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ProgR_5.5_Heterogeneity.R
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ProgR_5.5_Heterogeneity.R
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################################################################
################################################################
# Script: ProgR_5.5_Heterogeneity.R
# Author: Ingrid Lonnstedt
# Date: 23/05/2013
# R version: R 3.0.0
# Details: Derive basic heterogeneity estimates
################################################################
################################################################
################################################################
################################################################
#
# File paths, functions and date
#
################################################################
################################################################
setwd(paste(getwd(), '/RESPONSIFY', sep=''))
#setwd('/Users/lonnstedt/Documents/RESPONSIFY')
prefix.raw = paste(getwd(), "/AROMA/rawData/responsify/GenomeWideSNP_6/", sep='')
prefix.ann = paste(getwd(), "/AROMA/annotationData/chipTypes/GenomeWideSNP_6/", sep='')
prefix.out = paste(getwd(), "/ABSOLUTE/HAPSEG/", sep='')
date = format(Sys.Date())
source(file.path(getwd(), 'programs', 'ProgR_0.1_Functions.R'))
################################################################
################################################################
#
# Format intensities for HAPSEG
#
################################################################
################################################################
info = read.delim(paste(prefix.ann, '../../Infokey.txt', sep = ''), stringsAsFactors = F)
info$sample = substr(info$Sample.ID, 1, 4)
info$sample.name = paste(rownames(info), info$sample, sep = '.')
info$myarray = info$arrname
setwd(paste(getwd(), '/AROMA', sep=''))
######Find allele intensities
for (i in 1:56){
library(aroma.affymetrix)
dataSet <- "responsify";
tags <- "ACC,ra,-XY,BPN,-XY,RMA,FLN,-XY,CMTN,v2";
chipType <- "GenomeWideSNP_6";
ds <- AromaUnitTotalCnBinarySet$byName(dataSet, tags=tags, chipType=chipType);
df <- getFile(ds, which(getNames(ds) == info$arrname[i]));
bs <- AromaUnitFracBCnBinarySet$byName(dataSet, tags=tags, chipType=chipType);
bf <- getFile(bs, which(getNames(ds) == info$arrname[i]));
ugp <- getAromaUgpFile(df)
unf <- getUnitNamesFile(ugp)
unitNames <- getUnitNames(unf)
chromosome <- ugp[, 1, drop = TRUE]
position <- ugp[, 2, drop = TRUE]
data = data.frame(unitNames = unitNames, chromosome=chromosome, position = position,
total = df[,1], fracB = bf[,1], stringsAsFactors = F)
names(data)[4:5] = c('total','fracB')
d = file.path(getwd(), 'ABSOLUTE', 'HAPSEG', info$sample.name[i])
dir.create(d, recursive= T, showWarnings = F)
write.table(data, file.path(d, 'Total,fracB.txt'), row.names = F, quote = F, sep = '\t')
}
######Output channel specific intensities
#First move from old to new directory, only run this once!
for (i in 1:56){
d.old = file.path(getwd(), 'ABSOLUTE', 'Old', 'HAPSEG', info$sample.name[i])
d = file.path(getwd(), 'ABSOLUTE', 'SampleData', info$sample.name[i])
dir.create(d, recursive = T, showWarnings = F)
file.copy(file.path(d.old, 'Total,fracB.txt'), file.path(d, 'Total,fracB.txt'))
file.copy(file.path(d.old, 'Hetsegs.txt'), file.path(d, 'Hetsegs.txt'))
file.copy(file.path(d.old, 'Homsegs.txt'), file.path(d, 'Homsegs.txt'))
file.copy(file.path(d.old, 'Subsegs.txt'), file.path(d, 'Subsegs.txt'))
}
#Now output the new intensities
for (i in 1:56){
d = file.path(getwd(), 'ABSOLUTE', 'SampleData', info$sample.name[i])
data = read.delim(file.path(d, 'Total,fracB.txt'), stringsAsFactors = F)
###Write to file for HAPSEG
data$baf = pmax(data$fracB, 0)
data$baf = pmin(data$fracB, 1)
data$A = data$total*(1-data$baf)
data$B = data$total*(data$baf)
dat = subset(data, select = c('A','B'))
rownames(dat) = data$unitNames
d = file.path(getwd(), 'ABSOLUTE', 'SampleData', info$sample.name[i])
dir.create(d, recursive = T, showWarnings = F)
save(dat, file = file.path(d, 'snpfile.RData'))
}
#Intensities for nexus preprocessed samples
#Need the previous section to just have been run, becuase will build on the object 'data'
for (i in c(23, 39)){
d = file.path(getwd(), 'TAPS', info$sample.name[i])
probes = read.delim(file.path(d, 'probes.txt'))
bafs = read.delim(file.path(d, 'snps.txt'))
data$total = NA
data$fracB = NA
probes$Chromosome = as.character(probes$Chromosome)
nc = nchar(probes$Chromosome, type = 'c')
