inst/example/battenberg_snp6.R
In Wedge-lab/battenberg: Battenberg subclonal copy number caller
args = commandArgs(TRUE)
TUMOURNAME = toString(args[1])
NORMALCEL = toString(args[2])
TUMOURCEL = toString(args[3])
RUN_DIR = toString(args[4])
NORMALNAME = NA
SKIP_PREPROCESSING = F
SKIP_PHASING = F
library(Battenberg)
###############################################################################
# 2017-10-03
# A pure R Battenberg v2.2.7 SNP6 pipeline implementation.
# sd11@sanger.ac.uk
###############################################################################
# Parallelism parameters
NTHREADS = 8
# General static
IMPUTEINFOFILE = "/lustre/scratch116/casm/team113/sd11/reference/GenomeFiles/battenberg_impute/impute_info.txt"
G1000PREFIX = "/lustre/scratch116/casm/team113/sd11/reference/GenomeFiles/battenberg_1000genomesloci2012/1000genomesAlleles2012_chr"
IMPUTE_EXE = "impute2"
# General SNP6 specific
PROBLEMLOCI = NA
SNP6_REF_INFO_FILE = "/lustre/scratch116/casm/team113/sd11/reference/GenomeFiles/battenberg_snp6/snp6_ref_info_file.txt"
APT_PROBESET_GENOTYPE_EXE = "apt-probeset-genotype"
APT_PROBESET_SUMMARIZE_EXE = "apt-probeset-summarize"
NORM_GENO_CLUST_EXE = "normalize_affy_geno_cluster.pl"
BIRDSEED_REPORT_FILE = "birdseed.report.txt" # No control over the name of this file, as it is automatically generated by APT within cel2baf.logr
# Parameters
PLATFORM_GAMMA = 0.55
PHASING_GAMMA = 1
SEGMENTATION_GAMMA = 10
SEGMENTATIIN_KMIN = 3
PHASING_KMIN = 1
CLONALITY_DIST_METRIC = 0
ASCAT_DIST_METRIC = 1
MIN_PLOIDY = 1.6 #1.6
MAX_PLOIDY = 4.8 #4.8
MIN_RHO = 0.13 #0.1
MAX_RHO = 1.02 #1
MIN_GOODNESS_OF_FIT = 0.63
BALANCED_THRESHOLD = 0.51
MIN_NORMAL_DEPTH = 10
CALC_SEG_BAF_OPTION = 1
HETEROZYGOUSFILTER = "none"
# Change to work directory and load the chromosome information
setwd(RUN_DIR)
battenberg(tumourname=TUMOURNAME,
normalname=NORMALNAME,
tumour_data_file=TUMOURCEL,
normal_data_file=NORMALCEL,
imputeinfofile=IMPUTEINFOFILE,
g1000prefix=G1000PREFIX,
problemloci=PROBLEMLOCI,
data_type="snp6",
impute_exe=IMPUTE_EXE,
nthreads=NTHREADS,
platform_gamma=PLATFORM_GAMMA,
phasing_gamma=PHASING_GAMMA,
segmentation_gamma=SEGMENTATION_GAMMA,
segmentation_kmin=SEGMENTATIIN_KMIN,
phasing_kmin=PHASING_KMIN,
clonality_dist_metric=CLONALITY_DIST_METRIC,
ascat_dist_metric=ASCAT_DIST_METRIC,
min_ploidy=MIN_PLOIDY,
max_ploidy=MAX_PLOIDY,
min_rho=MIN_RHO,
min_goodness=MIN_GOODNESS_OF_FIT,
uninformative_BAF_threshold=BALANCED_THRESHOLD,
calc_seg_baf_option=CALC_SEG_BAF_OPTION,
skip_preprocessing=SKIP_PREPROCESSING,
skip_phasing=SKIP_PHASING,
snp6_reference_info_file=SNP6_REF_INFO_FILE,
apt.probeset.genotype.exe=APT_PROBESET_GENOTYPE_EXE,
apt.probeset.summarize.exe=APT_PROBESET_SUMMARIZE_EXE,
norm.geno.clust.exe=NORM_GENO_CLUST_EXE,
birdseed_report_file=BIRDSEED_REPORT_FILE,
heterozygousFilter=HETEROZYGOUSFILTER)
