				if(nt1 == nt2){
+					next
+				}
+				NT1 = LETTERS[letters == nt1]
+				NT2 = LETTERS[letters == nt2]
+				FR1 = paste("FR1.IMGT.", nt1, ".", nt2, sep="")
+				CDR1 = paste("CDR1.IMGT.", nt1, ".", nt2, sep="")
+				FR2 = paste("FR2.IMGT.", nt1, ".", nt2, sep="")
+				CDR2 = paste("CDR2.IMGT.", nt1, ".", nt2, sep="")
+				FR3 = paste("FR3.IMGT.", nt1, ".", nt2, sep="")
+				if(include_fr1){
+					transitionTable[NT1,NT2] = sum(tmp[,c(FR1, CDR1, FR2, CDR2, FR3)])
+				} else {
+					transitionTable[NT1,NT2] = sum(tmp[,c(CDR1, FR2, CDR2, FR3)])
+				}
+			}
+		}
+		transition = transitionTable
+		transition$id = names(transition)
+		
+		transition2 = melt(transition, id.vars="id")
+		
+		transition2 = merge(transition2, base.order, by.x="id", by.y="base")
+		transition2 = merge(transition2, base.order, by.x="variable", by.y="base")
+
+		transition2[is.na(transition2$value),]$value = 0
+		
+		if(!all(transition2$value == 0)){ #having rows of data but a transition table filled with 0 is bad
+
+			print("Plotting stacked transition")
+
+			png(filename=paste("transitions_stacked_", name, ".png", sep=""))
+			p = ggplot(transition2, aes(factor(reorder(id, order.x)), y=value, fill=factor(reorder(variable, order.y)))) + geom_bar(position="fill", stat="identity", colour="black") #stacked bar
+			p = p + xlab("From base") + ylab("To base") + ggtitle("Mutations frequency from base to base") + guides(fill=guide_legend(title=NULL))
+			p = p + theme(panel.background = element_rect(fill = "white", colour="black")) + scale_fill_manual(values=c("A" = "blue4", "G" = "lightblue1", "C" = "olivedrab3", "T" = "olivedrab4"))
+			#p = p + scale_colour_manual(values=c("A" = "black", "G" = "black", "C" = "black", "T" = "black"))
+			print(p)
+			dev.off()
+
+			print("Plotting heatmap transition")
+
+			png(filename=paste("transitions_heatmap_", name, ".png", sep=""))
+			p = ggplot(transition2, aes(factor(reorder(id, order.x)), factor(reorder(variable, order.y)))) + geom_tile(aes(fill = value)) + scale_fill_gradient(low="white", high="steelblue") #heatmap
+			p = p + xlab("From base") + ylab("To base") + ggtitle("Mutations frequency from base to base")  + theme(panel.background = element_rect(fill = "white", colour="black"))
+			print(p)
+			dev.off()
+		} else {
+			print("No data to plot")
+		}
+	}
+
+	#print(paste("writing value file: ", name, "_", fname, "_value.txt" ,sep=""))
+
+	write.table(x=transitionTable, file=paste("transitions_", name ,"_", fname, ".txt", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
+	write.table(x=tmp[,c("Sequence.ID", "best_match", "chunk_hit_percentage", "nt_hit_percentage", "start_locations")], file=paste("matched_", name , "_", fname, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T)
+
+	cat(matrx[1,x], file=paste(name, "_", fname, "_value.txt" ,sep=""))
+	cat(nrow(tmp), file=paste(name, "_", fname, "_n.txt" ,sep=""))
+
+	#print(paste(fname, name, nrow(tmp)))
+
+	matrx
+}
+
+nts = c("a", "c", "g", "t")
+zeros=rep(0, 4)
+
+funcs = c(median, sum, mean)
+fnames = c("median", "sum", "mean")
+
+print("Creating result tables")
+
+for(i in 1:length(funcs)){
+	func = funcs[[i]]
+	fname = fnames[[i]]
+	
+	rows = 9
+	if(fname == "sum"){
+		rows = 11
+	}
+	matrx = matrix(data = 0, ncol=((length(genes) + 1) * 3),nrow=rows)
+	