probes$Chromosome = substr(probes$Chromosome, 4, nc)
probes$Chromosome[probes$Chromosome == 'X'] = '23'
probes$Chromosome[probes$Chromosome == 'Y'] = '24'
probes$Chromosome = factor(probes$Chromosome, levels = as.character(1:25))
bafs$Chromosome = as.character(bafs$Chromosome)
nc = nchar(bafs$Chromosome, type = 'c')
bafs$Chromosome = substr(bafs$Chromosome, 4, nc)
bafs$Chromosome[bafs$Chromosome == 'X'] = '23'
bafs$Chromosome[bafs$Chromosome == 'Y'] = '24'
bafs$Chromosome = factor(bafs$Chromosome, levels = as.character(1:25))
data$chromosome = factor(data$chromosome, levels = as.character(1:25))
h = RangedData(IRanges(start = probes[,'Start'], width = 1),
space = probes$Chromosome, universe = 'hg19', val = probes[,'Value'])
ix = which(!is.na(data$chromosome))
d = RangedData(IRanges(start = data$position[ix], width = 1),
space = data$chromosome[ix], universe = 'hg19')
hits = findOverlaps(query = h, subject = d)
for (j in 1:length(levels(data$chromosome))){
ch = levels(data$chromosome)[j]
ind = which(data$chromosome == ch & !is.na(data$chromosome))
data$total[ind[subjectHits(hits[[ch]])]] = (probes$Value[probes$Chromosome
== ch])[queryHits(hits[[ch]])]
}
h = RangedData(IRanges(start = bafs[,'Start'], width = 1),
space = bafs$Chromosome, universe = 'hg19', val = bafs[,'Value'])
hits = findOverlaps(query = h, subject = d)
for (j in 1:length(levels(data$chromosome))){
ch = levels(data$chromosome)[j]
ind = which(data$chromosome == ch & !is.na(data$chromosome))
data$fracB[ind[subjectHits(hits[[ch]])]] = (bafs$Value[bafs$Chromosome
== ch])[queryHits(hits[[ch]])]
}
data$total = 2**data$total
data$baf = pmax(data$fracB, 0)
data$baf = pmin(data$fracB, 1)
data$A = data$total*(1-data$baf)
data$B = data$total*(data$baf)
dat = subset(data, select = c('A','B'))
rownames(dat) = data$unitNames
d = file.path(getwd(), 'ABSOLUTE', 'SampleData', info$sample.name[i])
dir.create(d, recursive = T, showWarnings = F)
save(dat, file = file.path(d, 'snpfile.RData'))
}
###### Segment datasets from PSCBS
library(PSCBS)
for (i in 2:56){
print(i)
#Prepare data for this sample
fitname = paste('fit', i, sep = '')
filename <- paste(getwd(), '/AROMA/reports/SNP_segmentation/CBSfits/', fitname, '.RData', sep = '');
fit <- loadObject(filename);
seg = getSegments(fit)
seg$Sample = info$myarray[i]
seg = data.frame(Sample = seg$Sample, Chromosome = seg$chromosome, "Start Position" = seg$tcnStart,
"End Position" = seg$tcnEnd, "Num markers" = seg$tcnNbrOfSNPs,
check.names = F)
seg = subset(seg, !is.na(Chromosome) & Chromosome != 25)
seg$Chromosome = factor(seg$Chromosome, levels = as.character(1:24))
seg$cn = NA
d = file.path(getwd(), 'ABSOLUTE', 'SampleData', info$sample.name[i], 'Segdat.txt')
write.table(seg, d, row.names = F, quote = F, sep = '\t')
}
#Fix the nexus normalized samples
for (i in c(23, 39)){
d = file.path(getwd(), 'TAPS', info$sample.name[i])
seg = read.delim(file.path(d, 'segments.txt'))
seg = data.frame(Sample = info$myarray[i], Chromosome = seg$Chromosome, "Start Position" = seg$Start,
"End Position" = seg$End, "Num markers" = NA,
check.names = F)
seg = subset(seg, !is.na(Chromosome) & Chromosome != 'chr25')
seg$Chromosome = as.character(seg$Chromosome)
nc = nchar(seg$Chromosome, type = 'c')
seg$Chromosome = substr(seg$Chromosome, 4, nc)
seg$Chromosome[seg$Chromosome == 'X'] = '23'
seg$Chromosome[seg$Chromosome == 'Y'] = '24'
seg$Chromosome = factor(seg$Chromosome, levels = as.character(1:24))
seg$cn = NA
d = file.path(getwd(), 'ABSOLUTE', 'SampleData', info$sample.name[i], 'Segdat.txt')
write.table(seg, d, row.names = F, quote = F, sep = '\t')
}
################################################################
################################################################
#
# HAPSEG
#
################################################################
################################################################
library(HAPSEG)
library(foreach)
library(doMC)
registerDoMC(20)
info = read.delim(paste(prefix.ann, '../../Infokey.txt', sep = ''))
info$sample = substr(info$Sample.ID, 1, 4)
info$myarray = info$arrname
info$sample.name = paste(info$ind, info$sample, sep = '.')
DoHapseg1 <- function(myarray, mysample) {
registerDoSEQ()
plate.name <- mysample
phased.bgl.dir <- "/usr/local/bioinfsoftware/R/current/lib64/R/library/ABSOLUTE/etc/phasedBGL/hg19"
d = file.path(getwd(), 'ABSOLUTE', 'SampleData', mysample)
snp.fn <- file.path(d, 'snpfile.RData')
seg.fn = file.path(d, 'Segdat.txt')
results.dir <- file.path(getwd(), 'ABSOLUTE', 'HAPSEG1', mysample)
if (!file.exists(results.dir)) {
