From noreply at r-forge.r-project.org  Wed Apr 10 15:49:55 2013
From: noreply at r-forge.r-project.org (noreply at r-forge.r-project.org)
Date: Wed, 10 Apr 2013 15:49:55 +0200 (CEST)
Subject: [Patchwork-commits] r167 - www
Message-ID: <20130410134955.81AD718477E@r-forge.r-project.org>

Author: sebastian_d
Date: 2013-04-10 15:49:55 +0200 (Wed, 10 Apr 2013)
New Revision: 167

Modified:
   www/cg_resu.php
   www/intro.php
Log:
add publikation info to homepage

Modified: www/cg_resu.php
===================================================================
--- www/cg_resu.php	2013-03-19 10:43:37 UTC (rev 166)
+++ www/cg_resu.php	2013-04-10 13:49:55 UTC (rev 167)
@@ -10,7 +10,8 @@
 
 The clusters in the plot display regions of certain allelic constitution and copy number.
 The copy number increases along the Coverage axis while paternal/maternal allele ratio
-becomes less balanced along the Allelic Imbalance axis.<br /><br />
+becomes less balanced along the Allelic Imbalance axis. We can use this information to determine the clusters probable copy number
+and allele content.<br /><br />
 
 The chromosome in question is colored against a background of the complete genome in grey.
 A colored circles gradient and size correlate with its segments position and size on the
@@ -18,15 +19,11 @@
  The circles are semi-transparent so a darker hue, both for colored and grey,
  indicate greater amount of genomic content in that region.<br /><br />
 
-We know that each cluster has a certain copy number and allele content and we know that
-a human is usually diploid (copy number 2, heterozygous). Finally we know that the average
+We know that each cluster has a certain copy number and allele content and we know that the average
 copy number of the genome in question is at position 1 on the Coverage axis.
-We can use this information to determine the clusters probable copy number
-and allele content.<br /><br />
-
 The far left cluster is the deletions, copy number 1. After copy number 1 we
-see two clusters, albeit the lower one does not have much content. This is pretty
-close to Coverage of 1 and as such they are most likely the two allelic states of copy number 2.
+see two clusters, albeit the lower one does not have much content. They are most likely
+the two allelic states of copy number 2.
 Continuing with this reasoning the next set of clusters is copy number 3, etc.
 
 This arrangement of the genome is easier to see from one of the plots generated by

Modified: www/intro.php
===================================================================
--- www/intro.php	2013-03-19 10:43:37 UTC (rev 166)
+++ www/intro.php	2013-04-10 13:49:55 UTC (rev 167)
@@ -19,7 +19,7 @@
 If you have any feedback or questions please do not hesitate to contact us!
 
 
-<br /><br />
+<br /><br /><br /><br /><br />
 <div style="text-align:center;">
 
 <a href="http://www.medsci.uu.se" accesskey="1" tabindex="2">
@@ -27,14 +27,16 @@
 			src="http://www.medsci.uu.se/digitalAssets/22/22975_UU_logga_transp.png" 
 			alt="Medical Sciences" title="Medical Sciences Uppsala University"></a>
 </div>
+<br /><br /><br />
 
 
-<!--
 <div style="text-align:center;">
 <h4>Publication</h4>
 
-M Mayrhofer,S DiLorenzo,A Isaksson <br />
-?r detta en bra plats att ha publikationen? <br />
-Aftonbladet,25,DOI:23442342
+<a href="http://genomebiology.com/2013/14/3/R24/abstract" style="text-decoration:none;">
+Patchwork: allele-specific copy number analysis of whole genome sequenced tumor tissue</a> <br />
+Markus Mayrhofer, Sebastian DiLorenzo and Anders Isaksson <br />
+Genome Biology 2013, 14:R24 doi:10.1186/gb-2013-14-3-r24 <br />
+Published: 25 March 2013
 </div>
--->
+


From noreply at r-forge.r-project.org  Mon Apr 22 16:29:39 2013
From: noreply at r-forge.r-project.org (noreply at r-forge.r-project.org)
Date: Mon, 22 Apr 2013 16:29:39 +0200 (CEST)
Subject: [Patchwork-commits] r168 - in pkg: . TAPS TAPS/R TAPS/man
Message-ID: <20130422142939.1C122184FF2@r-forge.r-project.org>

Author: sebastian_d
Date: 2013-04-22 16:29:38 +0200 (Mon, 22 Apr 2013)
New Revision: 168

Added:
   pkg/TAPS/
   pkg/TAPS/.Rhistory
   pkg/TAPS/DESCRIPTION
   pkg/TAPS/NAMESPACE
   pkg/TAPS/R/
   pkg/TAPS/R/TAPS.r
   pkg/TAPS/R/sysdata.rda
   pkg/TAPS/R/zzz.r
   pkg/TAPS/man/
   pkg/TAPS/man/TAPS_call.Rd
   pkg/TAPS/man/TAPS_plot.Rd
   pkg/TAPS/man/TAPS_region.Rd
Log:
Initial addition of TAPS to Rforge

