Mercurial > repos > bornea > dotplot_runner
view Dotplot_Release/R_dotPlot_hc.R @ 0:dfa3436beb67 draft
Uploaded
author | bornea |
---|---|
date | Fri, 29 Jan 2016 09:56:02 -0500 |
parents | |
children |
line wrap: on
line source
#!/usr/bin/env Rscript args <- commandArgs(trailingOnly = TRUE) pheatmapj_loc <- paste(args[6],"pheatmap_j.R",sep="/") heatmap2j_loc <- paste(args[6],"heatmap_2j.R",sep="/") library('latticeExtra') library('RColorBrewer') library('grid') library(reshape2) library('gplots') library('gtools') source(pheatmapj_loc) source(heatmap2j_loc) data.file <- read.table("SC_data.txt", sep="\t", header=TRUE, row.names=1) ### import spectral count data data.file2 <- read.table("FDR_data.txt", sep="\t", header=TRUE, row.names=1) ### import FDR count data #setting parameters Sfirst=as.numeric(args[1]) #first FDR cutoff Ssecond=as.numeric(args[2]) #second FDR cutoff maxp=as.integer(args[3]) #maximum value for a spectral count methd <- args[4] dist_methd <- args[5] #determine bait and prey ordering dist_bait <- dist(as.matrix(t(data.file)), method= dist_methd) # "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski" dist_prey <- dist(as.matrix(data.file), method= dist_methd) if(methd == "ward"){ dist_bait <- dist_bait^2 #comment out this line and the next if not using Ward's method of clustering dist_prey <- dist_prey^2 } hc_bait <- hclust(dist_bait, method = methd) # method = "average", "single", "complete", "ward", "mcquitty", "median" or "centroid" hc_prey <- hclust(dist_prey, method = methd) data.file = data.file[hc_prey$order, , drop = FALSE] data.file = data.file[, hc_bait$order, drop = FALSE] data.file2 = data.file2[hc_prey$order, , drop = FALSE] data.file2 = data.file2[, hc_bait$order, drop = FALSE] x_ord=factor(row.names(data.file), levels=row.names(data.file)) y_ord=factor(names(data.file[1,]), levels=names(data.file[1,])) df<-data.frame(y=rep(y_ord, nrow(data.file)) ,x=rep(x_ord, each=ncol(data.file)) ,z1=as.vector(t(data.file)) # Circle color ,z2=as.vector(t(data.file/apply(data.file,1,max))) # Circle size ,z3=as.vector(t(data.file2)) # FDR ) df$z1[df$z1>maxp] <- maxp #maximum value for spectral count df$z2[df$z2==0] <- NA df$z3[df$z3>Ssecond] <- 0.05*maxp df$z3[df$z3<=Ssecond & df$z3>Sfirst] <- 0.5*maxp df$z3[df$z3<=Sfirst] <- 1*maxp df$z4 <- df$z1 df$z4[df$z4==0] <- 0 df$z4[df$z4>0] <- 2.5 # The labeling for the colorkey labelat = c(0, maxp) labeltext = c(0, maxp) # color scheme to use nmb.colors<-maxp z.colors<-grey(rev(seq(0,0.9,0.9/nmb.colors))) #grayscale color scale #plot dotplot pl <- levelplot(z1~x*y, data=df ,col.regions =z.colors #terrain.colors(100) ,scales = list(x = list(rot = 90), y=list(cex=0.8), tck=0) # rotates X,Y labels and changes scale ,colorkey = FALSE #,colorkey = list(space="bottom", width=1.5, height=0.3, labels=list(at = labelat, labels = labeltext)) #put colorkey at top with my labeling scheme ,xlab="Prey", ylab="Bait" ,panel=function(x,y,z,...,col.regions){ print(x) z.c<-df$z1[ (df$x %in% as.character(x)) & (df$y %in% y)] z.2<-df$z2[ (df$x %in% as.character(x)) & (df$y %in% y)] z.3<-df$z3 z.4<-df$z4 panel.xyplot(x,y ,as.table=TRUE ,pch=21 # point type to use (circles in this case) ,cex=((z.2-min(z.2,na.rm=TRUE))/(max(z.2,na.rm=TRUE)-min(z.2,na.rm=TRUE)))*3 #circle size ,fill=z.colors[floor((z.c-min(z.c,na.rm=TRUE))*nmb.colors/(max(z.c,na.rm=TRUE)-min(z.c,na.rm=TRUE)))+1] # circle colors ,col=z.colors[1+z.3] # border colors ,lex=z.4 #border thickness ) } #,main="Fold change" # graph main title ) if(ncol(data.file) > 4) ht=3.5+(0.36*((ncol(data.file)-1)-4)) else ht=3.5 if(nrow(data.file) > 20) wd=8.25+(0.29*(nrow(data.file) -20)) else wd=5.7+(0.28*(nrow(data.file) -10)) pdf("dotplot.pdf", onefile = FALSE, paper = "special", height = ht, width = wd, pointsize = 2) print(pl) dev.off() #plot bait vs prey heatmap heat_df <- acast(df, y~x, value.var="z1") heat_df <- apply(heat_df, 2, rev) if(ncol(data.file) > 4) ht=3.5+(0.1*((ncol(data.file)-1)-4)) else ht=3.5 if(nrow(data.file) > 20) wd=8.25+(0.1*(nrow(data.file)-20)) else wd=5+(0.1*(nrow(data.file)-10)) pdf("heatmap_borders.pdf", onefile = FALSE, paper = "special", height = ht, width = wd, pointsize = 2) pheatmap_j(heat_df, scale="none", border_color="black", border_width = 0.1, cluster_rows=FALSE, cluster_cols=FALSE, col=colorRampPalette(c("#FFFFFF", brewer.pal(9,"Blues")))(100)) dev.off() pdf("heatmap_no_borders.pdf", onefile = FALSE, paper = "special", height = ht, width = wd, pointsize = 2) pheatmap_j(heat_df, scale="none", border_color=NA, cluster_rows=FALSE, cluster_cols=FALSE, col=colorRampPalette(c("#FFFFFF", brewer.pal(9,"Blues")))(100)) dev.off() #plot bait vs bait heatmap using dist matrix dist_bait <- dist_bait/max(dist_bait) pdf("bait2bait.pdf", onefile = FALSE, paper = "special") heatmap_2j(as.matrix(dist_bait), trace="none", scale="none", density.info="none", col=rev(colorRampPalette(c("#FFFFFF", brewer.pal(9,"Blues")))(100)), xMin=0, xMax=1, margins=c(1.5*max(nchar(rownames(as.matrix(dist_bait)))),1.5*max(nchar(colnames(as.matrix(dist_bait)))))) dev.off()