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root/owl/trunk/src/owl/decoyScoring/decoys_summary_plots.R
Revision: 1006
Committed: Wed Mar 31 13:00:35 2010 UTC (9 years, 10 months ago) by hstehr
File size: 4413 byte(s)
Log Message:
refactoring: created packages runners.blast, runners.gromacs, structure.graphs, moved actionTools to util, structure.decoyScoring to owl.decoyScoring, tinker to runners.tinker
Line File contents
1
2 DECOYSETS<-c("4state_reduced", "fisa", "fisa_casp3", "hg_structal", "ig_structal", "ig_structal_hires", "lattice_ssfit", "lmds", "vhp_mcmd","means")
3 TYPES<-c("atomtype","atomcount","atomcomb","restype","rescount","rescomb")
4 COLORS<-c(2,5,3,6,4,8)
5
6 postscript("plots-means.ps")
7
8 for (decoySet in DECOYSETS) {
9 data<-read.table(paste(decoySet,".summary",sep=""),header=FALSE)
10 data<-data[order(data$V3),] # sorting by cutoff ascending
11
12 # zscores
13 xlim<-c(min(data$V3),max(data$V3))
14 #ylim<-c(min(data$V7),max(data$V7))
15 ylim<-c(-2,6.5)
16 yspacing<-(ylim[2]-ylim[1])/25
17 i=1
18 for (type in TYPES) {
19 if (i==1) {
20 plot(data$V3[data$V2==type],data$V7[data$V2==type],xlim=xlim,ylim=ylim,
21 xlab="cutoff",ylab="zscore",main=paste("Mean native z-scores for decoy set '",decoySet,"' (size ",data$V5[1],")",sep=""),
22 type="b",col=COLORS[i])
23 } else {
24 points(data$V3[data$V2==type],data$V7[data$V2==type],xlim=xlim,ylim=ylim,type="b",col=COLORS[i])
25 }
26 legend(x=xlim[2]-3,y=ylim[2]-yspacing*i, type, col=COLORS[i], lty=1 , bty="n", pch=1)
27 i=i+1
28
29 }
30 abline(v=6,col=8,lty=2)
31 abline(h=seq(-2,6,2),col=8)
32
33 # proportion native ranked 1
34 ylim<-c(0,1)
35 yspacing<-(ylim[2]-ylim[1])/25
36 i=1
37 for (type in TYPES) {
38 if (i==1) {
39 plot(data$V3[data$V2==type],data$V6[data$V2==type],xlim=xlim,ylim=ylim,
40 xlab="cutoff",ylab="native ranked 1st",main=paste("Proportion of ranked-1st natives for decoy set '",decoySet,"' (size ",data$V5[1],")",sep=""),
41 type="b",col=COLORS[i])
42 } else {
43 points(data$V3[data$V2==type],data$V6[data$V2==type],xlim=xlim,ylim=ylim,type="b",col=COLORS[i])
44 }
45 legend(x=xlim[2]-3,y=ylim[2]-yspacing*i, type, col=COLORS[i], lty=1 , bty="n", pch=1)
46 i=i+1
47
48 }
49 abline(v=6,col=8,lty=2)
50
51 # rank correlation
52 ylim<-c(0,1)
53 yspacing<-(ylim[2]-ylim[1])/25
54 i=1
55 for (type in TYPES) {
56 if (i==1) {
57 plot(data$V3[data$V2==type],-data$V8[data$V2==type],xlim=xlim,ylim=ylim,
58 xlab="cutoff",ylab="spearman",main=paste("Mean rank correlation for decoy set '",decoySet,"' (size ",data$V5[1],")",sep=""),
59 type="b",col=COLORS[i])
60 } else {
61 points(data$V3[data$V2==type],-data$V8[data$V2==type],xlim=xlim,ylim=ylim,type="b",col=COLORS[i])
62 }
63 legend(x=xlim[2]-3,y=ylim[2]-yspacing*i, type, col=COLORS[i], lty=1 , bty="n", pch=1)
64 i=i+1
65
66 }
67 abline(v=6,col=8,lty=2)
68
69 }
70
71 dev.off()
72
73
74 DECOYSETS<-c("4state_reduced", "fisa", "fisa_casp3", "hg_structal", "ig_structal", "ig_structal_hires", "lattice_ssfit", "lmds", "vhp_mcmd")
75 TYPES<-c("atomcomb","atomcount","atomtype","rescomb","rescount","restype")
76 COLORS<-c(2,2,2,4,4,4)
77 PCHS<-c(1,4,5,1,4,5)
78
79 postscript("plots-individual.ps")
80 all.atType.scores<-c()
81 all.atCount.scores<-c()
82 all.resType.scores<-c()
83 all.resCount.scores<-c()
84
85 for (decoySet in DECOYSETS) {
86 data<-read.table(paste(decoySet,".indiv.summary",sep=""),header=FALSE)
87 # zscores
88 xlim<-c(1,dim(data)[1])
89 #ylim<-c(min(data$V6),max(data$V6))
90 ylim<-c(-2,6.5)
91 yspacing<-(ylim[2]-ylim[1])/25
92 i=1
93 for (type in TYPES) {
94 if (i==1) {
95 plot(data[,3*i],ylim=ylim,
96 xlab="decoys",ylab="zscore",main=paste("z-scores for decoy set '",decoySet,"'",sep=""),
97 type="p",pch=PCHS[i],col=COLORS[i],
98 axes=FALSE)
99 axis(2)
100 axis(1, at=c(1:dim(data)[1]), labels=data$V1)
101 box()
102 } else {
103 points(data[,3*i],ylim=ylim,type="p",pch=PCHS[i],col=COLORS[i])
104 }
105 legend(x=xlim[2]-(xlim[2]-xlim[1])/10,y=ylim[2]-yspacing*(i-1), type, col=COLORS[i], lty=0 , bty="n", pch=PCHS[i])
106 i=i+1
107
108 }
109
110 abline(v=c(1:dim(data)[1]),col=8,lty=3)
111
112 all.atCount.scores<-c(all.atCount.scores,data$V3)
113 all.atType.scores<-c(all.atType.scores,data$V6)
114 all.resCount.scores<-c(all.resCount.scores,data$V9)
115 all.resType.scores<-c(all.resType.scores,data$V12)
116
117
118 }
119
120 # correlation of z-scores atom type vs atom count
121 corr<-cor(all.atCount.scores,all.atType.scores,method="spearman")
122 plot(all.atCount.scores,all.atType.scores,xlab="z-score atom count",ylab="z-score atom type",
123 main="correlation of z-scores atom type vs count")
124 legend(x=max(all.atCount.scores)-2,y=min(all.atType.scores)+1,corr,col=3,bty="n")
125 # correlation of z-scores res type vs res count
126 corr<-cor(all.resCount.scores,all.resType.scores,method="spearman")
127 plot(all.resCount.scores,all.resType.scores,xlab="z-score res count",ylab="z-score res type",
128 main="correlation of z-scores res type vs count")
129 legend(x=max(all.resCount.scores)-2,y=min(all.resType.scores)+1,corr,col=2,bty="n")
130 dev.off()