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ProgR_5.6_Heterogeneity.R
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ProgR_5.6_Heterogeneity.R
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################################################################
################################################################
# Script: ProgR_5.6_Heterogeneity.R
# Author: Ingrid Lonnstedt
# Date: 23/05/2013
# R version: R 3.0.2
# Details: Derive basic heterogeneity estimates with HAPSEG and ABSOLUTE
# This version handles case when germline SNP arrays exists
# This version creates MAF file with Python script (earlier versions were Perl)
################################################################
################################################################
################################################################
################################################################
#
# 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/HAPSEGnorm/", 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$sample.name = format(info$sample.name, nsmall = 4, trim = T)
info$myarray = info$arrname
setwd(paste(getwd(), '/AROMA', sep=''))
######Find allele intensities
for (i in 1:86){
library(aroma.affymetrix)
dataSet <- "responsify";
tags <- "ACC,ra,-XY,BPN,-XY,RMA,FLN,-XY,CMTN,v2,normalReferences";
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', 'WithNormals', 'SampleData', info$sample.name[i])
dir.create(d, recursive= T, showWarnings = F)
write.table(data, file.path(d, 'Total,fracB.txt'), row.names = F, quote = F, sep = '\t')
}
######Output channel specific intensities
setwd(file.path(getwd(), '/..'))
for (i in 1:86){
d = file.path(getwd(), 'ABSOLUTE', 'WithNormals', 'SampleData', info$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$unitNames
d = file.path(getwd(), 'ABSOLUTE', 'WithNormals', 'SampleData', info$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', 'TAPSWithNormals', 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$unitNames
d = file.path(getwd(), 'ABSOLUTE', 'WithNormals', 'SampleData', info$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 1:56){
print(i)
#Prepare data for this sample
fitname = paste('fit', i, sep = '')
filename <- paste(getwd(), '/AROMA/reports/SNP_segmentation/CBSfitsWithNormals/', 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', 'WithNormals', 'SampleData', info$sample.name[i], 'Segdat.txt')
write.table(seg, d, row.names = F, quote = F, sep = '\t')
}
#Fix the nexus normalized samples
for (i in c(23, 39)){
d = file.path(getwd(), 'TAPS', 'TAPSWithNormals', info$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', 'WithNormals', 'SampleData', info$sample.name[i], 'Segdat.txt')
write.table(seg, d, row.names = F, quote = F, sep = '\t')
}
################################################################
################################################################
#
# HAPSEG
#
################################################################
################################################################
library(HAPSEG)
library(foreach)
library(doMC)
registerDoMC(15)
info = read.delim(paste(prefix.ann, '../../Infokey.txt', sep = ''))
info$myarray = info$arrname
info$sample.name = format(info$sample.name, nsmall = 4, trim = T)
DoHapseg1 <- function(myarray, mysample) {
registerDoSEQ()
plate.name <- mysample
phased.bgl.dir <- "/usr/local/bioinfsoftware/R/current/lib64/R/library/ABSOLUTE/etc/phasedBGL/hg19"
d = file.path(getwd(), 'ABSOLUTE', 'WithNormals', 'SampleData', mysample)
snp.fn <- file.path(d, 'snpfile.RData')
seg.fn = file.path(d, 'Segdat.txt')
