diff Dotplot_Release/R_dotPlot_hc.R @ 0:dfa3436beb67 draft

Uploaded

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Dotplot_Release/R_dotPlot_hc.R	Fri Jan 29 09:56:02 2016 -0500
@@ -0,0 +1,125 @@
+#!/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()