Added: pkg/TAPS/.Rhistory
===================================================================
--- pkg/TAPS/.Rhistory	                        (rev 0)
+++ pkg/TAPS/.Rhistory	2013-04-22 14:29:38 UTC (rev 168)
@@ -0,0 +1,512 @@
+dupcor<-duplicateCorrelation(Data, design, block=block)
+dupcor$consensus.correlation
+cont.matrix<- makeContrasts(C_ATRAvsA=C_ATRA-C, IKK1_ATRAvsIKK1=IKK1_ATRA-IKK1, IKK2_ATRAvsIKK2=IKK2_ATRA-IKK2, levels=design)
+fit<-lmFit(Data,design, block=block, correlation=dupcor$consensus.correlation)
+fit2<-contrasts.fit(fit, cont.matrix)
+fit2<-eBayes(fit2)
+summary(decideTests(fit2))
+vennDiagram(fit2)
+results=decideTests(fit2)
+vennDiagram(results)
+cont.matrix<- makeContrasts(C_ATRAvsC=C_ATRA-C, IKK1_ATRAvsIKK1=IKK1_ATRA-IKK1, IKK2_ATRAvsIKK2=IKK2_ATRA-IKK2, levels=design)
+fit<-lmFit(Data,design, block=block, correlation=dupcor$consensus.correlation)
+fit2<-contrasts.fit(fit, cont.matrix)
+fit2<-eBayes(fit2)
+summary(decideTests(fit2))
+list1<-topTable(fit2, coef=1, number="all")
+list2<-topTable(fit2, coef=2, number="all")
+list3<-topTable(fit2, coef=3, number="all")
+write.table(list1, file="C_ATRAvsC.txt", sep="\t")
+write.table(list2, file="IKK1_ATRAvsIKK1.txt", sep="\t")
+write.table(list3, file="IKK2_ATRAvsIKK2.txt", sep="\t")
+vennDiagram(results)
+results=decideTests(fit2)
+vennDiagram(results)
+savehistory("E:/PROJEKT/PA126/hist120605.Rhistory")
+save.image("E:/PROJEKT/PA126/Analysis120605.RData")
+ls()
+design2=cbind(Control=c(1,1,1,1,0,0,0,0,0,0,0,0), IKK=c(0,0,0,0,1,1,1,1,1,1,1,1), ATRA=c(0,1,0,1,0,1,0,1,0,1,0,1))
+design2=cbind(Control=c(1,1,1,1,0,0,0,0,0,0,0,0), IKK=c(0,0,0,0,1,1,1,1,1,1,1,1))
+design3=cbind(Untreated=c(1,0,1,0,1,0,1,0,1,0,1,0), ATRA=c(0,1,0,1,0,1,0,1,0,1,0,1))
+design2
+design3
+designFinal=model.matrix(~design2+design3)
+designFinal
+colnames(design) <- c("Fem.Case","Male.Case",
+"UnTreated","ATRA")
+colnames(designFinal)=c("Control", "IKK", "Untreated", "ATRA")
+colnames(designFinal)=c("Control", "IKK", "Untreated", "ATRA")
+designFinal=model.matrix(~0+design2:design3)
+designFinal
+colnames(designFinal)=c("Control", "IKK", "Untreated", "ATRA")
+designFinal
+colnames(designFinal)=c("Control.Untreated", "IKK.Untreated", "Control.ATRA", "IKK.ATRA")
+designFinal
+contrastI=makeContrasts((IKK.ATRA-Control.ATRA)-(IKK.Untreated-Control.Untreated), levels=designFinal)
+library(limma)
+contrastI=makeContrasts((IKK.ATRA-Control.ATRA)-(IKK.Untreated-Control.Untreated), levels=designFinal)
+contrastI
+source('Y:/tillHanna/COLON/Gruppanalys_Colon_apr.r')
+setwd("E:/PROJEKT/PA126")
+Data<-read.table("RMAnorm_wA_main_data.txt", sep="\t")
+block=c(1,1,2,2,1,1,2,2,1,1,2,2,1,1,2,2)
+block
+block=c(1,1,2,2,1,1,2,2,1,1,2,2)
+design=cbind(C=c(1,0,1,0,0,0,0,0,0,0,0,0), C_ATRA=c(0,1,0,1,0,0,0,0,0,0,0,0), IKK1=c(0,0,0,0,1,0,1,0,0,0,0,0), IKK1_ATRA=c(0,0,0,0,0,1,0,1,0,0,0,0), IKK2=c(0,0,0,0,0,0,0,0,1,0,1,0), IKK2_ATRA=c(0,0,0,0,0,0,0,0,0,1,0,1))
+deisgn
+design
+dupcor<-duplicateCorrelation(Data, design, block=block)
+library(limma)
+dupcor<-duplicateCorrelation(Data, design, block=block)
+dupcor$consensus.correlation
+cont.matrix<- makeContrasts(C_ATRAvsA=C_ATRA-C, IKK1_ATRAvsIKK1=IKK1_ATRA-IKK1, IKK2_ATRAvsIKK2=IKK2_ATRA-IKK2, levels=design)
+fit<-lmFit(Data,design, block=block, correlation=dupcor$consensus.correlation)
+fit2<-contrasts.fit(fit, cont.matrix)
+fit2<-eBayes(fit2)
+summary(decideTests(fit2))
+vennDiagram(fit2)
+results=decideTests(fit2)
+vennDiagram(results)
+cont.matrix<- makeContrasts(C_ATRAvsC=C_ATRA-C, IKK1_ATRAvsIKK1=IKK1_ATRA-IKK1, IKK2_ATRAvsIKK2=IKK2_ATRA-IKK2, levels=design)
+fit<-lmFit(Data,design, block=block, correlation=dupcor$consensus.correlation)
+fit2<-contrasts.fit(fit, cont.matrix)
+fit2<-eBayes(fit2)
+summary(decideTests(fit2))
+list1<-topTable(fit2, coef=1, number="all")
+list2<-topTable(fit2, coef=2, number="all")
+list3<-topTable(fit2, coef=3, number="all")
+write.table(list1, file="C_ATRAvsC.txt", sep="\t")
+write.table(list2, file="IKK1_ATRAvsIKK1.txt", sep="\t")
+write.table(list3, file="IKK2_ATRAvsIKK2.txt", sep="\t")
+vennDiagram(results)
+results=decideTests(fit2)
+vennDiagram(results)