# # Change to work directory and load the chromosome information
# setwd(RUN_DIR)
# chrom_names = get.chrom.names(IMPUTEINFOFILE, TRUE)
#
# # Setup for parallel computing
# clp = makeCluster(NTHREADS)
# registerDoParallel(clp)
#
# # Extract the LogR and BAF from both tumour and normal cel files.
# cel2baf.logr(normal_cel_file=NORMALCEL,
# tumour_cel_file=TUMOURCEL,
# output_file=paste(TUMOURNAME, "_lrr_baf.txt", sep=""),
# snp6_reference_info_file=SNP6_REF_INFO_FILE,
# apt.probeset.genotype.exe=APT_PROBESET_GENOTYPE_EXE,
# apt.probeset.summarize.exe=APT_PROBESET_SUMMARIZE_EXE,
# norm.geno.clust.exe=NORM_GENO_CLUST_EXE)
#
# gc.correct(samplename=TUMOURNAME,
# infile.logr.baf=paste(TUMOURNAME, "_lrr_baf.txt", sep=""),
# outfile.tumor.LogR=paste(TUMOURNAME, "_mutantLogR.tab", sep=""),
# outfile.tumor.BAF=paste(TUMOURNAME, "_mutantBAF.tab", sep=""),
# outfile.normal.LogR=paste(TUMOURNAME, "_germlineLogR.tab", sep=""),
# outfile.normal.BAF=paste(TUMOURNAME, "_germlineBAF.tab", sep=""),
# outfile.probeBAF=paste(TUMOURNAME, "_probeBAF.txt", sep=""),
# snp6_reference_info_file=SNP6_REF_INFO_FILE,
# birdseed_report_file=BIRDSEED_REPORT_FILE,
# chr_names=chrom_names)
#
# # Infer what the gender is
# gender = infer_gender_birdseed(BIRDSEED_REPORT_FILE)
# is_male = gender == "male"
# chrom_names = get.chrom.names(IMPUTEINFOFILE, is_male)
#
# foreach(chrom=1:length(chrom_names), .export=c("generate.impute.input.snp6","run.impute","combine.impute.output","GetChromosomeBAFs_SNP6","plot.haplotype.data")) %dopar% {
#
# # Transform input into a format that Impute2 takes
# generate.impute.input.snp6(infile.germlineBAF=paste(TUMOURNAME, "_germlineBAF.tab", sep=""),
# infile.tumourBAF=paste(TUMOURNAME, "_mutantBAF.tab", sep=""),
# outFileStart=paste(TUMOURNAME, "_impute_input_chr", sep=""),
# chrom=chrom,
# chr_names=chrom_names,
# problemLociFile=PROBLEMLOCI,
# snp6_reference_info_file=SNP6_REF_INFO_FILE,
# imputeinfofile=IMPUTEINFOFILE,
# is.male=is_male,
# heterozygousFilter=HETEROZYGOUSFILTER)
#
# # Run impute on the files
# run.impute(inputfile=paste(TUMOURNAME, "_impute_input_chr", chrom, ".txt", sep=""),
# outputfile.prefix=paste(TUMOURNAME, "_impute_output_chr", chrom, ".txt", sep=""),
# is.male=is_male,
# imputeinfofile=IMPUTEINFOFILE,
# chrom=chrom,
# impute.exe=IMPUTE_EXE)
#
# # As impute runs in windows across a chromosome we need to assemble the output
# combine.impute.output(inputfile.prefix=paste(TUMOURNAME, "_impute_output_chr", chrom, ".txt", sep=""),
# outputfile=paste(TUMOURNAME, "_impute_output_chr", chrom, "_allHaplotypeInfo.txt", sep=""),
# is.male=is_male,
# imputeinfofile=IMPUTEINFOFILE,
# region.size=5000000,
# chrom=chrom)
#
# # Transform the impute output into haplotyped BAFs
# GetChromosomeBAFs_SNP6(chrom=chrom,
# alleleFreqFile=paste(TUMOURNAME, "_impute_input_chr", chrom, "_withAlleleFreq.csv", sep=""),
# haplotypeFile=paste(TUMOURNAME, "_impute_output_chr", chrom, "_allHaplotypeInfo.txt", sep=""),
# samplename=TUMOURNAME,
# outputfile=paste(TUMOURNAME, "_chr", chrom, "_heterozygousMutBAFs_haplotyped.txt", sep=""),
# chr_names=chrom_names)
#
# # Plot what we have until this point
# plot.haplotype.data(haplotyped.baf.file=paste(TUMOURNAME, "_chr", chrom, "_heterozygousMutBAFs_haplotyped.txt", sep=""),
# imageFileName=paste(TUMOURNAME,"_chr",chrom_names[chrom],"_heterozygousData.png",sep=""),
# samplename=TUMOURNAME,
# chrom=chrom,
# chr_names=chrom_names)
#
# # Cleanup temp Impute output
# unlink(paste(TUMOURNAME, "_impute_output_chr", chrom, ".txt_*K.txt*", sep=""))
# }
#
# # Kill the threads as from here its all single core
# stopCluster(clp)