dir.create(results.dir, recursive=TRUE)
}
print(paste("Starting array", mysample, "at", results.dir))
log.dir <- file.path(getwd(), 'ABSOLUTE', 'HAPSEG1', 'log')
if (!file.exists(log.dir)) {
dir.create(log.dir, recursive=TRUE)
}
sink(file=file.path(log.dir, paste(mysample, ".hapseg.out.txt", sep="")))
myRunHapSeg(plate.name, myarray, seg.fn, snp.fn, genome.build = 'hg19', results.dir, platform = "SNP_6.0",
use.pop='CEPH',
impute.gt=TRUE, plot.segfit=TRUE, merge.small=FALSE, merge.close=TRUE, min.seg.size=2,
normal=FALSE,
out.p=0.001, seg.merge.thresh=1, use.normal = FALSE, adj.atten = FALSE,
phased.bgl.dir, calibrate.data=FALSE,
drop.x=FALSE, verbose=TRUE)
sink()
}
foreach (i=1:56, .combine=c) %dopar% {
DoHapseg1(myarray = info$myarray[i], mysample = info$sample.name[i])
}
DoHapseg2 <- function(myarray, mysample) {
registerDoSEQ()
plate.name <- mysample
phased.bgl.dir <- "/usr/local/bioinfsoftware/R/current/lib64/R/library/ABSOLUTE/etc/phasedBGL/hg19"
d = file.path(getwd(), 'ABSOLUTE', 'SampleData', mysample)
snp.fn <- file.path(d, 'snpfile.RData')
seg.fn = file.path(d, 'Segdat.txt')
results.dir <- file.path(getwd(), 'ABSOLUTE', 'HAPSEG2', mysample)
if (!file.exists(results.dir)) {
dir.create(results.dir, recursive=TRUE)
}
print(paste("Starting array", mysample, "at", results.dir))
log.dir <- file.path(getwd(), 'ABSOLUTE', 'HAPSEG2', 'log')
if (!file.exists(log.dir)) {
dir.create(log.dir, recursive=TRUE)
}
sink(file=file.path(log.dir, paste(mysample, ".hapseg.out.txt", sep="")))
myRunHapSeg(plate.name, myarray, seg.fn, snp.fn, genome.build = 'hg19', results.dir, platform = "SNP_6.0",
use.pop='CEPH',
impute.gt=TRUE, plot.segfit=TRUE, merge.small=FALSE, merge.close=TRUE, min.seg.size=2,
normal=FALSE,
out.p=0.001, seg.merge.thresh=1e-10, use.normal = FALSE, adj.atten = FALSE,
phased.bgl.dir, calibrate.data=FALSE,
drop.x=FALSE, verbose=TRUE)
sink()
}
foreach (i=1:56, .combine=c) %dopar% {
DoHapseg2(myarray = info$myarray[i], mysample = info$sample.name[i])
}
DoHapseg3 <- function(myarray, mysample) {
registerDoSEQ()
plate.name <- mysample
phased.bgl.dir <- "/usr/local/bioinfsoftware/R/current/lib64/R/library/ABSOLUTE/etc/phasedBGL/hg19"
d = file.path(getwd(), 'ABSOLUTE', 'SampleData', mysample)
snp.fn <- file.path(d, 'snpfile.RData')
seg.fn = file.path(d, 'Segdat.txt')
results.dir <- file.path(getwd(), 'ABSOLUTE', 'HAPSEG3', mysample)
if (!file.exists(results.dir)) {
dir.create(results.dir, recursive=TRUE)
}
print(paste("Starting array", mysample, "at", results.dir))
log.dir <- file.path(getwd(), 'ABSOLUTE', 'HAPSEG3', 'log')
if (!file.exists(log.dir)) {
dir.create(log.dir, recursive=TRUE)
}
sink(file=file.path(log.dir, paste(mysample, ".hapseg.out.txt", sep="")))
myRunHapSeg(plate.name, myarray, seg.fn, snp.fn, genome.build = 'hg19', results.dir, platform = "SNP_6.0",
use.pop='CEPH',
impute.gt=TRUE, plot.segfit=TRUE, merge.small=FALSE, merge.close=TRUE, min.seg.size=2,
normal=FALSE,
out.p=0.001, seg.merge.thresh=1e-80, use.normal = FALSE, adj.atten = FALSE,
phased.bgl.dir, calibrate.data=FALSE,
drop.x=FALSE, verbose=TRUE)
sink()
}
foreach (i=1:56, .combine=c) %dopar% {
DoHapseg3(myarray = info$myarray[i], mysample = info$sample.name[i])
}
################################################################
################################################################
#
# Format exome mutation file for ABSOLUTE
#
################################################################
################################################################
# Create sample_list for Pearl script TSV2MAF_pearl_script.rtf
# and reformat mutations file to MAF (Broad's format)
setwd(paste(getwd(), '/RESPONSIFY', sep=''))
#setwd('/Users/lonnstedt/Documents/RESPONSIFY')
prefix.in = paste(getwd(), "/rawData/Exome/", sep='')
prefix.raw = paste(getwd(), "/datasets/", sep='')
###Sample_list
mut = read.delim(paste(prefix.in, 'Loi_130516_24samples/ALL_130516_Filter_CBS_MinDepth_20.txt',
sep = ''))
samplesT = unique(mut$SAMPLE)
samples = sort(unique(substr(as.character(samplesT), 1, 4)))
sampT = paste(samples, 'T', sep = '')
sampN = paste(samples, 'N', sep = '')
out = rbind(data.frame(sample = sampT, type = 'Tumour', pairsamp = sampN, bam = 'dummy'),
data.frame(sample = sampN, type = 'Normal', pairsamp = sampT, bam = 'dummy'))
write.table(out, file = paste(prefix.in, 'sample_list.txt', sep = ''), row.names = F,col.names = F,
quote = F, sep = '\t')
###Pearl calls
###Run from Exome library
###This never worked with our system, but Jason Li run it for us.