+savehistory("E:/PROJEKT/PA126/hist120605.Rhistory")
+dupcorF<-duplicateCorrelation(Data, designFinal, block=block)
+dupcorF$consensus.correlation
+fitF<-lmFit(Data,designFinal, block=block, correlation=dupcorF$consensus.correlation)
+fitF2<-contrasts.fit(fitF, contrastI)
+fitF2<-eBayes(fitF2)
+summary(decideTests(fitF2))
+topTable(fitF2)
+listF=topTable(fitF2, coef=1, number='all')
+write.table(listF, file="Interaction.txt", sep="\t")
+save.image("E:\\PROJEKT\\PA126\\Analysis120827.RData")
+contrastI
+setwd("E:/PROJEKT/PA071_AS_HGK/Analysis2012")
+Data=read.table("Results_RMAnorm_50prov_uAFFX_120830_data.txt", sep="\t")
+design=cbind(NK=c(1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), NT=c(0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), MDK=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0), MDT=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1))
+design=cbind(NK=c(1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), NT=c(0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), MDK=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0), MDT=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1))
+design
+library(limma)
+cont.matrix<- makeContrasts(MDKvNK_VS_MDTvNT=(MDK-NK)-(MDT-NT)), levels=design)
+cont.matrix<- makeContrasts(MDKvNK_VS_MDTvNT=(MDK-NK)-(MDT-NT), levels=design)
+cont.matrix
+fit<-lmFit(Data,design)
+fit2<-contrasts.fit(fit, cont.matrix)
+fit2<-eBayes(fit2)
+summary(decideTests(fit2))
+topTable(fit2)
+fitR<-lmFit(Data,design, method="robust")
+fitR2<-contrasts.fit(fitR, cont.matrix)
+fitR2<-eBayes(fitR2)
+summary(decideTests(fitR2))
+topTable(fitR2)
+list=topTable(fitR2, coef=1, number='all')
+write.table(list, file="MDKvNK_VS_MDTvNT", sep"\t")
+write.table(list, file="MDKvNK_VS_MDTvNT", sep="\t")
+write.table(list, file="MDKvNK_VS_MDTvNT.txt", sep="\t")
+DataA5=read.table("Results_RMAnorm_50prov_A5_uAFFX_120830_data.txt", sep="\t")
+fitR_A5<-lmFit(DataA5,design, method="robust")
+fitR_A5_2<-contrasts.fit(fitR_A5, cont.matrix)
+fitR_A5_2<-eBayes(fitR_A5_2)
+summary(decideTests(fitR_A5_2))
+topTable(fitR_A5_2)
+topTable(fitR_A5_2, number=30)
+listA5=topTable(fitR_A5_2, coef=1, number='all')
+write.table(listA5, file="MDKvNK_VS_MDTvNT_A5.txt", sep="\t")
+savehistory("E:/PROJEKT/PA071_AS_HGK/Analysis2012/hist120830.Rhistory")
+load("C:/Users/hagor/Desktop/Analysis120918.RData")
+#quartz(file='genome_dups.png',width=14,height=14,dpi=300,type='png')
+png(file='genome_dups_Recurrence.png', width=1000,height=1000)
+layout(matrix(1:1,nrow=1,byrow=T), widths=1,heights=1)
+## Recurrence
+plot_genome_dup('Recurrence',sampleData,sampleData$Recidiv=='recidiv' | sampleData$Stadium==4 )
+## Chromothripsis
+#plot_genome_dup('Chromothripsis',sampleData,sampleData$Fragmented!='')
+## BRAF 600
+#plot_genome_dup('BRAF 600',sampleData,sampleData$BRAF_600!='wt')
+## KRAS 12 13
+#plot_genome_dup('KRAS 12/13',sampleData,sampleData$KRAS_12_13!='wt')
+## KRAS 61
+#plot_genome_dup('KRAS 61',sampleData,sampleData$KRAS_61!='wt')
+## MSI-L
+#plot_genome_dup('MSI-L',sampleData,sampleData$MSI=='MSI-L')
+## MSI-H
+#plot_genome_dup('MSI-H',sampleData,sampleData$MSI=='MSI-H')
+## PI3K Exon 9
+#plot_genome_dup('PI3K-ex9',sampleData,sampleData$PI3K_ex9!='' & sampleData$PI3K_ex9!='wt')
+## PI3K Exon 20
+#plot_genome_dup('PI3K-ex20',sampleData,sampleData$PI3K_ex20!='' & sampleData$PI3K_ex20!='wt')
+dev.off()
+mss=sampleData$MSI!='MSI-H'
+dim(mss)
+mss
+sampleData[mss]
+mss[sampleData]
+png(file='genome_dups_Recurrence.png', width=1000,height=1000)
+layout(matrix(1:1,nrow=1,byrow=T), widths=1,heights=1)
+## Recurrence
+plot_genome_dup('Recurrence',sampleData$MSI!='MSI-H',sampleData$Recidiv=='recidiv' | sampleData$Stadium==4 )
+## Chromothripsis
+#plot_genome_dup('Chromothripsis',sampleData,sampleData$Fragmented!='')
+## BRAF 600
+#plot_genome_dup('BRAF 600',sampleData,sampleData$BRAF_600!='wt')
+## KRAS 12 13
+#plot_genome_dup('KRAS 12/13',sampleData,sampleData$KRAS_12_13!='wt')
+## KRAS 61
+#plot_genome_dup('KRAS 61',sampleData,sampleData$KRAS_61!='wt')
+## MSI-L
+#plot_genome_dup('MSI-L',sampleData,sampleData$MSI=='MSI-L')
+## MSI-H
+#plot_genome_dup('MSI-H',sampleData,sampleData$MSI=='MSI-H')
