|
0
|
1 #!/usr/bin/env Rscript
|
|
|
2
|
|
|
3 args <- commandArgs(trailingOnly = TRUE)
|
|
|
4
|
|
|
5 pheatmapj_loc <- paste(args[6],"pheatmap_j.R",sep="/")
|
|
|
6 heatmap2j_loc <- paste(args[6],"heatmap_2j.R",sep="/")
|
|
|
7
|
|
|
8 library('latticeExtra')
|
|
|
9 library('RColorBrewer')
|
|
|
10 library('grid')
|
|
|
11 library(reshape2)
|
|
|
12 library('gplots')
|
|
|
13 library('gtools')
|
|
|
14 source(pheatmapj_loc)
|
|
|
15 source(heatmap2j_loc)
|
|
|
16
|
|
|
17 data.file <- read.table("SC_data.txt", sep="\t", header=TRUE, row.names=1) ### import spectral count data
|
|
|
18 data.file2 <- read.table("FDR_data.txt", sep="\t", header=TRUE, row.names=1) ### import FDR count data
|
|
|
19
|
|
|
20 #setting parameters
|
|
|
21
|
|
|
22 Sfirst=as.numeric(args[1]) #first FDR cutoff
|
|
|
23 Ssecond=as.numeric(args[2]) #second FDR cutoff
|
|
|
24 maxp=as.integer(args[3]) #maximum value for a spectral count
|
|
|
25 methd <- args[4]
|
|
|
26 dist_methd <- args[5]
|
|
|
27
|
|
|
28 #determine bait and prey ordering
|
|
|
29
|
|
|
30 dist_bait <- dist(as.matrix(t(data.file)), method= dist_methd) # "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski"
|
|
|
31 dist_prey <- dist(as.matrix(data.file), method= dist_methd)
|
|
|
32
|
|
|
33 if(methd == "ward"){
|
|
|
34 dist_bait <- dist_bait^2 #comment out this line and the next if not using Ward's method of clustering
|
|
|
35 dist_prey <- dist_prey^2
|
|
|
36 }
|
|
|
37
|
|
|
38 hc_bait <- hclust(dist_bait, method = methd) # method = "average", "single", "complete", "ward", "mcquitty", "median" or "centroid"
|
|
|
39 hc_prey <- hclust(dist_prey, method = methd)
|
|
|
40
|
|
|
41 data.file = data.file[hc_prey$order, , drop = FALSE]
|
|
|
42 data.file = data.file[, hc_bait$order, drop = FALSE]
|
|
|
43 data.file2 = data.file2[hc_prey$order, , drop = FALSE]
|
|
|
44 data.file2 = data.file2[, hc_bait$order, drop = FALSE]
|
|
|
45
|
|
|
46 x_ord=factor(row.names(data.file), levels=row.names(data.file))
|
|
|
47 y_ord=factor(names(data.file[1,]), levels=names(data.file[1,]))
|
|
|
48
|
|
|
49 df<-data.frame(y=rep(y_ord, nrow(data.file))
|
|
|
50 ,x=rep(x_ord, each=ncol(data.file))
|
|
|
51 ,z1=as.vector(t(data.file)) # Circle color
|
|
|
52 ,z2=as.vector(t(data.file/apply(data.file,1,max))) # Circle size
|
|
|
53 ,z3=as.vector(t(data.file2)) # FDR
|
|
|
54 )
|
|
|
55
|
|
|
56 df$z1[df$z1>maxp] <- maxp #maximum value for spectral count
|
|
|
57 df$z2[df$z2==0] <- NA
|
|
|
58 df$z3[df$z3>Ssecond] <- 0.05*maxp
|
|
|
59 df$z3[df$z3<=Ssecond & df$z3>Sfirst] <- 0.5*maxp
|
|
|
60 df$z3[df$z3<=Sfirst] <- 1*maxp
|
|
|
61 df$z4 <- df$z1
|
|
|
62 df$z4[df$z4==0] <- 0
|
|
|
63 df$z4[df$z4>0] <- 2.5
|
|
|
64
|
|
|
65 # The labeling for the colorkey
|
|
|
66
|
|
|
67 labelat = c(0, maxp)
|
|
|