perl parse_tsv_to_maf_format.pl sample_list.txt HUGO_EntrezIDs.txt Loi_130516_24samples/ALL_130516_Filter_CBS_MinDepth_20.txt ALL_130516_Filter_CBS_MinDepth_20.maf
### Find additional variables to merge with the maf file
### AND split maf file into one file for each sample
mut = read.delim(paste(prefix.in, 'Loi_130516_24samples/ALL_130516_Filter_CBS_MinDepth_20.txt',
sep = ''))
dat = data.frame(Tumor_Sample_Barcode = mut$SAMPLE,
Chromosome = mut$X.CHROM, Start_position = mut$POS,
t_alt_count = mut$X4688T_Variant_reads,
t_ref_count = mut$X4688T_Read_depth - mut$X4688T_Variant_reads)
dat = unique(dat)
mafbase = 'ALL_130516_Filter_CBS_MinDepth_20'
maf = read.delim(paste(prefix.in, mafbase,'.maf', sep = ''),
row.names = NULL, stringsAsFactors = FALSE,
check.names = FALSE, na.strings = c("NA", "---"),
blank.lines.skip = TRUE, comment.char = "#")
names(maf)[names(maf) == 'Start_Position'] = 'Start_position'
names(maf)[names(maf) == 'End_Position'] = 'End_position'
nrow(maf)
maf = merge(maf, dat, all = T, sort = F)
nrow(maf)
info = read.delim(paste(prefix.ann, '../../Infokey.txt', sep = ''))
info$sample = substr(as.character(info$Sample.ID), 1, 4)
for (i in 1:nrow(info)){
samp = info$sample[i]
out = subset(maf, Tumor_Sample_Barcode == paste(samp, 'T', sep = ''))
if (nrow(out)>0) write.table(out, file = paste(prefix.in,'MAF/',
mafbase, '_', i,'.', samp, '.maf', sep = ''), row.names = F, quote = F,
sep = '\t')
}
#maf = read.delim(maf.fn,
# row.names = NULL, stringsAsFactors = FALSE,
# check.names = FALSE, na.strings = c("NA", "---"),
# blank.lines.skip = TRUE, comment.char = "#")
################################################################
################################################################
#
# Run ABSOLUTE without scaling, just for comparison
#
################################################################
################################################################
library(ABSOLUTE)
library(foreach)
library(doMC)
info = read.delim(paste(prefix.ann, '../../Infokey.txt', sep = ''))
info$sample = substr(info$Sample.ID, 1, 4)
info$myarray = info$arrname
info$sample.name = paste(rownames(info),info$sample, sep = '.')
DoAbsolute1 <- function(myarray, mysample, i) {
registerDoSEQ()
sample.name <- mysample
maf.fn = paste(getwd(),
'/AROMA/rawData/responsify/Exome/MAF/ALL_130516_Filter_CBS_MinDepth_20', '_',
mysample, '.maf', sep = '')
seg.dat.fn <- paste(getwd(), '/ABSOLUTE/HAPSEG1/', mysample,'/',mysample,'_',myarray,
"_segdat.RData", sep="")
results.dir <- paste(getwd(), '/ABSOLUTE/RunAbsolute1', sep = '')
print(paste("Starting myarray", mysample, "at", results.dir))
log.dir <- paste(getwd(), '/ABSOLUTE/RunAbsolute1/abs_logs', sep = '')
if (!file.exists(log.dir)) {
dir.create(log.dir, recursive=TRUE)
}
if (!file.exists(results.dir)) {
dir.create(results.dir, recursive=TRUE)
}
sink(file=file.path(log.dir, paste(mysample, ".abs.out.txt", sep="")))
myRunAbsolute(seg.dat.fn, sigma.p = 0, max.sigma.h = 0.01, min.ploidy = 0.95, max.ploidy = 12,
primary.disease = "Breast Cancer",
platform = 'SNP_6.0', sample.name, results.dir, max.as.seg.count = 1500,
max.non.clonal = 0,
max.neg.genome = 0, copy_num_type = 'allelic', verbose=TRUE, maf.fn = maf.fn,
min.mut.af = 0.03, output.fn.base = mysample)
sink()
}
foreach (i = c(2, 3, 12:16, 20:27, 29, 30, 34, 36, 51, 53, 56), .combine=c) %dopar% {
DoAbsolute1(myarray = info$myarray[i], mysample = paste(i,info$sample[i], sep = '.'))
}
DoAbsolute2 <- function(myarray, mysample, i) {
registerDoSEQ()
sample.name <- mysample
maf.fn = paste(getwd(),
'/AROMA/rawData/responsify/Exome/MAF/ALL_130516_Filter_CBS_MinDepth_20', '_',
mysample, '.maf', sep = '')
seg.dat.fn <- paste(getwd(), '/ABSOLUTE/HAPSEG2/', mysample,'/',mysample,'_',myarray,
"_segdat.RData", sep="")
results.dir <- paste(getwd(), '/ABSOLUTE/RunAbsolute2', sep = '')
print(paste("Starting myarray", mysample, "at", results.dir))
log.dir <- paste(getwd(), '/ABSOLUTE/RunAbsolute2/abs_logs', sep = '')
if (!file.exists(log.dir)) {
dir.create(log.dir, recursive=TRUE)
}
if (!file.exists(results.dir)) {
dir.create(results.dir, recursive=TRUE)
}
sink(file=file.path(log.dir, paste(mysample, ".abs.out.txt", sep="")))
myRunAbsolute(seg.dat.fn, sigma.p = 0, max.sigma.h = 0.01, min.ploidy = 0.95, max.ploidy = 12,
primary.disease = "Breast Cancer",
platform = 'SNP_6.0', sample.name, results.dir, max.as.seg.count = 1500,
max.non.clonal = 0,
max.neg.genome = 0, copy_num_type = 'allelic', verbose=TRUE, maf.fn = maf.fn,
min.mut.af = 0.03, output.fn.base = mysample)
sink()
}
foreach (i = c(2, 3, 12:16, 20:27, 29, 30, 34, 36, 51, 53, 56), .combine=c) %dopar% {
DoAbsolute2(myarray = info$myarray[i], mysample = paste(i,info$sample[i], sep = '.'))