+## PI3K Exon 9
+#plot_genome_dup('PI3K-ex9',sampleData,sampleData$PI3K_ex9!='' & sampleData$PI3K_ex9!='wt')
+## PI3K Exon 20
+#plot_genome_dup('PI3K-ex20',sampleData,sampleData$PI3K_ex20!='' & sampleData$PI3K_ex20!='wt')
+dev.off()
+library(xps)
+setRepositories()
+install.packages("xps")
+library(xps)
+load("C:/Users/hagor/Dropbox/Colon_snp6/RESULTAT/Analysis121002.RData")
+ls()
+Model=glm(poor~loss1p, family=binomial)
+m <- summary(Model)
+m
+help(glm)
+save.image("C:/Users/hagor/Dropbox/Colon_snp6/RESULTAT/Analysis121002.RData")
+load("E:/PROJEKT/PA106_AS_HGK/AgeStudyOkt2012/Analysis121030.RData")
+block
+design
+dupcor$consensus.correlation
+fitB<-lmFit(Data,design, block=block, correlation=dupcor$consensus.correlation)
+help(topTable)
+load("C:/Users/hagor/Dropbox/Colon_snp6/Analysis121119.RData")
+setwd("C:/Users/hagor/Dropbox/Colon_snp6")
+source('TAPS_may2012.r')
+sampleData <- load.txt('allSamples.txt')
+load('ideogram.Rdata')
+chroms=ideogram
+#load('chroms.Rdata') #### Varning! En annan chroms har nog anv??nts till frekvensplottarna...
+chroms$before <- temp <- 0
+chroms$Chromosome=chroms$chr
+for (i in 1:24) {
+chroms$before[chroms$chr==chrom_ucsc(i)] = temp
+temp=temp+ideogram$length[chroms$chr==chrom_ucsc(i)]
+}
+#load('ideogram.Rdata')
+nSamples=nrow(sampleData)
+#for (i in 1:ncol(sampleData)) sampleData[,i][sampleData[,i]==''] <- NA
+## Load and parse all samples
+samples <- chromwise <- meanCns <- meanImba <- maxfrag <- meanFrag <- maxfragchr <- fragmat <- LCn4mCn2 <- LCn6mCn2 <- LCn6mCn2 <-LTotal <- NULL
+for (i in 1:nSamples) {
+table <- load.txt(paste('allCNs/',sampleData$Sample[i],'_segmentCN.txt',sep=''))
+table$n <- i
+table$name <- as.character(sampleData$Sample[i])
+table$stage=sampleData$Stadium[i]
+table$rec=sampleData$Recidiv[i]
+table$kras1213=sampleData$KRAS_12_13[i]
+table$kras61=sampleData$KRAS_61[i]
+table$braf=sampleData$BRAF_600[i]
+table$msi=sampleData$MSI[i]
+table$nodes=sampleData$Antal[i]
+table$pi3k9=sampleData$PI3K_ex9[i]
+table$pi3k20=sampleData$PI3K_ex20[i]
+## Calculate degree of Cn 4/6 mCn 2 for each sample
+LCn6mCn2[i]=sum(table$lengthMB[table$Cn==6 & table$mCn==2 & !is.na(table$mCn)])
+LCn4mCn2[i]=sum(table$lengthMB[table$Cn==4 & table$mCn==2 & !is.na(table$mCn)])
+LCn46mCn2[i]=LCn4mCn2[i]+LCn6mCn2[i]
+LTotal[i]=sum(table$lengthMB)
+## Calculate chrom-arm-wise Cn-changes.
+changes=ideogram; changes$p=0; changes$q=0; changes$maxfrag=0; changes$pmed=NA; changes$qmed=NA; changes$plocal=NA; changes$qlocal=NA;
+for (c in as.character(changes$chr)) {
+ix=changes$chr==c
+# p arm: breakpoints
+temp=table[table$Chromosome==c & table$End<ideogram$mid[ideogram$chr==c],]
+temp=temp[!is.na(temp$imba) & temp$snps>100,]
+if (nrow(temp)>1) {
+changes$p[ix]=sum(abs(temp$imba[1:(nrow(temp)-1)] - temp$imba[2:(nrow(temp))])>0.15)
+} else changes$p[ix]=c(0)
+# median copy number
+if (nrow(temp)>0) changes$pmed[ix]=weightedMedian(temp$Cn,temp$probes)
+# Localized chromothripsis (20MB window)
+maxp <- max <- end <- 0; while (end<ideogram$start[ix]) {
+t=temp[temp$End<end & temp$Start<end-20e6,]
+if (nrow(t)>1) {
+breaks=sum(abs(t$imba[1:(nrow(t)-1)] - t$imba[2:(nrow(t))])>0.15)
+} else breaks=0
+if (breaks>max) {
+max=breaks; maxp=end-10e6
+}
+end=end+1e6
+}; changes$plocal[ix]=max
+# q arm: breakpoints
+temp=table[table$Chromosome==c & table$Start>ideogram$mid[ideogram$chr==c],]
+temp=temp[!is.na(temp$imba) & temp$snps>100,]
+if (nrow(temp)>1) {
+changes$q[ix]=sum(abs(temp$imba[1:(nrow(temp)-1)] - temp$imba[2:(nrow(temp))])>0.05)
+} else changes$q[ix]=c(0)
+# median copy number
+if (nrow(temp)>0) changes$qmed[ix]=weightedMedian(temp$Cn,temp$probes)
+# Localized chromothripsis (20MB window)
+maxp <- max <-0; end <- ideogram$end[ix]; while (end<ideogram$length[ix]) {
+t=temp[temp$End<end & temp$Start<end-20e6,]
+if (nrow(t)>1) {
+breaks=sum(abs(t$imba[1:(nrow(t)-1)] - t$imba[2:(nrow(t))])>0.05)
+} else breaks=0
+if (breaks>max) {
+max=breaks; maxp=end-10e6
+}
+end=end+1e6
+}; changes$qlocal[ix]=max
+}
+changes$p=changes$p/ (changes$start) * 100e6
+changes$q=changes$q/ (changes$length-changes$end) * 100e6
+changes$maxfrag=apply(changes[,6:7],1,max)