results.dir <- file.path(getwd(), 'ABSOLUTE', 'WithNormals', 'HAPSEG1', mysample)
if (!file.exists(results.dir)) {
dir.create(results.dir, recursive=TRUE)
}
print(paste("Starting array", mysample, "at", results.dir))
log.dir <- file.path(getwd(), 'ABSOLUTE', 'WithNormals', 'HAPSEG1', 'log')
if (!file.exists(log.dir)) {
dir.create(log.dir, recursive=TRUE)
}
sink(file=file.path(log.dir, paste(mysample, ".hapseg.out.txt", sep="")))
myRunHapSeg(plate.name, array.name = myarray, seg.fn, snp.fn, genome.build = 'hg19', results.dir, platform = "SNP_6.0",
use.pop='CEPH',
impute.gt=TRUE, plot.segfit=TRUE, merge.small=FALSE, merge.close=TRUE, min.seg.size=2,
normal=FALSE,
out.p=0.001, seg.merge.thresh=1, use.normal = FALSE, adj.atten = FALSE,
phased.bgl.dir, calibrate.data=FALSE,
drop.x=FALSE, verbose=TRUE)
sink()
}
foreach (i=1:56, .combine=c) %dopar% {
DoHapseg1(myarray = info$myarray[i], mysample = info$sample.name[i])
}
DoHapseg2 <- function(myarray, mysample) {
registerDoSEQ()
plate.name <- mysample
phased.bgl.dir <- "/usr/local/bioinfsoftware/R/current/lib64/R/library/ABSOLUTE/etc/phasedBGL/hg19"
d = file.path(getwd(), 'ABSOLUTE', 'WithNormals', 'SampleData', mysample)
snp.fn <- file.path(d, 'snpfile.RData')
seg.fn = file.path(d, 'Segdat.txt')
results.dir <- file.path(getwd(), 'ABSOLUTE', 'WithNormals', 'HAPSEG2test', mysample)
if (!file.exists(results.dir)) {
dir.create(results.dir, recursive=TRUE)
}
print(paste("Starting array", mysample, "at", results.dir))
log.dir <- file.path(getwd(), 'ABSOLUTE', 'WithNormals', 'HAPSEG2test', 'log')
if (!file.exists(log.dir)) {
dir.create(log.dir, recursive=TRUE)
}
sink(file=file.path(log.dir, paste(mysample, ".hapseg.out.txt", sep="")))
myRunHapSeg(plate.name, array.name = myarray, seg.fn, snp.fn, genome.build = 'hg19', results.dir, platform = "SNP_6.0",
use.pop='CEPH',
impute.gt=TRUE, plot.segfit=TRUE, merge.small=FALSE, merge.close=TRUE, min.seg.size=2,
normal=FALSE,
out.p=0.001, seg.merge.thresh=1e-10, use.normal = FALSE, adj.atten = FALSE,
phased.bgl.dir, calibrate.data=FALSE,
drop.x=FALSE, verbose=TRUE)
sink()
}
foreach (i=1:56, .combine=c) %dopar% {
DoHapseg2(myarray = info$myarray[i], mysample = info$sample.name[i])
}
DoHapseg3 <- function(myarray, mysample) {
registerDoSEQ()
plate.name <- mysample
phased.bgl.dir <- "/usr/local/bioinfsoftware/R/current/lib64/R/library/ABSOLUTE/etc/phasedBGL/hg19"
d = file.path(getwd(), 'ABSOLUTE', 'WithNormals', 'SampleData', mysample)
snp.fn <- file.path(d, 'snpfile.RData')
seg.fn = file.path(d, 'Segdat.txt')
results.dir <- file.path(getwd(), 'ABSOLUTE', 'WithNormals', 'HAPSEG3', mysample)
if (!file.exists(results.dir)) {
dir.create(results.dir, recursive=TRUE)
}
print(paste("Starting array", mysample, "at", results.dir))
log.dir <- file.path(getwd(), 'ABSOLUTE', 'WithNormals', 'HAPSEG3', 'log')
if (!file.exists(log.dir)) {
dir.create(log.dir, recursive=TRUE)
}
sink(file=file.path(log.dir, paste(mysample, ".hapseg.out.txt", sep="")))
myRunHapSeg(plate.name, myarray, seg.fn, snp.fn, genome.build = 'hg19', results.dir, platform = "SNP_6.0",
use.pop='CEPH',
impute.gt=TRUE, plot.segfit=TRUE, merge.small=FALSE, merge.close=TRUE, min.seg.size=2,
normal=FALSE,
out.p=0.001, seg.merge.thresh=1e-80, use.normal = FALSE, adj.atten = FALSE,
phased.bgl.dir, calibrate.data=FALSE,
drop.x=FALSE, verbose=TRUE)
sink()
}
foreach (i=1:56, .combine=c) %dopar% {
DoHapseg3(myarray = info$myarray[i], mysample = info$sample.name[i])
}
################################################################
################################################################