}
DoAbsolute3 <- function(myarray, mysample, i) {
registerDoSEQ()
sample.name <- mysample
maf.fn = paste(getwd(),
'/AROMA/rawData/responsify/Exome/MAF/ALL_130516_Filter_CBS_MinDepth_20', '_',
mysample, '.maf', sep = '')
seg.dat.fn <- paste(getwd(), '/ABSOLUTE/HAPSEG3/', mysample,'/',mysample,'_',myarray,
"_segdat.RData", sep="")
results.dir <- paste(getwd(), '/ABSOLUTE/RunAbsolute3', sep = '')
print(paste("Starting myarray", mysample, "at", results.dir))
log.dir <- paste(getwd(), '/ABSOLUTE/RunAbsolute3/abs_logs', sep = '')
if (!file.exists(log.dir)) {
dir.create(log.dir, recursive=TRUE)
}
if (!file.exists(results.dir)) {
dir.create(results.dir, recursive=TRUE)
}
sink(file=file.path(log.dir, paste(mysample, ".abs.out.txt", sep="")))
myRunAbsolute(seg.dat.fn, sigma.p = 0, max.sigma.h = 0.01, min.ploidy = 0.95, max.ploidy = 12,
primary.disease = "Breast Cancer",
platform = 'SNP_6.0', sample.name, results.dir, max.as.seg.count = 1500,
max.non.clonal = 0,
max.neg.genome = 0, copy_num_type = 'allelic', verbose=TRUE, maf.fn = maf.fn,
min.mut.af = 0.03, output.fn.base = mysample)
sink()
}
foreach (i = c(2, 3, 12:16, 20:27, 29, 30, 34, 36, 51, 53, 56), .combine=c) %dopar% {
DoAbsolute3(myarray = info$myarray[i], mysample = paste(i,info$sample[i], sep = '.'))
}
#Not used yet
d = paste(getwd(), '/ABSOLUTE/RunAbsolute1', sep = '')
obj.name = 'ALL'
CreateReviewObject(obj.name=obj.name, absolute.files=paste(d, '/',
info$sample.name[c(2, 3, 12:16, 20:27, 29, 30, 34, 36, 51, 53, 56)],
'.ABSOLUTE.RData', sep = ''),
indv.results.dir = file.path(d, obj.name), copy_num_type = "allelic",
plot.modes = TRUE, verbose=TRUE)
################################################################
################################################################
#
# Pick patterns
#
################################################################
################################################################
info = read.delim(paste(prefix.ann, '../../Infokey.txt', sep = ''), stringsAsFactors = F)
info$sample.name = paste(info$ind, info$sample, sep = '.')
dir.create('HAPSEG1')
for (i in 1:56){
dir.create(file.path('HAPSEG1', info$sample.name[i]))
d = file.path(getwd(), 'ABSOLUTE', 'HAPSEG1', info$sample.name[i])
f = paste(info$sample.name[i], '_', info$arrname[i], '_segdat.RData', sep = '')
file.copy(file.path(d, f), file.path('HAPSEG1', info$sample.name[i], f))
}
dir.create('HAPSEG3')
for (i in 1:56){
dir.create(file.path('HAPSEG3', info$sample.name[i]))
d = file.path(getwd(), 'ABSOLUTE', 'HAPSEG3', info$sample.name[i])
f = paste(info$sample.name[i], '_', info$arrname[i], '_segdat.RData', sep = '')
file.copy(file.path(d, f), file.path('HAPSEG3', info$sample.name[i], f))
}
for (i in 1:56){
i = 15
d = file.path(getwd(), 'ABSOLUTE', 'HAPSEG2', info$sample.name[i])
da = load(file.path(d, paste(info$sample.name[i], '_', info$arrname[i], '_segdat.RData', sep = '')))
da = as.data.frame(seg.dat$allele.segs)
da$Chromosome = factor(da$Chromosome, levels = as.character(1:22))
names(da)[6:7] = c('A1','A2')
da$tot = (da$A1+da$A2)
da$imba = 2*abs(da$A1/(da$A1+da$A2) - 0.5)
d0.imba = 1/200
d0.tot = diff(range(quantile(da$tot, c(.05, .95))))/500
n = 100
out = NULL
for (pattern in 1:20){
plot(da$tot, da$imba, pch = 16, col = col.transp('grey', .3), cex = 1,
xlab = 'Total Copy Number', ylab = 'Alleleic imbalance 2*|BAF - 0.5|',
main = info$sample.name[i])#, xlim = c(0,5))
grid()
#Start picking a new pattern
print('Choose points within this pattern, press Esc when finished!')
pos = locator(n) #Choose points within this specific pattern, press Esc when finished!
di = 0
dt = 0
tmp = NULL
plot(da$tot, da$imba, pch = 16, col = col.transp('grey', .3), cex = 1,
xlab = 'Total Copy Number', ylab = 'Alleleic imbalance 2*|BAF - 0.5|',
main = info$sample.name[i])
grid()
#########RUN THESE LINES UNTIL JUST TOO MANY SEGMENTS HIGHLIGHTED
for (findpoints in 1:1000){
if (di>0) last.tmp = tmp
di = di + d0.imba
dt = dt + d0.tot
y.close = outer(da$imba, pos$y, function(x, y, di){abs(x-y)<di},
di = di)
x.close = outer(da$tot, pos$x, function(x, y, dt){abs(x-y)<dt},
dt = dt)
ix = apply(y.close & x.close, 1, any)
tmp = da[ ix,]
points(tmp$tot, tmp$imba, col = 'blue', pch = 16, cex = 1)
points(pos$x, pos$y, pch = 4, col = 'red')
print('Continue [Return] or give pattern name [e.g. maj2, min1, tot4 or bal]:')
finished.this.pattern = readline()
if (finished.this.pattern != '') {
last.tmp$pattern = finished.this.pattern
break
}
}
#########THEN SAVE THE PREVIOUS SEGMENTS (RUN THIS ONCE)
rownames(last.tmp) = NULL
out = rbind(out, last.tmp)
print('Find more patterns? (y/n)')
finished.all.patterns = readline()
if (finished.all.patterns == 'n') break
}
plot(da$tot, da$imba, pch = 16, col = col.transp('grey', .3), cex = 1,
xlab = 'Total Copy Number', ylab = 'Alleleic imbalance 2*|BAF - 0.5|',
main = info$sample.name[i])
grid()
for (p in seq_along(unique(out$pattern))) points(imba~tot, data = out[out$pattern == unique(out$pattern)[p],],
col = col.transp(p, .5), pch = 16)
d = file.path(getwd(), 'ABSOLUTE', 'SampleData', info$sample.name[i])
dev.print(png, file=file.path(d, 'Patterns.png'),
width=640, height=640)
write.table(out, file.path(d, 'Patternsegs.txt'),
row.names = F, quote = F, sep = '\t')
}
################################################################
################################################################
#
# Pick TAPS clusters
#
################################################################
################################################################
info = read.delim(paste(prefix.ann, '../../Infokey.txt', sep = ''), stringsAsFactors = F)
info$sample.name = paste(info$ind, info$sample, sep = '.')