+ix <- as.numeric(deChrom_ucsc(table$Chromosome)) <= 22
+table$meanCn <- meanCns[i] <- round(weightedMean(table$Cn[ix], table$lengthMB[ix]),2)
+table$meanImba <- meanImba[i] <- sum(table$lengthMB[table$Cn != table$mCn*2],na.rm=T) / 3000#1 - weightedMean(table$mCn[ix] / (table$Cn[ix]-table$mCn[ix]), table$length[ix])
+maxfrag[i] <- max(changes$maxfrag)
+maxfragchr[i] <- as.character(changes$chr[changes$maxfrag==maxfrag[i]][1])
+meanFrag[i] <- mean(mean(changes$p,na.rm=T),mean(changes$q,na.rm=T))
+samples <- rbind(samples,table)
+chromwise[[i]] = changes
+}
+sampleData$meanCn=meanCns
+sampleData$meanImba=meanImba
+sampleData$maxFrag=maxfrag
+sampleData$maxFragChr=maxfragchr
+sampleData$meanFrag=meanFrag
+## Load and parse all samples
+samples <- chromwise <- meanCns <- meanImba <- maxfrag <- meanFrag <- maxfragchr <- fragmat <- LCn4mCn2 <- LCn6mCn2 <- LCn46mCn2 <-LTotal <- NULL
+for (i in 1:nSamples) {
+table <- load.txt(paste('allCNs/',sampleData$Sample[i],'_segmentCN.txt',sep=''))
+table$n <- i
+table$name <- as.character(sampleData$Sample[i])
+table$stage=sampleData$Stadium[i]
+table$rec=sampleData$Recidiv[i]
+table$kras1213=sampleData$KRAS_12_13[i]
+table$kras61=sampleData$KRAS_61[i]
+table$braf=sampleData$BRAF_600[i]
+table$msi=sampleData$MSI[i]
+table$nodes=sampleData$Antal[i]
+table$pi3k9=sampleData$PI3K_ex9[i]
+table$pi3k20=sampleData$PI3K_ex20[i]
+## Calculate degree of Cn 4/6 mCn 2 for each sample
+LCn6mCn2[i]=sum(table$lengthMB[table$Cn==6 & table$mCn==2 & !is.na(table$mCn)])
+LCn4mCn2[i]=sum(table$lengthMB[table$Cn==4 & table$mCn==2 & !is.na(table$mCn)])
+LCn46mCn2[i]=LCn4mCn2[i]+LCn6mCn2[i]
+LTotal[i]=sum(table$lengthMB)
+## Calculate chrom-arm-wise Cn-changes.
+changes=ideogram; changes$p=0; changes$q=0; changes$maxfrag=0; changes$pmed=NA; changes$qmed=NA; changes$plocal=NA; changes$qlocal=NA;
+for (c in as.character(changes$chr)) {
+ix=changes$chr==c
+# p arm: breakpoints
+temp=table[table$Chromosome==c & table$End<ideogram$mid[ideogram$chr==c],]
+temp=temp[!is.na(temp$imba) & temp$snps>100,]
+if (nrow(temp)>1) {
+changes$p[ix]=sum(abs(temp$imba[1:(nrow(temp)-1)] - temp$imba[2:(nrow(temp))])>0.15)
+} else changes$p[ix]=c(0)
+# median copy number
+if (nrow(temp)>0) changes$pmed[ix]=weightedMedian(temp$Cn,temp$probes)
+# Localized chromothripsis (20MB window)
+maxp <- max <- end <- 0; while (end<ideogram$start[ix]) {
+t=temp[temp$End<end & temp$Start<end-20e6,]
+if (nrow(t)>1) {
+breaks=sum(abs(t$imba[1:(nrow(t)-1)] - t$imba[2:(nrow(t))])>0.15)
+} else breaks=0
+if (breaks>max) {
+max=breaks; maxp=end-10e6
+}
+end=end+1e6
+}; changes$plocal[ix]=max
+# q arm: breakpoints
+temp=table[table$Chromosome==c & table$Start>ideogram$mid[ideogram$chr==c],]
+temp=temp[!is.na(temp$imba) & temp$snps>100,]
+if (nrow(temp)>1) {
+changes$q[ix]=sum(abs(temp$imba[1:(nrow(temp)-1)] - temp$imba[2:(nrow(temp))])>0.05)
+} else changes$q[ix]=c(0)
+# median copy number
+if (nrow(temp)>0) changes$qmed[ix]=weightedMedian(temp$Cn,temp$probes)
+# Localized chromothripsis (20MB window)
+maxp <- max <-0; end <- ideogram$end[ix]; while (end<ideogram$length[ix]) {
+t=temp[temp$End<end & temp$Start<end-20e6,]
+if (nrow(t)>1) {
+breaks=sum(abs(t$imba[1:(nrow(t)-1)] - t$imba[2:(nrow(t))])>0.05)
+} else breaks=0
+if (breaks>max) {
+max=breaks; maxp=end-10e6
+}
+end=end+1e6
+}; changes$qlocal[ix]=max
+}
+changes$p=changes$p/ (changes$start) * 100e6
+changes$q=changes$q/ (changes$length-changes$end) * 100e6
+changes$maxfrag=apply(changes[,6:7],1,max)
+ix <- as.numeric(deChrom_ucsc(table$Chromosome)) <= 22
+table$meanCn <- meanCns[i] <- round(weightedMean(table$Cn[ix], table$lengthMB[ix]),2)
+table$meanImba <- meanImba[i] <- sum(table$lengthMB[table$Cn != table$mCn*2],na.rm=T) / 3000#1 - weightedMean(table$mCn[ix] / (table$Cn[ix]-table$mCn[ix]), table$length[ix])
+maxfrag[i] <- max(changes$maxfrag)
+maxfragchr[i] <- as.character(changes$chr[changes$maxfrag==maxfrag[i]][1])
+meanFrag[i] <- mean(mean(changes$p,na.rm=T),mean(changes$q,na.rm=T))
+samples <- rbind(samples,table)
+chromwise[[i]] = changes
+}
+sampleData$meanCn=meanCns
+sampleData$meanImba=meanImba
+sampleData$maxFrag=maxfrag