68 labeltext = c(0, maxp)
|
|
|
69
|
|
|
70 # color scheme to use
|
|
|
71
|
|
|
72 nmb.colors<-maxp
|
|
|
73 z.colors<-grey(rev(seq(0,0.9,0.9/nmb.colors))) #grayscale color scale
|
|
|
74
|
|
|
75 #plot dotplot
|
|
|
76
|
|
|
77 pl <- levelplot(z1~x*y, data=df
|
|
|
78 ,col.regions =z.colors #terrain.colors(100)
|
|
|
79 ,scales = list(x = list(rot = 90), y=list(cex=0.8), tck=0) # rotates X,Y labels and changes scale
|
|
|
80 ,colorkey = FALSE
|
|
|
81 #,colorkey = list(space="bottom", width=1.5, height=0.3, labels=list(at = labelat, labels = labeltext)) #put colorkey at top with my labeling scheme
|
|
|
82 ,xlab="Prey", ylab="Bait"
|
|
|
83 ,panel=function(x,y,z,...,col.regions){
|
|
|
84 print(x)
|
|
|
85 z.c<-df$z1[ (df$x %in% as.character(x)) & (df$y %in% y)]
|
|
|
86 z.2<-df$z2[ (df$x %in% as.character(x)) & (df$y %in% y)]
|
|
|
87 z.3<-df$z3
|
|
|
88 z.4<-df$z4
|
|
|
89 panel.xyplot(x,y
|
|
|
90 ,as.table=TRUE
|
|
|
91 ,pch=21 # point type to use (circles in this case)
|
|
|
92 ,cex=((z.2-min(z.2,na.rm=TRUE))/(max(z.2,na.rm=TRUE)-min(z.2,na.rm=TRUE)))*3 #circle size
|
|
|
93 ,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
|
|
|
94 ,col=z.colors[1+z.3] # border colors
|
|
|
95 ,lex=z.4 #border thickness
|
|
|
96 )
|
|
|
97 }
|
|
|
98 #,main="Fold change" # graph main title
|
|
|
99 )
|
|
|
100 if(ncol(data.file) > 4) ht=3.5+(0.36*((ncol(data.file)-1)-4)) else ht=3.5
|
|
|
101 if(nrow(data.file) > 20) wd=8.25+(0.29*(nrow(data.file) -20)) else wd=5.7+(0.28*(nrow(data.file) -10))
|
|
|
102 pdf("dotplot.pdf", onefile = FALSE, paper = "special", height = ht, width = wd, pointsize = 2)
|
|
|
103 print(pl)
|
|
|
104 dev.off()
|
|
|
105
|
|
|
106 #plot bait vs prey heatmap
|
|
|
107
|
|
|
108 heat_df <- acast(df, y~x, value.var="z1")
|
|
|
109 heat_df <- apply(heat_df, 2, rev)
|
|
|
110
|
|
|
111 if(ncol(data.file) > 4) ht=3.5+(0.1*((ncol(data.file)-1)-4)) else ht=3.5
|
|
|
112 if(nrow(data.file) > 20) wd=8.25+(0.1*(nrow(data.file)-20)) else wd=5+(0.1*(nrow(data.file)-10))
|
|
|
113 pdf("heatmap_borders.pdf", onefile = FALSE, paper = "special", height = ht, width = wd, pointsize = 2)
|
|
|
114 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))
|
|
|
115 dev.off()
|
|
|
116
|
|
|
117 pdf("heatmap_no_borders.pdf", onefile = FALSE, paper = "special", height = ht, width = wd, pointsize = 2)
|
|
|
118 pheatmap_j(heat_df, scale="none", border_color=NA, cluster_rows=FALSE, cluster_cols=FALSE, col=colorRampPalette(c("#FFFFFF", brewer.pal(9,"Blues")))(100))
|
|
|
119 dev.off()
|
|
|
120
|
|
|
121 #plot bait vs bait heatmap using dist matrix
|
|
|
122 dist_bait <- dist_bait/max(dist_bait)
|
|
|
123 pdf("bait2bait.pdf", onefile = FALSE, paper = "special")
|
|
|
124 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))))))
|
|
|
125 dev.off()
|
