view test-data/ceas_out1.log @ 3:df54f8fcc7a7 draft

Version 1.0.2-3: move to conda-based dependency resolution.

ceas -- 0.9.9.7 (package version 1.0.2)
INFO  @ Tue, 23 Jun 2015 09:12:22: 
# ARGUMENTS: 
# name = ceas
# gene annotation table = galGal3.refGene
# BED file = ceas_in.bed
# WIG file = None
# extra BED file = None
# ChIP annotation = On
# gene-centered annotation =  On
# average profiling = Off
# dump profiles = Off
# re-annotation for genome background (ChIP region annotation) = False
# promoter sizes (ChIP region annotation) = 1000,2000,3000 bp
# downstream sizes (ChIP region annotation) = 1000,2000,3000 bp
# bidrectional promoter sizes (ChIP region annotation) = 2500,5000 bp
# span size (gene-centered annotation) = 3000 bp 
INFO  @ Tue, 23 Jun 2015 09:12:22: #1 read the gene table... 
INFO  @ Tue, 23 Jun 2015 09:12:22: #2 read the bed file of ChIP regions... 
INFO  @ Tue, 23 Jun 2015 09:12:22: #3 perform gene-centered annotation... 
INFO  @ Tue, 23 Jun 2015 09:12:22: #4 See ceas.xls for gene-centered annotation! 
INFO  @ Tue, 23 Jun 2015 09:12:22: #5 read the pre-computed genome bg annotation... 
INFO  @ Tue, 23 Jun 2015 09:12:22: #6 perform ChIP region annotation... 
INFO  @ Tue, 23 Jun 2015 09:12:22: #7 write a R script of ChIP region annotation... 

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> # ARGUMENTS: 
> # name = ceas
> # gene annotation table = galGal3.refGene
> # BED file = ceas_in.bed
> # WIG file = None
> # extra BED file = None
> # ChIP annotation = On
> # gene-centered annotation =  On
> # average profiling = Off
> # dump profiles = Off
> # re-annotation for genome background (ChIP region annotation) = False
> # promoter sizes (ChIP region annotation) = 1000,2000,3000 bp
> # downstream sizes (ChIP region annotation) = 1000,2000,3000 bp
> # bidrectional promoter sizes (ChIP region annotation) = 2500,5000 bp
> # span size (gene-centered annotation) = 3000 bp
> pdf("ceas.pdf",height=11.5,width=8.5)
> 
> # 09:12:22 Tue, 23 Jun 2015
> # 
> # ChIP annotation
> # 
> 
> 
> # 
> # Chromosomal Distribution
> # 
> 
> par(mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2))
> r0<-c(100.0)
> r1<-c(100.0)
> height<-rbind(r0,r1)
> names=c("26")
> mp<-barplot(height=height,names=names,beside=TRUE,horiz=TRUE,col=c("#5FA1C1","#EB9D86"),main="Chromosomal Distribution of ChIP Regions",xlab="Percentage %",ylab="Chromosome",border=FALSE,xlim=c(0.000000,183.333333),cex.names=1)
> text(x=c(100.0),y=mp[1,],label=c("100.0 %"),pos=4,offset=0.2,cex=0.9)
> text(x=c(100.0),y=mp[2,],label=c("100.0 % (<=4.9e-324)"),pos=4,offset=0.2,cex=0.9)
> legend("right",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
> 
> # 
> # Promoter,Bipromoter,Downstream, Gene and Regions of interest
> # 
> 
> par(mfrow=c(4, 1),mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2))
> r0<-c(1.8532425688606797, 3.616851183410451, 5.322318854623416)
> r1<-c(0.0, 0.0, 0.0)
> height<-rbind(r0,r1)
> names=c("<=1000 bp","<=2000 bp","<=3000 bp")
> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Promoter",ylab="Percentage %",border=FALSE,ylim=c(0.000000,9.757585),cex.names=1)
> text(x=mp[1,],y=c(1.8532425688606797, 3.616851183410451, 5.322318854623416),label=c("1.9 %","3.6 %","5.3 %"),pos=3,offset=0.2)
> text(x=mp[2,],y=c(0.0, 0.0, 0.0),label=c("0.000 %
+ (0.981)","0.000 %
+ (0.964)","0.000 %
+ (0.947)"),pos=3,offset=0.2)
> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
> r0<-c(0.03876062889120376, 0.03876062889120376)
> r1<-c(0.0, 0.0)
> height<-rbind(r0,r1)
> names=c("<=2500 bp","<=5000 bp")
> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Bidirectional Promoter",ylab="Percentage %",border=FALSE,ylim=c(0.000000,0.071061),cex.names=1)
> text(x=mp[1,],y=c(0.03876062889120376, 0.03876062889120376),label=c("0.04 %","0.04 %"),pos=3,offset=0.2)
> text(x=mp[2,],y=c(0.0, 0.0),label=c("0.000 %
+ (1.000)","0.000 %
+ (1.000)"),pos=3,offset=0.2)
> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
> r0<-c(1.8290171758036773, 3.4690762857627364, 4.980740812519683)
> r1<-c(0.0, 0.0, 0.0)
> height<-rbind(r0,r1)
> names=c("<=1000 bp","<=2000 bp","<=3000 bp")
> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Downstream",ylab="Percentage %",border=FALSE,ylim=c(0.000000,9.131358),cex.names=1)
> text(x=mp[1,],y=c(1.8290171758036773, 3.4690762857627364, 4.980740812519683),label=c("1.8 %","3.5 %","5.0 %"),pos=3,offset=0.2)
> text(x=mp[2,],y=c(0.0, 0.0, 0.0),label=c("0.000 %
+ (0.982)","0.000 %
+ (0.965)","0.000 %
+ (0.950)"),pos=3,offset=0.2)
> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
> r0<-c(0.2034933016788197, 1.3978051793890356, 2.359553283752029, 19.734005184234114, 23.694856949054)
> r1<-c(0.0, 0.0, 0.0, 0.0, 0.0)
> height<-rbind(r0,r1)
> names=c("5'UTR","3'UTR","Coding Exon","Intron","All")
> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Gene",ylab="Percentage %",border=FALSE,ylim=c(0.000000,43.440571),cex.names=1)
> text(x=mp[1,],y=c(0.2034933016788197, 1.3978051793890356, 2.359553283752029, 19.734005184234114, 23.694856949054),label=c("0.2 %","1.4 %","2.4 %","19.7 %","23.7 %"),pos=3,offset=0.2)
> text(x=mp[2,],y=c(0.0, 0.0, 0.0, 0.0, 0.0),label=c("0.000 %
+ (0.998)","0.000 %
+ (0.986)","0.000 %
+ (0.976)","0.000 %
+ (0.803)","0.000 %
+ (0.763)"),pos=3,offset=0.2)
> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n")
> 
> # 
> # Distribution of Genome and ChIP regions over cis-regulatory element
> # Note that the x may be modified for better graphics in case a value is too small
> # Thus, look at the labels of the pie chart to get the real percentage values
> # 
> 
> par(mfcol=c(2, 2),mar=c(3, 3, 4, 2.8),oma=c(4, 2, 4, 2))
> x<-c(0.018532,0.017055,0.016037,0.017830,0.015092,0.014051,0.010000,0.013833,0.023014,0.192592,0.670292)
> pie(x=x,labels=c("1.9 %","1.7 %","1.6 %","1.8 %","1.5 %","1.4 %","0.2 %","1.4 %","2.3 %","19.3 %","67.0 %"),main="Genome",col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),clockwise=TRUE,border=FALSE,radius=0.9,cex=0.8,init.angle=90,density=100)
> x<-c(0.000000,1.000000)