+sampleData$maxFragChr=maxfragchr
+sampleData$meanFrag=meanFrag
+LCn46mCn2
+Ratio=LCn46mCn2/LTotal
+Ratio=LCn46mCn2/LTotal
+SelectedRatio=Ratio[samples$msi!='MSI-H',]
+Ratio=LCn46mCn2/LTotal
+SelectedRatio=Ratio[samples$msi!='MSI-H']
+dim(SelectedRatio)
+SelectedRatio
+Ratio=LCn46mCn2/LTotal
+SelectedRatio=Ratio[Ratio(samples$msi!='MSI-H')]
+ratio=LCn46mCn2/LTotal
+selected<- samples[samples$msi!='MSI-H',]
+selected
+View(sampleData)
+View(sampleData)
+View(sampleData)
+View(sampleData)
+sampleData$MSI!='MSI-H'
+ratio[sampleData$MSI!='MSI-H']
+ratio=(LCn46mCn2/LTotal)*100
+selected<- ratio[sampleData$MSI!='MSI-H']
+hist(selected)
+hist(selected,50)
+MSS&MSI-L=selected
+MSSandMSIL=selected
+hist(MSSandMSIL,50)
+dim(selected)
+selected
+hist(selected,50)
+plot(sampleData$meanCn[sampelData$MSI!='MSI-H'], selected)
+plot(sampleData$meanCn[sampleData$MSI!='MSI-H'], selected)
+PercentageTemp=(LCn46mCn2/LTotal)*100
+Percentage<- ratio[sampleData$MSI!='MSI-H']
+hist(Percentage,50)
+PercentageTemp=(LCn46mCn2/LTotal)*100
+Percentage_Cn4or6_mCn2<- ratio[sampleData$MSI!='MSI-H']
+hist(Percentage_Cn4or6_mCn2,50)
+MeanCN_MSSandMSILsamples=sampleData$meanCn[sampleData$MSI!='MSI-H']
+plot(MeanCN_MSSandMSILsample, Percentage_Cn4or6_mCn2)
+MeanCN_MSSandMSILsamples=sampleData$meanCn[sampleData$MSI!='MSI-H']
+plot(MeanCN_MSSandMSILsamples, Percentage_Cn4or6_mCn2)
+sum(sampleData$meanCn[sampleData$MSI!='MSI-H')
+sampleData$meanCn[sampleData$MSI!='MSI-H'
+]
+PercentageTemp=(LCn46mCn2/LTotal)*100
+Percentage_Cn4or6_mCn2<- ratio[sampleData$MSI!='MSI-H']
+hist(Percentage_Cn4or6_mCn2,100)
+save.image("C:/Users/hagor/Dropbox/Colon_snp6/Analysis121119.RData")
+savehistory("C:/Users/hagor/Desktop/hist121120.Rhistory")
+view(SampleData)
+samples
+SampleData
+sampleData
+sampleData[,1]
+sampleData[1,]
+ratio
+sampleData[1,1:3]
+cbind(sampleData[,2],ratio)
+sampleData[1,1:3]
+cbind(sampleData[,3],ratio)
+cbind(sampleData[,1:3],ratio)
+RatioSampleinfo=cbind(sampleData[,1:3],ratio)
+RatioSampleinfo=cbind(sampleData[,1:3],ratio,sampleData$meanCn)
+RatioSampleinfo
+write.table(RatioSampleInfo, file="RatiosampleInfo.txt", sep="\t")
+RatioSampleInfo=cbind(sampleData[,1:3],ratio,sampleData$meanCn)
+write.table(RatioSampleInfo, file="RatiosampleInfo.txt", sep="\t")
+RatioSampleinfo
+save.image("C:/Users/hagor/Desktop/DuplicationAnalysis.RData")
+load("E:/PROJEKT/PA176/Analysis130123.RData")
+ls()
+library(limma)
+help(vennDiagram)
+result1=decideTests(fitB1R2, p=0.2)
+result2=decideTests(fitB2R2, p=0.2)
+result=decideTests(fit_R2, p=0.2)
+result
+Result=cbind(result1, result2, result)
+vennDiagram(Result)
+summary(result1)
+summary(result2)
+summary(result)
+A=[1,2,3,4,5]
+A=c(1,2,3,4,5)
+A
+hiat(A)
+hist(A)
+help(hist)
+B=A>3
+B
+load("E:/PROJEKT/PA176/Analysis130123.RData")
+ls()
+result1=decideTests(fitB1R2, p=0.2)
+result2=decideTests(fitB2R2, p=0.2)
+result=decideTests(fit_R2, p=0.2)
+Result=cbind(result1, result2, result)
+ResultNew=cbind(Result[,1:3])
+dim(Result)
+dim(ResultNew)
+ResultNew=Result[,1:3]
+dim(ResultNew)
+ResultNew=Result[,1:2]
+dim(ResultNew)
+ResultNew=Result[,1:2]
+dim(Result)
+ResultNew=Result[,1:2]
+dim(ResultNew)
+vennDiagram(ResultNew)
+load("E:/PROJEKT/PA183/AnalysBatch1and2/Analysis130311.RData")
+ls()
+results=decideTests(fitNew2)
+summary(results)
+topTable(ditNew2)
+topTable(fitNew2)
+topTable(fitNew2, coef=1)
+topTable(fitNew2, coef=2)
+topTable(fitNew2, coef=2, number=20)
+topTable(fitNew2, coef=2, number=30)
+setRepositories()
+install.packages("TAPS")
+install.packages('TAPS')
+library(TAPS)
+setRepositories()
+install.packages('TAPS')
+setwd("C:/Users/hagor/Dropbox/TAPSdev")
+setRepositories()
+install.packages('TAPS')
+setwd("C:/Users/hagor/Dropbox/TAPSdev")
+setwd("C:/Users/hagor/Dropbox/TAPSdev/TAPS")
+install.packages('TAPS')
+install.packages("C:/Users/hagor/Dropbox/TAPSdev/TAPS")
+install.packages("C:/Users/hagor/Dropbox/TAPSdev")
+install.packages("C:/Users/hagor/Dropbox/TAPSdev/TAPS.zip", repos = NULL)
+library(TAPS)
+setRepositories()
+install.packages("C:/Users/hagor/Dropbox/TAPSdev/TAPS.zip", repos = NULL)
+library(TAPS)
+library('TAPS)
+''
+)
+)')'
+library('TAPS')

