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root/owl/trunk/src/owl/decoyScoring/decoys_summary_plots.R
Revision: 1005
Committed: Wed Mar 31 12:29:26 2010 UTC (10 years ago) by hstehr
Original Path: trunk/src/owl/core/structure/decoyScoring/decoys_summary_plots.R
File size: 4413 byte(s)
Log Message:
refactoring: renaming proteinstructure to structure and tools to util; moving connections,features,runners,sequence,structure,util to owl.core
Line User Rev File contents
1 duarte 957
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()