#
# Format exome mutation file for ABSOLUTE
#
################################################################
################################################################
##### Python scripts to convert to MAF format
##### Use python 2.7
##### Run in RESPONSIFY/AROMA/rawData/responsify/Exome
##Filter to keep only MuTect mutations
python filterTSV.py -o Data3AllVariants_mutect.tsv -s Data3AllVariants.tsv -v "mutect" -c "Identified" -p "in"
##### Check which mutations are non-silent according to mutect
#####
#####
##### Positions of non-silent mutations in Oncotator example dataset
setwd(paste(getwd(), '/RESPONSIFY', sep=''))
d = file.path(getwd(), 'AROMA', 'rawData', 'responsify','Exome', 'Mutsig_non-silent')
mut = read.delim(file.path(d,"AllVarintsMutectOncotatorPlusMutsigCategNo5214.tsv"))
table(mut$Variant_Classification, mut$effect)
nonsilent = subset(mut, effect == 'nonsilent', select = c('Chromosome', 'Start_position',
'End_position'))
nonsilent$these_are_nonsilent = 1
nonsilent$Chromosome = as.character(nonsilent$Chromosome)
##### "Consequences" classes that occur in our Ensemble dataset out of those above
d = file.path(getwd(), 'AROMA', 'rawData', 'responsify', 'Exome', "Data3AllVariants.tsv")
mut = read.delim(d)
mut = subset(mut, select = c('X.CHROM','POS','Consequence'))
mut$X.CHROM = as.character(mut$X.CHROM)
conseq = NULL
for (i in 1:nrow(nonsilent)){
tmp = (1:nrow(mut))[mut$X.CHROM == nonsilent$Chromosome[i] &
mut$POS == nonsilent$Start_position[i]]
if (length(tmp) == 0) next
conseq = c(conseq,as.character(mut$Consequence[tmp]))
}
unique(conseq)
#These include 17 synonymous variants and 5 synonymous_variant,NMD_transcript_variants
#I exclude those but use the other categories.
myconseq = setdiff(unique(conseq),c('synonymous_variant,NMD_transcript_variant','synonymous_variant'))
[1] "non_coding_exon_variant,nc_transcript_variant"
[2] "missense_variant"
[3] "intron_variant"
[4] "downstream_gene_variant"
[5] "3_prime_UTR_variant,NMD_transcript_variant"
[6] "upstream_gene_variant"
[7] "intron_variant,nc_transcript_variant"
[8] "missense_variant,NMD_transcript_variant"
[9] "incomplete_terminal_codon_variant,coding_sequence_variant"
[10] "missense_variant,splice_region_variant"
[11] "intron_variant,NMD_transcript_variant"
[12] "3_prime_UTR_variant"
[13] "5_prime_UTR_variant"
[14] "missense_variant,splice_region_variant,NMD_transcript_variant"
[15] "splice_region_variant,non_coding_exon_variant,nc_transcript_variant"
[16] "splice_region_variant,intron_variant"
[17] "5_prime_UTR_variant,NMD_transcript_variant"
[18] "splice_region_variant,3_prime_UTR_variant,NMD_transcript_variant"
[19] "initiator_codon_variant"
[20] "initiator_codon_variant,splice_region_variant"
[21] "splice_donor_variant,nc_transcript_variant"
[22] "splice_donor_variant"
[23] "stop_lost,NMD_transcript_variant"
[24] "splice_region_variant,intron_variant,NMD_transcript_variant"
[25] "splice_acceptor_variant,nc_transcript_variant"
[26] "intergenic_variant"
[27] "splice_region_variant,5_prime_UTR_variant"
[28] "splice_region_variant,synonymous_variant,NMD_transcript_variant"
[29] "splice_region_variant,intron_variant,nc_transcript_variant"
##### Now filter out mutations not in myconseq.
d = file.path(getwd(), 'AROMA', 'rawData', 'responsify', 'Exome', "Data3AllVariants_mutect.tsv")
mut = read.delim(d, check.names = F)
mut = subset(mut, Consequence %in% myconseq)
##### Filter out mutations with <20 read depth
mut = subset(mut, 'Tumour:DP'>=20)
d = file.path(getwd(), 'AROMA', 'rawData', 'responsify', 'Exome', "Data3AllVariants_mutect_nonsilent_mindepth20.tsv")
write.table(mut, file = d,row.names = F, quote = F, sep = '\t', na = '.')