Added: pkg/TAPS/DESCRIPTION
===================================================================
--- pkg/TAPS/DESCRIPTION	                        (rev 0)
+++ pkg/TAPS/DESCRIPTION	2013-04-22 14:29:38 UTC (rev 168)
@@ -0,0 +1,11 @@
+Package: TAPS
+Type: Package
+Title: Tumor Abberation Prediction Suite
+Version: 1.0
+Date: 2013-02-11
+Author: Markus Rasmussen, Hanna Goransson-Kultima
+Maintainer: Markus Rasmussen <Markus.Mayrhofer at medsci.uu.se>
+Description: Performs a allele-specific copy number analysis of array data.
+License: GPL-2
+Depends: R (>= 2.10), DNAcopy, stats, fields, affxparser
+Packaged: 2013-03-22 10:05:27 UTC; S_D

Added: pkg/TAPS/NAMESPACE
===================================================================
--- pkg/TAPS/NAMESPACE	                        (rev 0)
+++ pkg/TAPS/NAMESPACE	2013-04-22 14:29:38 UTC (rev 168)
@@ -0,0 +1,5 @@
+export(TAPS_plot,
+		TAPS_call,
+		TAPS_region)
+
+

Added: pkg/TAPS/R/TAPS.r
===================================================================
--- pkg/TAPS/R/TAPS.r	                        (rev 0)
+++ pkg/TAPS/R/TAPS.r	2013-04-22 14:29:38 UTC (rev 168)
@@ -0,0 +1,3090 @@
+
+TAPS_plot <- function(directory=NULL,#xlim=c(-1,2),ylim=c(0,1),
+  bin=400) {
+#Automatically check, and if needed install, packages stats and fields
+
+#list.of.packages <- c("stats", "fields")
+#new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
+#if(length(new.packages)) install.packages(new.packages)
+  
+if (is.null(directory))
+  {
+  cat("No directory supplied, using working directory.")
+  directory = "."
+  #cat("You have not assigned a directory containing one or more folders of samples for TAPS_plot to execute. \n")
+  #cat("Example: \"/user/mysamples/\" or, to run it in your current working directory, \".\" \n")
+  #directory = readline("Please supply such a directory now: ")
+  }
+
+## This function takes a directory as input, then builds short-segment TAPS scatter plots for each sample (subdirectory) in the directory.
+  setwd(directory)
+  subs <- getSubdirs()
+  if (is.null(subs)) {                                         ## check samples = subdirectories or a single sample = current directory
+    subs=thisSubdir()
+    setwd('..')
+  }
+    
+  # create SampleData file if there is none.
+  if (length(grep('SampleData.txt',dir()))==0) save.txt(data.frame(Sample=subs,cn2='',delta='',loh='',completed.analysis='no'),file='SampleData.txt')
+  
+  for (i in 1:length(subs)) try( {
+    setwd(subs[i])
+    name <- subs[i]
+    cat(' ..loading', subs[i])
+	
+	if(length(grep("*.cyhd.cychp",dir()))==1)				##cyhd sample
+		{
+		##read CYCHP file from ChAS with logratio info##
+		################################################
+		library(affxparser)
+
+		name=list.files(".",pattern="*.cychp")
+		temp=readCcg(name)
+		tempLog2=temp$dataGroups$ProbeSets$dataSets$CopyNumber$table[,1:4]
+		#Value=NULL
+		Start=tempLog2$Position
+		End=tempLog2$Position
+		Value=tempLog2$Log2Ratio
+		tempChr=tempLog2$Chromosome
+		nrows=dim(tempLog2)[1]
+		Chromosome=rep(NA,nrows)
+		for (i in 1:nrows) {
+			Chromosome[i]=paste('chr', tempChr[i], sep="")
+		  }
+
+
+		Log2=as.data.frame(cbind(Chromosome, Start, End, Value))
+
+		colnames(Log2)=c("Chromosome","Start","End","Value")
+
+    Log2$Chromosome = as.character(Log2$Chromosome)
+    Log2$Chromosome[Log2$Chromosome=="chr24"] = "chrX"
+    Log2$Chromosome[Log2$Chromosome=="chr25"] = "chrY"
+    Log2$Chromosome = as.factor(Log2$Chromosome)
+   
+    Log2$Start = as.integer(as.character(Log2$Start))
+    Log2$End = as.integer(as.character(Log2$End))
+    Log2$Value = as.numeric(as.character(Log2$Value))
+
+		##read txt file from ChAS with snp info##
+		#########################################
+
+		name=list.files(".",pattern="*.cyhd.txt")
+		tempalf=read.table(file=name, header=FALSE, sep="\t", skip=12)
+		SignalA=tempalf$V4
+		SignalB=tempalf$V5
+		tempChr=tempalf$V8
+		Start=tempalf$V9
+		End=tempalf$V9
+		Chromosome=Value=rep(NA,nrows)
+		nrows=dim(tempalf)[1]
+		for (i in 1:nrows) {
+			Value[i]=SignalB[i]/(SignalB[i]+SignalA[i])
+			Chromosome[i]=paste('chr', tempChr[i], sep="")
+		  }
+		  
+		alf=as.data.frame(cbind(Chromosome, Start, End, Value))
+
+		colnames(alf)=c("Chromosome","Start","End","Value")
+
+		alf$Start = as.integer(as.character(alf$Start))
+		alf$End = as.integer(as.character(alf$End))
+		alf$Value = as.numeric(as.character(alf$Value))
+		}
+	else
+		{
+		Log2 <- readLog2()                                     ## Log-R
+		alf <- readAlf()                                         ## Allele Frequency
+		}
+		
+    Log2=Log2[!is.nan(Log2$Value),]
+    Log2=Log2[!is.na(Log2$Value),]
+    alf=alf[!is.nan(alf$Value),]
+    alf=alf[!is.na(alf$Value),]
+    
+    segments <- readSegments()                                 ## segments if available (CBS recommended)
+   
+    cat(' ..processing')
+    if (is.null(segments)) {                                 ## segmentation using DNA-copy if needed (must then be installed)
+    segments <- segment_DNAcopy(Log2)
+    save.txt(segments,'_segments.txt') 
+    }
+
+    segments$Value <- segments$Value-median(Log2$Value)     ## Median-centering
+    Log2$Value <- Log2$Value-median(Log2$Value)             ## Median-centering
+
+
+    allRegions <- makeRegions(Log2, alf, segments)            ## Calculates necessary data for segments (all functions are in this file)
+    save(allRegions,file='allRegions.Rdata')
+    regs <- regsFromSegs(Log2,alf,segments,bin=bin,min=5)    ## Calculates the same data for shortened segments, very useful for visualization
+    save(regs,file='shortRegions.Rdata')
+
+    #Save for TAPS_region()
+    save(regs,Log2,alf,segments,file="TAPS_plot_output.Rdata")
+
+    #Clear warnings generated previously so hopefully I can see what is actually causing the program to fail.
+    #assign("last.warning", NULL, envir = baseenv())
+
+    cat('..plotting.\n')
+
+    OverviewPlot(regs$chr,regs$start,regs$end,regs$logs,regs$scores,ideogram=NULL,
+    as.character(Log2$Chromosome),Log2$Start,Log2$Value,as.character(alf$Chromosome),alf$Start,alf$Value,
+    name=name,xlim=c(-1,1.5),ylim=c(0,1))                
+
+    ## Chromosome-wise plots for manual analysis
+    regions=allRegions$regions
+
+    karyotype_chroms(regs$chr,regs$start,regs$end,regs$logs,regs$scores,ideogram=NULL,
+      as.character(Log2$Chromosome),Log2$Start,Log2$Value,as.character(alf$Chromosome),alf$Start,
+      alf$Value,name=name,xlim=c(-1,2),ylim=c(0,1))
+
+    cat('..done\n')
+    setwd('..')
+  }, silent=T)
+}
+###
+
+###
+TAPS_call <- function(directory=NULL,xlim=c(-1,1),ylim=c(0,1),minseg=1,maxCn=12) {