+	for(i in 1:length(genes)){
+		print(paste("Creating table for", fname, genes[i]))
+		matrx = calculate_result(i, genes[i], dat, matrx, func, fname, genes[i])
+	}
+
+	matrx = calculate_result(i + 1, ".*", dat[!grepl("unmatched", dat$best_match),], matrx, func, fname, name="all")
+	
+	result = data.frame(matrx)
+	if(fname == "sum"){
+		row.names(result) = c("Number of Mutations (%)", "Transitions (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of C G (%)", "Transitions at A T (%)", "Targeting of A T (%)", "FR R/S (ratio)", "CDR R/S (ratio)", "nt in FR", "nt in CDR")
+	} else {
+		row.names(result) = c("Number of Mutations (%)", "Transitions (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of C G (%)", "Transitions at A T (%)", "Targeting of A T (%)", "FR R/S (ratio)", "CDR R/S (ratio)")
+	}
+
+	write.table(x=result, file=paste("mutations_", fname, ".txt", sep=""), sep=",",quote=F,row.names=T,col.names=F)
+}
+
+print("Adding median number of mutations to sum table")
+
+sum.table = read.table("mutations_sum.txt", sep=",", header=F)
+median.table = read.table("mutations_median.txt", sep=",", header=F)
+
+new.table = sum.table[1,]
+new.table[2,] = median.table[1,]
+new.table[3:12,] = sum.table[2:11,]
+new.table[,1] = as.character(new.table[,1])
+new.table[2,1] = "Median of Number of Mutations (%)"
+
+#sum.table = sum.table[c("Number of Mutations (%)", "Median of Number of Mutations (%)", "Transition (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of C G (%)", "Transitions at A T (%)", "Targeting of A T (%)", "FR R/S (ratio)", "CDR R/S (ratio)", "nt in FR", "nt in CDR"),]
+
+write.table(x=new.table, file="mutations_sum.txt", sep=",",quote=F,row.names=F,col.names=F)
+
+
+print("Plotting IGA piechart")
+
+dat = dat[!grepl("^unmatched", dat$best_match),]
+
+#blegh
+genesForPlot = dat[grepl("IGA", dat$best_match),]$best_match
+if(length(genesForPlot) > 0){
+	genesForPlot = data.frame(table(genesForPlot))
+	colnames(genesForPlot) = c("Gene","Freq")
+	genesForPlot$label = paste(genesForPlot$Gene, "-", genesForPlot$Freq)
+
+	pc = ggplot(genesForPlot, aes(x = factor(1), y=Freq, fill=Gene))
+	pc = pc + geom_bar(width = 1, stat = "identity") + scale_fill_manual(labels=genesForPlot$label, values=c("IGA1" = "lightblue1", "IGA2" = "blue4"))
+	pc = pc + coord_polar(theta="y")
+	pc = pc + theme(panel.background = element_rect(fill = "white", colour="black"))
+	pc = pc + xlab(" ") + ylab(" ") + ggtitle(paste("IGA subclasses", "( n =", sum(genesForPlot$Freq), ")"))
+	write.table(genesForPlot, "IGA.txt", sep="\t",quote=F,row.names=F,col.names=T)
+
+	png(filename="IGA.png")
+	print(pc)
+	dev.off()
+}
+
+print("Plotting IGG piechart")
+
+genesForPlot = dat[grepl("IGG", dat$best_match),]$best_match
+if(length(genesForPlot) > 0){
+	genesForPlot = data.frame(table(genesForPlot))
+	colnames(genesForPlot) = c("Gene","Freq")
+	genesForPlot$label = paste(genesForPlot$Gene, "-", genesForPlot$Freq)
+
+	pc = ggplot(genesForPlot, aes(x = factor(1), y=Freq, fill=Gene))
+	pc = pc + geom_bar(width = 1, stat = "identity") + scale_fill_manual(labels=genesForPlot$label, values=c("IGG1" = "olivedrab3", "IGG2" = "red", "IGG3" = "gold", "IGG4" = "darkred"))
+	pc = pc + coord_polar(theta="y") 
+	pc = pc + theme(panel.background = element_rect(fill = "white", colour="black"))