##### Python scripts to convert to MAF format
##### Use python 2.7
##### Run in RESPONSIFY/AROMA/rawData/responsify/Exome
##Transform to one large common MAF file
python variant_to_maf.py -v Data3AllVariants_mutect_nonsilent_mindepth20.tsv -g HUGO_EntrezIDs.txt -o Data3AllVariants_mutect_nonsilent_mindepth20.maf
##### Split maf file into individual maf files
##### and change formats and add additional required variables
#####
setwd(paste(getwd(), '/RESPONSIFY', sep=''))
#setwd('/Users/lonnstedt/Documents/RESPONSIFY')
info = read.delim(paste(prefix.ann, '../../Infokey.txt', sep = ''), stringsAsFactors = F)
info$sample.name = format(info$sample.name, nsmall = 4, trim = T)
d = file.path(getwd(), 'AROMA', 'rawData', 'responsify', 'Exome',
'Data3AllVariants_mutect_nonsilent_mindepth20.maf')
maf = read.delim(d, row.names = NULL, stringsAsFactors = FALSE,
check.names = FALSE, na.strings = c("NA", "---"),
blank.lines.skip = TRUE, comment.char = "#")
maf$Tumor_Sample_Barcode = paste(unlist(lapply(strsplit(maf$Tumor_Sample_Barcode, split = '_', fixed = T),
function(x){x[[2]]})), 'T', sep = '')
d = file.path(getwd(), 'AROMA', 'rawData', 'responsify', 'Exome', "Data3AllVariants_mutect_nonsilent_mindepth20.tsv")
mut = read.delim(d, dec = ',')
dat = data.frame(Tumor_Sample_Barcode = paste(mut$Sample, 'T', sep = ''),
Chromosome = as.character(mut$X.CHROM), Start_position = mut$POS,
t_alt_count = mut$Tumor.AD,
t_ref_count = mut$Tumor.RD, stringsAsFactors = F)
dat = unique(dat)
nrow(maf)
maf = merge(maf, dat, all = T, sort = F)
nrow(maf)
mafbase = "Data3AllVariants_mutect_nonsilent_mindepth20_withAlleleFreqs"
for (i in 1:56){
samp = info$sample[i]
out = subset(maf, Tumor_Sample_Barcode == paste(samp, 'T', sep = ''))
d = file.path(getwd(), 'AROMA', 'rawData', 'responsify', 'Exome', 'MAF')
if (nrow(out)>0) write.table(out, file = paste(d, '/',
mafbase, '_', i,'.', samp, '.maf', sep = ''), row.names = F, quote = F,
sep = '\t')
}
#Output a large common MAF file with allele frequencies
d = file.path(getwd(), 'AROMA', 'rawData', 'responsify', 'Exome')
write.table(maf, file = paste(d, '/',
mafbase, '.maf', sep = ''), row.names = F, quote = F,
sep = '\t')
### Which samples have maf files?
samples = as.numeric(substr(unique(maf$Tumor_Sample_Barcode), 1,4))
inds = info$ind[info$sample %in% samples & info$tissue == 'T']
paste(inds, collapse = ',')
[1] "1,2,3,4,5,6,8,9,10,11,13,14,18,19,20,21,24,26,30,32,33,34,35,36,38,39,40,41,42,43,45,46,48,49,50,52,53,55,56"
inds = c(1,2,3,4,5,6,8,9,10,11,13,14,18,19,20,21,24,26,30,32,33,34,35,36,38,39,40,41,42,43,45,46,48,49,50,52,53,55,56)
#These samples still miss exome data:
setdiff(1:56, inds)
[1] 7 12 15 16 17 22 23 25 27 28 29 31 37 44 47 51 54
################################################################
################################################################
#
# Run ABSOLUTE without scaling, just for comparison
#
################################################################
################################################################
library(ABSOLUTE)
library(foreach)
library(doMC)
registerDoMC(10)
info = read.delim(paste(prefix.ann, '../../Infokey.txt', sep = ''))
info$myarray = info$arrname
info$sample.name = format(info$sample.name, nsmall = 4, trim = T)
DoAbsolute <- function(myarray, mysample, i) {
registerDoSEQ()
sample.name <- mysample
maf.fn = paste(getwd(),
'/AROMA/rawData/responsify/Exome/MAF/Data3AllVariants_mutect_nonsilent_mindepth20_withAlleleFreqs', '_',
mysample, '.maf', sep = '')
seg.dat.fn <- paste(getwd(), '/ABSOLUTE/WithNormals/HAPSEG2/', mysample,'/',mysample,'_',myarray,
"_segdat.RData", sep="")
results.dir <- paste(getwd(), '/ABSOLUTE/WithNormals/RunAbsoluteUnscaled', sep = '')