+## TAPS_call outputs the total and minor allele copy numbers of all segments as a text file, and as images for visual confirmation.
+## sampleInfo_TAPS.txt must be present in each sample folder. If TAPS_plot could not make a good guess of the Log-R of copy number 2 
+## and the Log-R difference to a deletion, you must interpret the scatter plots and edit sampleInfo_TAPS.txt.
+  if (is.null(directory))
+  {
+  cat("No directory supplied, using working directory.")
+  directory = "."
+  #cat("You have not assigned a directory containing one or more folders of samples for TAPS_call to execute. \n")
+  #cat("Example: \"/user/mysamples/\" or, to run it in your current working directory, \".\" \n")
+  #directory = readline("Please supply such a directory now: ")
+  }
+
+
+  setwd(directory)
+  #subs <- getSubdirs()
+
+  sampleData <- load.txt('SampleData.txt')
+  subs=as.character(sampleData$Sample)
+  #save.txt(sampleData,file='sampleData_old.csv')
+  
+  if (is.null(subs)) {
+    subs=thisSubdir()
+    setwd('..')
+  }
+  for (i in 1:length(subs)) if (sampleData$completed.analysis[i]=='no') {
+    setwd(subs[i])
+    name <- subs[i]
+    sampleInfo <- sampleData[sampleData$Sample==subs[i],]
+   if (nrow(sampleInfo)==1) {
+  
+    cat(' ..loading', subs[i])
+    Log2 <- readLog2()
+    alf <- readAlf(localDir)
+    segments <- readSegments()
+
+    segments$Value <- segments$Value-median(Log2$Value) 
+    Log2$Value <- Log2$Value-median(Log2$Value)
+
+    cat(' ..processing.\n')
+  
+    load('allRegions.Rdata')                            ## These were prepared in TAPS_plot
+    #allRegions <- makeRegions(Log2, alf, segments)
+
+    t <- findCNs(Log2,alf,allRegions,dmin=0.9,maxCn=maxCn,ceiling=1,sampleInfo=sampleInfo) ## estimates the Log-R and Allelic Imbalance Ration of all variants up to maxCn
+
+    u <- setCNs(allRegions,t$int,t$ai,maxCn)            ## Assigns copy number variant for all segments
+    allRegions$regions <- u$regions
+    save.txt(u$regions,file=paste(name,'_segmentCN.txt',sep='')) ## adjacent segments with idendical copy number are merged (except over centromere) and all are saved to a text file
+    regions=allRegions$regions
+    save(t,regions,file="regions_t.Rdata")
+
+    karyotype_check(regions$Chromosome,regions$Start,regions$End,regions$log2,regions$imba,regions$Cn,regions$mCn,t,ideogram=NULL,name=name,xlim=xlim,ylim=ylim)
+
+    karyotype_chromsCN(regions$Chromosome,regions$Start,regions$End,regions$log2,
+      regions$imba,regions$Cn,regions$mCn,ideogram=NULL,
+      Log2$Chromosome,Log2$Start,Log2$Value,alf$Chromosome,
+      alf$Start,alf$Value,t,name=name,xlim=c(-1,1),ylim=c(0,1))
+    
+    cat('..done\n')
+    sampleData$completed.analysis[i] <- ''
+   } else cat('Skipped',name,'\n')
+    
+    setwd('..')
+  }
+  save.txt(sampleData,file='SampleData_new.csv')
+}
+###
+regsFromSegs <- function (Log2,alf, segments, bin=200,min=1) {
+## This function builds short segments and calcualtes their average Log-R and Allelic Imbalance Ratio.
+  rownames(Log2)=1:nrows(Log2)
+  rownames(alf)=1:nrows(alf)
+  regs=list('chr'=NULL,'start'=NULL,'end'=NULL,'logs'=NULL,'scores'=NULL,'het'=NULL,'hom'=NULL,'probes'=NULL,'snps'=NULL,'key1'=rep(NA,nrow(Log2)),'key2'=rep(NA,nrow(alf)))
+  n=nrow(segments)
+  s_check=NULL
+for (c in 1:n) { ## for every segment
+  tlog=Log2[Log2$Chromosome==segments$Chromosome[c],] ## Log-R on this chromosome
+  talf=alf[alf$Chromosome==segments$Chromosome[c],] ## Allele Freq on this chromosome
+  tlog=tlog[(tlog$Start>=segments$Start[c])&(tlog$Start<segments$End[c]),] ## Log-R on this segment
+  talf=talf[(talf$Start>=segments$Start[c])&(talf$Start<segments$End[c]),] ## Allele Freq on this segment
+
+  tsnps=nrow(talf)    ## number of snps and probes in this segment
+  tprobes=nrow(tlog)
+  tnregs=max(trunc(tsnps/bin),1) ## Further split into how many (a minimum of 1) subsegments
+  tcuts=segments$Start[c] ## The first start pos
+  tlength=segments$End[c]-segments$Start[c]    ## Length of this whole segment
+  for (i in 1:tnregs) tcuts = c(tcuts, tcuts[1]+round(i/tnregs*tlength)) ## Break the segment at these positions
+
+  for (r in 1:(tnregs)) {    ## build the subsegments
+    regs$chr=c(regs$chr,as.character(segments$Chromosome[c]))    ## Chromosome
+    s_=tcuts[r]                                                     ## Start
+    e_=tcuts[r+1]                                                 ## End
+    thisalf=talf[(talf$Start>=s_)&(talf$Start<=e_),]             ## get the Log-R values
+    thislog=tlog[(tlog$Start>=s_)&(tlog$Start<=e_),]             ## and the allele frequency
+    regs$key1[as.integer(rownames(thislog))]=length(regs$chr)    ## store their positions for fast access during plotting
+    regs$key2[as.integer(rownames(thisalf))]=length(regs$chr)    ## --"--
+    regs$logs=c( regs$logs, mean(thislog$Value) )                ## store average log ratio of this segment
+    regs$probes=c(regs$probes,nrow(thislog))                    ## store number of probes
+    regs$snps=c(regs$snps,nrow(thisalf))                        ## store number of bi-allelic probes (SNPs)
+    #regs$or_seg=c(regs$or_seg,c)    
+    regs$start=c(regs$start,s_)                                    ## store start and end positions
+    regs$end=c(regs$end,e_)
+
+    if (nrow(thisalf)>min) {                                    ## Time to calculate Allelic Imbalance Ratio (if enough SNPs)
+      t1=sort( abs(thisalf$Value-0.5) )                            ## distance from middle (het) in the allele freq pattern, ascending
+      if (length(unique(t1))>3) {                                ## do not attempt clustering with too few snps
+    xx=NULL
[TRUNCATED]

To get the complete diff run:
    svnlook diff /svnroot/patchwork -r 168

From noreply at r-forge.r-project.org  Tue Apr 23 16:51:43 2013
From: noreply at r-forge.r-project.org (noreply at r-forge.r-project.org)
Date: Tue, 23 Apr 2013 16:51:43 +0200 (CEST)
Subject: [Patchwork-commits] r169 - pkg/TAPS
Message-ID: <20130423145143.DAEB91839D9@r-forge.r-project.org>

Author: sebastian_d
Date: 2013-04-23 16:51:43 +0200 (Tue, 23 Apr 2013)
New Revision: 169

Modified:
   pkg/TAPS/DESCRIPTION
Log:
changes to description of TAPS

Modified: pkg/TAPS/DESCRIPTION
===================================================================
--- pkg/TAPS/DESCRIPTION	2013-04-22 14:29:38 UTC (rev 168)
+++ pkg/TAPS/DESCRIPTION	2013-04-23 14:51:43 UTC (rev 169)
@@ -8,4 +8,3 @@
 Description: Performs a allele-specific copy number analysis of array data.
 License: GPL-2
 Depends: R (>= 2.10), DNAcopy, stats, fields, affxparser
-Packaged: 2013-03-22 10:05:27 UTC; S_D