> y<-c(0.000000,1.000000)
> plot(x, y,type="n",main="",xlab="",ylab="",frame=FALSE,axes=FALSE,xaxt="s",yaxt="s")
> legend("top",legend=c("Promoter (<=1000 bp): 1.9 %","Promoter (1000-2000 bp): 1.7 %","Promoter (2000-3000 bp): 1.6 %","Downstream (<=1000 bp): 1.8 %","Downstream (1000-2000 bp): 1.5 %","Downstream (2000-3000 bp): 1.4 %","5'UTR: 0.2 %","3'UTR: 1.4 %","Coding exon: 2.3 %","Intron: 19.3 %","Distal intergenic: 67.0 %"),col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),pch=15,bty="n")
> x<-c(0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,1.000000)
> pie(x=x,labels=c("0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","100.0 %"),main="ChIP",col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),clockwise=TRUE,border=FALSE,radius=0.9,cex=0.8,init.angle=90,density=100)
> x<-c(0.000000,1.000000)
> y<-c(0.000000,1.000000)
> plot(x, y,type="n",main="",xlab="",ylab="",frame=FALSE,axes=FALSE,xaxt="s",yaxt="s")
> legend("top",legend=c("Promoter (<=1000 bp): 0.000 %","Promoter (1000-2000 bp): 0.000 %","Promoter (2000-3000 bp): 0.000 %","Downstream (<=1000 bp): 0.000 %","Downstream (1000-2000 bp): 0.000 %","Downstream (2000-3000 bp): 0.000 %","5'UTR: 0.000 %","3'UTR: 0.000 %","Coding exon: 0.000 %","Intron: 0.000 %","Distal intergenic: 100.0 %"),col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),pch=15,bty="n")
> 
> # 
> # ChIP regions over the genome
> # 
> 
> par(mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2))
> layout(matrix(c(1, 0, 2, 2), 2, 2, byrow = TRUE),widths=c(1, 1),heights=c(1, 5))
> x<-c(0.000000,2.515610)
> y<-c(0.000000,1.000000)
> plot(x, y,type="n",main="Distribution of Peak Heights",xlab="",ylab="",xlim=c(0.000000,2.515610),ylim=c(0.000000,1.000000),frame=FALSE,xaxt="s",yaxt="n",cex=0.9)
> x<-c(0.000000,2.515610,2.515610,0.000000)
> y<-c(0.000000,0.000000,1.000000,1.000000)
> polygon(x,y,col=c("black"))
> x <- c(0.000000,0.169726,0.339451,0.509177,0.678903,0.848628,1.018354,1.188079,1.357805,1.527531,1.697256,1.866982,2.036708,2.206433,2.376159)
> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.800000)
> lines(x, y,xlim=c(0, 2.51561),ylim=c(0, 1),type="l",col=c("cyan"),lwd=2)
> x<-c(4119129.000000,4119130.000000)
> y<-c(0.855556,1.144444)
> plot(x, y,type="n",main="ChIP Regions (Peaks) over Chromosomes",xlab="Chromosome Size (bp)",ylab="Chromosome",xlim=c(4119129.000000,4119130.000000),ylim=c(0.855556,1.144444),frame=FALSE,xaxt="s",yaxt="n")
> start <- c(4119129)
> end <- c(4119130)
> vals <- c(2.51561)
> vals[vals > 2.51561] <- 2.51561
> vals[vals < 0] <- 0
> heights <- 0.288889 * ((vals - 0)/(2.51561 - 0)) + 0.855555555556
> for (i in 1:length(heights)) {
+ 	polygon(x=c(start[i], end[i], end[i], start[i]), y=c(0.855555555556, 0.855555555556, heights[i], heights[i]), col=c("#CC0000"), border=c("#CC0000"))
+ }
> mtext("26",side=2,line=0,outer=FALSE,at=1.0)
> dev.off()
null device 
          1 
> 
INFO  @ Tue, 23 Jun 2015 09:12:22: #... cong! See ceas.pdf for the graphical results of CEAS!