for (i in 1:56){
i = 56
ta = read.delim(file.path(getwd(), 'TAPS', info$sample.name[i],
paste(info$sample.name[i], 'segmentCN.txt', sep = '_')))
plot(imba~log2, data = ta, xlim = c(-1,2), main = info$sample.name[i])
d0.imba = diff(range(ta$imba, na.rm = T))/200
d0.log = diff(range(ta$log2, na.rm = T))/1000
out = NULL
n = 1 ###MANUALLY INSERT THE NUMBER OF CLUSTERS YOU LIKE HERE:
pos = locator(n)
###FOR EACH CLUSTER (CHANGE c BETWEEN 1, 2, 3, ..., n)
c = 1
plot(imba~log2, data = ta, xlim = c(-1,2), main = info$sample.name[i])
di = 0
dl = 0
#########RUN THESE LINES UNTIL JUST TOO MANY SEGMENTS HIGHLIGHTED
if (di>0) last.tmp = subset(ta, abs(ta$imba - pos$y[c])<di & abs(ta$log2 - pos$x[c])<dl)
di = di + d0.imba
dl = dl + d0.log
tmp = subset(ta, abs(ta$imba - pos$y[c])<di & abs(ta$log2 - pos$x[c])<dl)
points(imba~log2, data = tmp, col = c+1, pch = 16)
#########THEN SAVE THE PREVIOUS SEGMENTS (RUN THIS ONCE)
out = rbind(out, cbind(last.tmp, cluster = c))
plot(imba~log2, data = ta, xlim = c(-1,2), main = info$sample.name[i], bg = 'white')
points(imba~log2, data = last.tmp, col = c+1, pch = 16)
plot(imba~log2, data = ta, xlim = c(-1,2), main = info$sample.name[i])
points(imba~log2, data = out, col = out$cluster+1, pch = 16)
d = file.path(getwd(), 'ABSOLUTE', 'Choose_solution')
dev.print(png, file=file.path(d, paste(info$sample.name[i], '_Chosen_subclones.png', sep = '')),
width=640, height=640)
d = file.path(getwd(), 'ABSOLUTE', 'HAPSEG', info$sample.name[i])
dir.create(d, recursive = T, showWarnings = F)
write.table(out[,c('Chromosome','Start','End','cluster')], file.path(d, 'Subsegs.txt'),
row.names = F, quote = F, sep = '\t')
}
################################################################
################################################################
#
# Plot PSCBS: Allele1 vs Allele2 coloured by the chosen clusters
#
################################################################
################################################################
info = read.delim(paste(prefix.ann, '../../Infokey.txt', sep = ''), stringsAsFactors = F)
info$sample.name = paste(info$ind, info$sample, sep = '.')
library(PSCBS)
for (i in 1:56){
d = file.path(getwd(), 'AROMA', 'reports', 'SNP_segmentation', 'CBSfits')
da = loadObject(paste(d, '/fit', i, '.RData', sep = ''))
seg = da$output
#plot(dhMean~log(tcnMean), data = seg, main = info$sample.name[i], xlim = c(-.2, 2))
#plot(c1Mean~c2Mean, data = seg, main = info$sample.name[i])
grid()
mat = seg
names(mat)[names(mat) == 'c1Mean'] = 'A1'
names(mat)[names(mat) == 'c2Mean'] = 'A2'
names(mat)[names(mat) == 'tcnStart'] = 'Start.bp'
names(mat)[names(mat) == 'tcnEnd'] = 'End.bp'
names(mat)[names(mat) == 'chromosome'] = 'Chromosome'
mat$length = mat$End.bp - mat$Start.bp
mat = subset(mat, !is.na(length) & Chromosome<23)
###Read selected segments
d = file.path(getwd(), 'ABSOLUTE', 'SampleData', info$sample.name[i])
tmp = getKnownClustersAndOverlaps(mat = mat, d = d)
cl = tmp$cl
clust = tmp$clust
w = tmp$w
#A1 vs A2
plot1v2(a1 = mat$A1, a2=mat$A2, mat = mat, clust = clust, cl = cl, w = w)
title(main = i)
plot1v2(a1 = mat$dhMean, a2=log(mat$tcnMean), mat = mat, clust = clust, cl = cl, w = w)
title(main = i)
}
d = file.path(getwd(), 'ABSOLUTE','ScaledAbsolute')
dev.print(png, file=file.path(d, paste(info$sample.name[i], '.............png', sep = '')),
width=640, height=640)
#This plot, too has the problem with CN 2 clusters not on a vertical line.
################################################################
################################################################
#
# Plot HAPSEG data with chosen clusters coloured
#
################################################################
################################################################
info = read.delim(paste(prefix.ann, '../../Infokey.txt', sep = ''), stringsAsFactors = F)
info$sample.name = paste(info$ind, info$sample, sep = '.')
#Either plot in one large pdf or plot each sample in a separate pdf.