print(paste("Starting myarray", mysample, "at", results.dir))
log.dir <- paste(getwd(), '/ABSOLUTE/WithNormals/RunAbsoluteUnscaled/abs_logs', sep = '')
if (!file.exists(log.dir)) {
dir.create(log.dir, recursive=TRUE)
}
if (!file.exists(results.dir)) {
dir.create(results.dir, recursive=TRUE)
}
sink(file=file.path(log.dir, paste(mysample, ".abs.out.txt", sep="")))
myRunAbsolute(seg.dat.fn, sigma.p = 0, max.sigma.h = 0.01, min.ploidy = 0.95, max.ploidy = 12,
primary.disease = "Breast Cancer",
platform = 'SNP_6.0', sample.name, results.dir, max.as.seg.count = 1500,
max.non.clonal = 0,
max.neg.genome = 0, copy_num_type = 'allelic', verbose=TRUE, maf.fn = maf.fn,
min.mut.af = 0.03, output.fn.base = mysample)
sink()
}
foreach (i = inds, .combine=c) %dopar% {
DoAbsolute(myarray = info$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)
################################################################
################################################################
#
# Transform: see ProgR_6...R
#
################################################################
################################################################
################################################################
################################################################
#
# Run ABSOLUTE with scaled haplotype copy values
#
################################################################
################################################################
library(ABSOLUTE)
library(foreach)
library(doMC)
info = read.delim(paste(prefix.ann, '../../Infokey.txt', sep = ''))
info$sample.name = format(info$sample.name, nsmall = 4, trim = T)
registerDoMC(10)
DoAbsolute <- function(myarray, mysample, i, h) {
registerDoSEQ()
sample.name <- mysample
maf.fn = paste(getwd(),
'/AROMA/rawData/responsify/Exome/MAF/Data3AllVariants_mutect_nonsilent_mindepth20_withAlleleFreqs', '_',
mysample, '.maf', sep = '')
seg.dat.fn <- paste(getwd(), '/ABSOLUTE/WithNormals/HAPSEG', h, '/', mysample,'/',mysample,'_',myarray,
"_scaled_segdat.RData", sep="")
if (!file.exists(seg.dat.fn)) seg.dat.fn = paste(getwd(), '/ABSOLUTE/WithNormals/HAPSEG', h, '/', mysample,'/',mysample,'_',myarray,
"_segdat.RData", sep="")
results.dir <- paste(getwd(), '/ABSOLUTE/WithNormals/Absolute', sep = '')
print(paste("Starting myarray", mysample, "at", results.dir))
log.dir <- paste(getwd(), '/ABSOLUTE/WithNormals/Absolute/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="")))
myRunAbsolute2(seg.dat.fn, sigma.p = 0, max.sigma.h = 0.01, min.ploidy = 2, max.ploidy = 15,
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, b.res = 0.1, d.res = 0.1)
sink()
}
#hapsegind = c(2, 1, 1, 1, 1, 2)
#names(hapsegind)=as.character(c(21, 27, 40, 51, 20, 23))
foreach (i = c(30, 24, 56, 36, 3, 21), .combine=c) %dopar% {
DoAbsolute(myarray = info$arrname[i], mysample = info$sample.name[i], i, h = 2)
}
d = paste(getwd(), '/ABSOLUTE/ScaledAbsolute', sep = '')
obj.name = '15,20,21,23,27,40,51'
CreateReviewObject(obj.name=obj.name, absolute.files=paste(d, '/',
info$sample.name[c(15,20,21,23,27,40,51)], '.ABSOLUTE.RData', sep = ''),
indv.results.dir = file.path(d, obj.name), copy_num_type = "allelic",
plot.modes = TRUE, verbose=TRUE)
obj.name = '15,21'
d = paste(getwd(), '/ABSOLUTE/ScaledAbsolute', sep = '')
calls.path = file.path(d, obj.name, paste(obj.name, ".PP-calls_tab.txt", sep = ''))
modes.path = file.path(d, obj.name, paste(obj.name, ".PP-modes.data.RData", sep = ''))
output.path = file.path(d, obj.name)
ExtractReviewedResults(reviewed.pp.calls.fn = calls.path, analyst.id = "IL",
modes.fn = modes.path, out.dir.base = output.path,
obj.name = "AfterScaling", copy_num_type="allelic")
obj.name = '15_subset'