+	pc = pc + xlab(" ") + ylab(" ") + ggtitle(paste("IGG subclasses", "( n =", sum(genesForPlot$Freq), ")"))
+	write.table(genesForPlot, "IGG.txt", sep="\t",quote=F,row.names=F,col.names=T)
+
+	png(filename="IGG.png")
+	print(pc)
+	dev.off()
+}
+
+
+print("Plotting scatterplot")
+
+dat$percentage_mutations = round(dat$VRegionMutations / dat$VRegionNucleotides * 100, 2)
+
+p = ggplot(dat, aes(best_match, percentage_mutations))
+p = p + geom_point(aes(colour=best_match), position="jitter") + geom_boxplot(aes(middle=mean(percentage_mutations)), alpha=0.1, outlier.shape = NA)
+p = p + xlab("Subclass") + ylab("Frequency") + ggtitle("Frequency scatter plot") + theme(panel.background = element_rect(fill = "white", colour="black"))
+p = p + scale_fill_manual(values=c("IGA1" = "lightblue1", "IGA2" = "blue4", "IGG1" = "olivedrab3", "IGG2" = "red", "IGG3" = "gold", "IGG4" = "darkred", "IGM" = "black"))
+p = p + scale_colour_manual(values=c("IGA1" = "lightblue1", "IGA2" = "blue4", "IGG1" = "olivedrab3", "IGG2" = "red", "IGG3" = "gold", "IGG4" = "darkred", "IGM" = "black"))
+
+png(filename="scatter.png")
+print(p)
+dev.off()
+
+write.table(dat[,c("Sequence.ID", "best_match", "VRegionMutations", "VRegionNucleotides", "percentage_mutations")], "scatter.txt", sep="\t",quote=F,row.names=F,col.names=T)
+
+write.table(dat, input, sep="\t",quote=F,row.names=F,col.names=T)
+
+
+print("Plotting frequency ranges plot")
+
+dat$best_match_class = substr(dat$best_match, 0, 3)
+freq_labels = c("0", "0-2", "2-5", "5-10", "10-15", "15-20", "20")
+dat$frequency_bins = cut(dat$percentage_mutations, breaks=c(-Inf, 0, 2,5,10,15,20, Inf), labels=freq_labels)
+
+frequency_bins_sum = data.frame(data.table(dat)[, list(class_sum=sum(.N)), by=c("best_match_class")])
+
+frequency_bins_data = data.frame(data.table(dat)[, list(frequency_count=.N), by=c("best_match_class", "frequency_bins")])
+
+frequency_bins_data = merge(frequency_bins_data, frequency_bins_sum, by="best_match_class")
+
+frequency_bins_data$frequency = round(frequency_bins_data$frequency_count / frequency_bins_data$class_sum * 100, 2)
+
+p = ggplot(frequency_bins_data, aes(frequency_bins, frequency))
+p = p + geom_bar(aes(fill=best_match_class), stat="identity", position="dodge") + theme(panel.background = element_rect(fill = "white", colour="black"))
+p = p + xlab("Frequency ranges") + ylab("Frequency") + ggtitle("Mutation Frequencies by class") + scale_fill_manual(values=c("IGA" = "blue4", "IGG" = "olivedrab3", "IGM" = "black"))
+
+png(filename="frequency_ranges.png")
+print(p)
+dev.off()
+
+frequency_bins_data_by_class = frequency_bins_data
+
+write.table(frequency_bins_data_by_class, "frequency_ranges_classes.txt", sep="\t",quote=F,row.names=F,col.names=T)
+
+frequency_bins_data = data.frame(data.table(dat)[, list(frequency_count=.N), by=c("best_match", "best_match_class", "frequency_bins")])
+
+frequency_bins_data = merge(frequency_bins_data, frequency_bins_sum, by="best_match_class")
+
+frequency_bins_data$frequency = round(frequency_bins_data$frequency_count / frequency_bins_data$class_sum * 100, 2)
+
+write.table(frequency_bins_data, "frequency_ranges_subclasses.txt", sep="\t",quote=F,row.names=F,col.names=T)
+
+
+#frequency_bins_data_by_class
+#frequency_ranges_subclasses.txt
+
+
+
+
+
+
+
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+