#d = file.path(getwd(), 'ABSOLUTE', NA)
#pdf(paste(d, 'Cluster_overview2', '.pdf', sep=''),
# paper = 'a4', height = 12, width = 8)
for (i in 1:56){
#Path to print output
d = file.path(getwd(), 'ABSOLUTE', 'SampleData')
pdf(file.path(d, info$sample.name[i], 'HAPSEG3_choosen_clusters.pdf'),
paper = 'a4', height = 12, width = 8)
### Load data for this sample
d = file.path(getwd(), 'ABSOLUTE', 'HAPSEG3', info$sample.name[i])
f = load(file.path(d, paste(info$sample.name[i], '_', info$arrname[i], '_segdat.RData', sep = '')))
ab = as.data.frame(seg.dat$allele.segs)
ab$Chromosome = factor(ab$Chromosome, levels = as.character(1:22))
names(ab)[6:7] = c('A1','A2')
###Read selected segments
d = file.path(getwd(), 'ABSOLUTE', 'SampleData', info$sample.name[i])
tmp = getKnownClustersAndOverlaps(mat = ab, d = d)
cl = tmp$cl
clust = tmp$clust
w = tmp$w
### Plots of HAPSEG output
###
par(mfrow = c(3,2))
#imba by logratio from hscrs
plot1v2(a1 = 2*abs(ab$A1/(ab$A1+ab$A2) - 0.5), a2=log2(ab$A1+ab$A2), mat = ab, clust = clust, cl = cl, w = w,
xlab = 'log2(Total Copy Number)', ylab = 'Alleleic imbalance 2*|BAF - 0.5|')
#A1 vs A2
plot1v2(a1 = ab$A1, a2=ab$A2, mat = ab, clust = clust, cl = cl, w = w)
### Plots of histograms
myab = histCol(mat = ab, cl = cl, clust = clust, xlab = 'Relative CN homologues pooled')
myab = histCol(toplot = 'A1', cl = cl, clust=clust, myab = myab, xlab = 'Relative CN homologue 1')
frame()
myab = histCol(toplot = 'A2', cl = cl, clust=clust, myab = myab, xlab = 'Relative CN homologue 2')
title(main = info$sample.name[i], outer = T, line = -1)
dev.off()
}
#dev.off()
################################################################
################################################################
#
# Transform
#
################################################################
################################################################
info = read.delim(paste(prefix.ann, '../../Infokey.txt', sep = ''), stringsAsFactors = F)
info$sample.name = paste(info$ind, info$sample, sep = '.')
source(file.path(getwd(), 'programs', 'make_HAPSEG_seg_obj.R'))
library(Rsolnp)
i = 23
dhapseg = file.path(getwd(), 'ABSOLUTE', 'HAPSEG2')
dhapseg = paste(dhapseg,'/', info$sample.name[i], sep = '')
#load(file.path(d, paste(info$sample.name[i], '_', info$arrname[i], '_segdat.RData', sep = '')))
seg.dat.fn = file.path(dhapseg, paste(info$sample.name[i], '_', info$arrname[i], '_segdat.RData', sep = ''))
seg.dat = HAPSEGMakeSegObj(seg.dat.fn, gender = 'Female', filter_segs=FALSE, min_probes=10, max_sd=100,
verbose=TRUE)
as.seg.dat = seg.dat$as.seg.dat
nr = as.integer(nrow(as.seg.dat))
mat = as.seg.dat[1:(nr/2),]
names(mat)[6] = 'A1'
mat$A2 = as.seg.dat$copy_num[(nr/2+1):(nr)]
mat$W = mat$W*2
###Read selected segments
tmp = getKnownClustersAndOverlaps(mat = mat, d = file.path(getwd(),'ABSOLUTE', 'SampleData', info$sample.name[i]))
cl = tmp$cl
clust = tmp$clust
w = tmp$w
#Define data for optimization
y = mat$A1
x = mat$A2
xclust = rep(NA, nrow(mat))
yclust = rep(NA, nrow(mat))
cv = read.delim(file.path(getwd(), 'ABSOLUTE', 'SampleData', 'ClusterCNs.txt'))
cv = cv[cv$sample == i,]
ix = which(clust == 'cluster hom')
if (length(ix)>0) xclust[ix] = cv$xhom
if (length(ix)>0) yclust[ix] = cv$yhom
ix = which(clust == 'cluster het')
if (length(ix)>0) xclust[ix] = cv$xhet
if (length(ix)>0) yclust[ix] = cv$yhet
ix = which(clust == 'cluster 1')
if (length(ix)>0) xclust[ix] = cv$x1
if (length(ix)>0) yclust[ix] = cv$y1
ix = which(clust == 'cluster 2')
if (length(ix)>0) xclust[ix] = cv$x2
if (length(ix)>0) yclust[ix] = cv$y2
ix = which(clust == 'cluster 3')
if (length(ix)>0) xclust[ix] = cv$x3
if (length(ix)>0) yclust[ix] = cv$y3
ix = which(clust == 'cluster 4')
if (length(ix)>0) xclust[ix] = cv$x4
if (length(ix)>0) yclust[ix] = cv$y4
#Save the cluster data to use
dd = data.frame(x=x, y=y, xclust = xclust, yclust=yclust, xw=w/sum(w), yw = w/sum(w),
myclust = clust)
dd = subset(dd, !(is.na(xclust) & is.na(yclust)))
##########################################################
#Optimize one issue at the time
### Scale x
###
dx = subset(dd, !is.na(xclust))
pars = solnp(pars = c(1,0), fun = minx, myclust = dx$myclust,
X=dx$x, Y=dx$y, xclust = dx$xclust, xw = dx$xw,
eqfun = eqfunx, eqB = mean(dx$x), LB = c(0, -1000))$pars
a = pars[1]
b = pars[2]
mat$A2new = a*mat$A2 + b*mat$A1
dd$x = a*dd$x + b*dd$y
param = c(a, b)
### Scale y
###
dy = subset(dd, !is.na(yclust))
pars = solnp(pars = c(0,1), fun = miny, myclust = dy$myclust,
X=dy$x, Y=dy$y, yclust = dy$yclust, yw = dy$yw,
eqfun = eqfuny, eqB = mean(dy$y), LB = c(-1000, 0))$pars
c = pars[1]
d = pars[2]
mat$A1new = c*mat$A2new + d*mat$A1
dd$y = c*dd$x + d*dd$y