myCreateReviewObject(obj.name=obj.name, absolute.files=paste(d, '/', info$sample.name[c(15)],
'.ABSOLUTE.RData', sep = ''),
indv.results.dir = file.path(d, obj.name), copy_num_type = "allelic",
plot.modes = TRUE, verbose=TRUE, myModes = c(1, 2, 4, 10))
#######Junk
foreach (i = c(2, 3, 12:16, 20:27, 29, 30, 34, 36, 51, 53, 56), .combine=c) %dopar% {
DoAbsolute(myarray = info$myarray[i], mysample = info$sample.name[i], i)
}
tmp = seg.dat[['obs.scna']][['segtab']]
tmp = tmp[tmp$Chromosome == 3,]
plot(range(c(tmp$Start.pb, tmp$End.bp)), range(tmp$copy_num), type = 'n',
xlim = c( 178910000, 178950000))
for (r in 1:nrow(tmp)) lines(c(tmp$Start.bp[r], tmp$End.bp[r]), rep(tmp$copy_num[r], 2))
abline(v = 178927976, col = 'red')
plot(range(c(tmp$tcnStart, tmp$tcnEnd)), range(tmp$tcnMean, na.rm = T), type = 'n',
xlim = c( 178910000, 178950000))
for (r in 1:nrow(tmp)) lines(c(tmp$tcnStart[r], tmp$tcnEnd[r]), rep(tmp$tcnMean[r], 2))
abline(v = 178927976, col = 'red')
################################################################
################################################################
#
# BAF evaluation
#
################################################################
################################################################
info = read.delim(paste(prefix.ann, '../../Infokey.txt', sep = ''), stringsAsFactors = F)
info$sample.name = format(info$sample.name, nsmall = 4, trim = T)
library(IRanges)
library(Rsolnp)
library(limma)
i = 15
solutions = c(1,2,4,10)
d = file.path(getwd(), 'ABSOLUTE', 'ScaledAbsolute')
d = paste(d,'/', '15,21', sep = '')
load(file.path(d, "15,21.PP-modes.data.RData"))
tab = segobj.list[[info$sample.name[i]]][['mode.res']][['mode.tab']][solutions,]
tab = tab[order(tab[,'alpha']),]
as.seg.dat = segobj.list[[info$sample.name[i]]][['as.seg.dat']]
nr = as.integer(nrow(as.seg.dat))
mat = as.seg.dat[1:(nr/2),]
names(mat)[6] = 'A1'
mat$A2 = as.seg.dat$copy_num[(nr/2+1):(nr)]
mat$W = mat$W*2
mat$baf = mat$A2/(mat$A1 + mat$A2)
###Read selected segments
tmp = getKnownClustersAndOverlaps(mat = mat, d = file.path(getwd(),'ABSOLUTE', 'SampleData', info$sample.name[i]))
cl = tmp$cl
clust = tmp$clust
w = tmp$w
#Lowest CN guess of chosen segments
y = mat$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 lowest guesses in dataframe
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)))
##########################################################
#
#Segments to use
ix = which(clust != '' & dd$xclust != dd$yclust)
#Observed BAFs
BAFo = mat$baf[ix]
#Weights
W = w[ix]
ss = numeric(nrow(tab))
for (j in 1:nrow(tab)){
#Theoretical BAFs
add = j-1
a2 = dd$xclust[ix] + add
a1 = dd$yclust[ix] + add
alpha = tab[j,'alpha']
BAFe = (alpha*a2 + (1-alpha))/(alpha*(a1+a2) + (1-alpha)*2)
#Out
ss[j] = sum(W*(BAFo-BAFe)**2)
}
plot(tab[,'alpha'], ss, type= 'b', log = 'y')
BAFe = (alpha*a2 + (1-alpha))/(alpha*(a1+a2) + (1-alpha)*2)
ebaf = function(a1, a2, alpha){(alpha*a2 + (1-alpha))/(alpha*(a1+a2) + (1-alpha)*2)}
ebaf(1,2, .28)
ebaf(2,3, .38)
##########################################################
#Optimize one issue at the time
### Scale x
###
dx = subset(dd, !is.na(xclust))
pars = solnp(pars = c(1,0), fun = minx, myclust = dx$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$arrname[i],
'_scaled_segdat.RData', sep = '')))
################################################################
################################################################
#
# Simulation of dataset for Hapseg
# This never passed HAPSEG
################################################################
################################################################
info = read.delim(paste(prefix.ann, '../../Infokey.txt', sep = ''), stringsAsFactors = F)