param = c(param, c, d)
### Scale so that x and y fit together
###
pars = solnp(pars = c(1, 1), fun = minxy, myclust = dd$myclust, mat = mat,
eqfun = eqfuncxy, eqB = 1,
X=dd$x, Y=dd$y, xclust = dd$xclust, xw = dd$xw, yclust = dd$yclust, yw = dd$yw)$pars
B = pars[1]
D = pars[2]
mat$A2last = B*mat$A2new
mat$A1last = D*mat$A1new
param = c(param, B, D)
names(param) = c('a','b','c','d', 'B','D')
dd$x = B*dd$x
dd$y = D*dd$y
###########################################################
# Figures of outcome
plot1v2(a1 = mat$A1, a2 = mat$A2, mat = mat, cl = cl, clust = clust, w = w, xlab = 'Relative CN homologue 2',
ylab = 'Relative CN homologue 1')
d = file.path(getwd(), 'ABSOLUTE','SampleData',info$sample.name[i], 'Transform')
dir.create(d, recursive = T, showWarnings = F)
dev.print(png, file=file.path(d, paste(info$sample.name[i], '_A1vA2orig.png', sep = '')),
width=640, height=640)
plot1v2(a1 = mat$A1, a2 = mat$A2new, mat = mat, cl = cl, clust = clust, w = w, xlab = 'Relative CN homologue 2',
ylab = 'Relative CN homologue 1')
dev.print(png, file=file.path(d, paste(info$sample.name[i], '_A1vA2scaledX.png', sep = '')),
width=640, height=640)
plot1v2(a1 = mat$A1new, a2 = mat$A2new, mat = mat, cl = cl, clust = clust, w = w, xlab = 'Relative CN homologue 2',
ylab = 'Relative CN homologue 1')
dev.print(png, file=file.path(d, paste(info$sample.name[i], '_A1vA2scaledXY.png', sep = '')),
width=640, height=640)
plot1v2(a1 = mat$A1last, a2 = mat$A2last, mat = mat, cl = cl, clust = clust, w = w, xlab = 'Relative CN homologue 2',
ylab = 'Relative CN homologue 1')
dev.print(png, file=file.path(d, paste(info$sample.name[i], '_A1vA2scaledAll.png', sep = '')),
width=640, height=640)
#Histograms
d = file.path(getwd(), 'ABSOLUTE','SampleData',info$sample.name[i], 'Transform')
myab = histCol(mat = mat, cl = cl, clust = clust, xlab = 'Relative CN (homologues pooled)')
dev.print(png, file=file.path(d, paste(info$sample.name[i], '_Historig.png', sep = '')),
width=640, height=640)
myab = histCol(toplot = c('A1last','A2last'), myab = myab, cl = cl, xlab = 'Relative CN (homologues pooled)')
dev.print(png, file=file.path(d, paste(info$sample.name[i], '_HistscaledAll.png', sep = '')),
width=640, height=640)
###########################################################
#Reset attenuation constant
seg.dat$error.model$AT = 0
#New version or hscr values in as.seg.dat
as.seg.dat$which = 1
as.seg.dat$which[(nr/2+1):nr] = 2
upper = as.seg.dat[as.seg.dat$which == 1,]
tmp = mat[,c('Chromosome','Start.bp','End.bp', 'A1last')]
upper = merge(upper, tmp, all = T)
upper$copy_num = upper$A1last
upper = upper[order(upper$seg.ix),1:9]
lower = as.seg.dat[as.seg.dat$which == 2,]
tmp = mat[,c('Chromosome','Start.bp','End.bp', 'A2last')]
lower = merge(lower, tmp, all = T)
lower$copy_num = lower$A2last
lower = lower[order(lower$seg.ix),1:9]
as.seg.dat = rbind(upper, lower)
seg.dat$as.seg.dat = as.seg.dat
#New version of allele.segs
allele.segs = seg.dat$allele.segs
allele.segs = as.data.frame(allele.segs)
tmp = mat[,c('Chromosome','Start.bp','End.bp', 'A1last', 'A2last')]
allele.segs = merge(allele.segs, tmp, sort = F)
allele.segs$A1.Seg.CN = allele.segs$A1last
allele.segs$A2.Seg.CN = allele.segs$A2last
allele.segs = as.matrix(allele.segs[,1:9])
seg.dat$allele.segs = allele.segs
#New version of seg.info
seg.info = seg.dat$seg.info
seg.info = as.data.frame(seg.info)
tmp = mat[,c('Chromosome','Start.bp','End.bp', 'A1last', 'A2last')]
seg.info = merge(seg.info, tmp, sort = F)
seg.info$cn = (seg.info$A1last + seg.info$A2last)/2
seg.info$copy_num = seg.info$cn
seg.info = as.matrix(seg.info[,1:8])
seg.dat$seg.info = seg.info
#Now switch over so that A2 is always the largest (keep index of switched ones)
ix = which(seg.dat$allele.segs[,'A1.Seg.CN'] > seg.dat$allele.segs[,'A2.Seg.CN'])
a1 = seg.dat$allele.segs[,'A1.Seg.CN']
a2 = seg.dat$allele.segs[,'A2.Seg.CN']
seg.dat$allele.segs[,'A1.Seg.CN'] = pmin(a1, a2)
seg.dat$allele.segs[,'A2.Seg.CN'] = pmax(a1, a2)
seg.dat$switched.allele.segs = ix
nr = nrow(seg.dat$as.seg.dat)
a1 = seg.dat$as.seg.dat[1:(nr/2),'copy_num']
a2 = seg.dat$as.seg.dat[(nr/2+1):(nr),'copy_num']
seg.dat$as.seg.dat[1:(nr/2),'copy_num'] = pmin(a1, a2)
seg.dat$as.seg.dat[(nr/2+1):(nr),'copy_num'] = pmax(a1, a2)
#Print the new object
save(seg.dat, file = file.path(dhapseg, paste(info$sample.name[i], '_', info$arrname[i],
'_scaled_segdat.RData', sep = '')))
################################################################
################################################################
#
# Run ABSOLUTE with scaled haplotype copy values
#
################################################################