info$sample.name = format(info$sample.name, nsmall = 4, trim = T)
info$myarray = info$arrname
i = 15
#Read real data and segments
d = file.path(getwd(), 'ABSOLUTE', 'SampleData', info$sample.name[i])
data = read.delim(file.path(d, 'Total,fracB.txt'), stringsAsFactors = F)
d = file.path(getwd(), 'ABSOLUTE', 'SampleData', info$sample.name[i], 'Segdat.txt')
newd = file.path(getwd(), 'ABSOLUTE', 'Simulation', 'SampleData', info$sample.name[i])
dir.create(newd, recursive = T, showWarnings = F)
file.copy(d, file.path(newd, 'Segdat.txt'))
seg = read.delim(d)
#Build matrix of possible copy numbers
cns = rbind(data.frame(t = 6, maj = 3:4, min = 3:2),
data.frame(t = 7, maj = 4:5, min = 3:2),
data.frame(t = 8, maj = 4:6, min = 4:2),
data.frame(t = 9, maj = 5, min = 4))
#Simulate observations from those copy numbers
set.seed(100)
copyrow = ceiling(runif(nrow(seg))*nrow(cns))
cop = cns[copyrow,]
cop$baf1 = cop$min/cop$t
cop$baf2 = cop$maj/cop$t
cop$imba = cop$baf2 - cop$baf1
data$total = NA
data$fracB = NA
data$A = NA
data$B = NA
for (r in 1:nrow(seg)){
ix = which(data$chromosome == seg$Chromosome[r] & data$position>=seg$Start.Position[r] &
data$position<=seg$End.Position[r])
noise = matrix(rnorm(2*length(ix), sd = .5), ncol = 2)
total = (cop$t[r] + noise[,1] + noise[,2])
baf1 = (noise[,1])/total
baf2 = (cop$min[r] + noise[,1])/total
baf3 = (cop$maj[r] + noise[,1])/total
baf4 = (cop$t[r] + noise[,1])/total
rand = ceiling(runif(length(ix)))
A = numeric(length(ix))
B = numeric(length(ix))
fracB = numeric(length(ix))
for (j in 1:4){
ind = which(rand == j)
bafj = get(paste('baf', j, sep = ''))
A[ind] = total[ind]*(1-bafj[ind])
B[ind] = total[ind]*bafj[ind]
fracB[ind] = bafj[ind]
}
data$A[ix] = A
data$B[ix] = B
data$fracB[ix] = fracB
data$total[ix] = total
}
#Example BAF plot
plot(data$position[ix], baf1, pch = '.', ylim = c(0,1), xlab =paste( 'Position on Chromosome',
seg$Chromosome[r]), ylab = 'B Allele Fraction')
points(data$position[ix], baf2, pch = '.')
points(data$position[ix], baf3, pch = '.')
points(data$position[ix], baf4, pch = '.')
dev.print(png, file = file.path(getwd(), 'ABSOLUTE', 'Simulation', 'SampleData',
info$sample.name[i], 'BAFs.png'), height = 400, width = 640)
#Print dataset
data$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', 'Simulation', 'SampleData', info$sample.name[i])
dir.create(d, recursive = T, showWarnings = F)
save(dat, file = file.path(d, 'snpfile.RData'))
dat = subset(data, select = c('unitNames','chromosome','position','total', 'fracB'))
write.table(data, file.path(d, 'Total,fracB.txt'), row.names = F, quote = F, sep = '\t')
################################################################
################################################################
#
# Sample 15 rescale again
#
################################################################
################################################################
info = read.delim(paste(prefix.ann, '../../Infokey.txt', sep = ''), stringsAsFactors = F)
info$sample.name = format(info$sample.name, nsmall = 4, trim = T)
library(IRanges)
library(Rsolnp)
library(limma)
i = 21
d = file.path(getwd(), 'ABSOLUTE', 'ScaledAbsolute')
d = paste(d,'/', '15,20,21,23,27,40,51', sep = '')
load(file.path(d, "15,20,21,23,27,40,51.PP-modes.data.RData"))
modpars = segobj.list[['21.4669']]$mode.res$mode.tab[2,]
delta = modpars['delta']
d = file.path(getwd(), 'ABSOLUTE', 'HAPSEG2')
d = paste(d,'/', info$sample.name[i], sep = '')
seg.dat.fn = file.path(d, paste(info$sample.name[i], '_', info$arrname[i], '_scaled_segdat.RData', sep = ''))
seg.dat = myMakeSegObj(seg.dat.fn, filter_segs=FALSE, min_probes=10, max_sd=100,
verbose=TRUE)
as.seg.dat = seg.dat$as.seg.dat