#
# # Combine all the BAF output into a single file
# combine.baf.files(inputfile.prefix=paste(TUMOURNAME, "_chr", sep=""),
# inputfile.postfix="_heterozygousMutBAFs_haplotyped.txt",
# outputfile=paste(TUMOURNAME, "_heterozygousMutBAFs_haplotyped.txt", sep=""),
# no.chrs=length(chrom_names))
#
# # Segment the phased and haplotyped BAF data
# # Call segment.baf.phased.sv when SVs are available - not relevant for SNP6 data
# segment.baf.phased(samplename=TUMOURNAME,
# inputfile=paste(TUMOURNAME, "_heterozygousMutBAFs_haplotyped.txt", sep=""),
# outputfile=paste(TUMOURNAME, ".BAFsegmented.txt", sep=""),
# gamma=SEGMENTATION_GAMMA,
# phasegamma=PHASING_GAMMA,
# kmin=3,
# phasekmin=1,
# calc_seg_baf_option=CALC_SEG_BAF_OPTION)
#
# # Fit a clonal copy number profile
# fit.copy.number(samplename=TUMOURNAME,
# outputfile.prefix=paste(TUMOURNAME, "_", sep=""),
# inputfile.baf.segmented=paste(TUMOURNAME, ".BAFsegmented.txt", sep=""),
# inputfile.baf=paste(TUMOURNAME,"_mutantBAF.tab", sep=""),
# inputfile.logr=paste(TUMOURNAME,"_mutantLogR.tab", sep=""),
# dist_choice=CLONALITY_DIST_METRIC,
# ascat_dist_choice=ASCAT_DIST_METRIC,
# min.ploidy=MIN_PLOIDY,
# max.ploidy=MAX_PLOIDY,
# min.rho=MIN_RHO,
# max.rho=MAX_RHO,
# min.goodness=MIN_GOODNESS_OF_FIT,
# uninformative_BAF_threshold=BALANCED_THRESHOLD,
# gamma_param=PLATFORM_GAMMA,
# use_preset_rho_psi=F,
# preset_rho=NA,
# preset_psi=NA,
# read_depth=30)
#
# # Go over all segments, determine which segements are a mixture of two states and fit a second CN state
# callSubclones(sample.name=TUMOURNAME,
# baf.segmented.file=paste(TUMOURNAME, ".BAFsegmented.txt", sep=""),
# logr.file=paste(TUMOURNAME,"_mutantLogR.tab", sep=""),
# rho.psi.file=paste(TUMOURNAME, "_rho_and_psi.txt",sep=""),
# output.file=paste(TUMOURNAME,"_subclones.txt", sep=""),
# output.figures.prefix=paste(TUMOURNAME,"_subclones_chr", sep=""),
# output.gw.figures.prefix=paste(TUMOURNAME,"_BattenbergProfile", sep=""),
# masking_output_file=paste(TUMOURNAME,"_segment_masking_details.txt", sep=""),
# chr_names=chrom_names,
# gamma=PLATFORM_GAMMA,
# segmentation.gamma=NA,
# siglevel=0.05,
# maxdist=0.01,
# noperms=1000,
# max_allowed_state=250,
# sv_breakpoints_file="NA",
# calc_seg_baf_option=CALC_SEG_BAF_OPTION)
#
# # Make some post-hoc plots
# make_posthoc_plots(samplename=TUMOURNAME,
# logr_file=paste(TUMOURNAME, "_mutantLogR.tab", sep=""),
# subclones_file=paste(TUMOURNAME, "_subclones.txt", sep=""),
# rho_psi_file=paste(TUMOURNAME, "_rho_and_psi.txt", sep=""),
# bafsegmented_file=paste(TUMOURNAME, ".BAFsegmented.txt", sep=""),
# logrsegmented_file=paste(TUMOURNAME, ".logRsegmented.txt", sep=""),
# allelecounts_file=NULL)
#
# # Save refit suggestions for a future rerun
# cnfit_to_refit_suggestions(samplename=TUMOURNAME,
# subclones_file=paste(TUMOURNAME, "_subclones.txt", sep=""),
# rho_psi_file=paste(TUMOURNAME, "_rho_and_psi.txt", sep=""),
# gamma_param=PLATFORM_GAMMA)
Wedge-lab/battenberg documentation built on July 31, 2023, 1:32 p.m.
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args = commandArgs(TRUE)
TUMOURNAME = toString(args[1])
NORMALCEL = toString(args[2])
TUMOURCEL = toString(args[3])
RUN_DIR = toString(args[4])
NORMALNAME = NA
SKIP_PREPROCESSING = F
SKIP_PHASING = F
library(Battenberg)
###############################################################################
# 2017-10-03
# A pure R Battenberg v2.2.7 SNP6 pipeline implementation.
# sd11@sanger.ac.uk
###############################################################################
# Parallelism parameters
NTHREADS = 8
# General static
IMPUTEINFOFILE = "/lustre/scratch116/casm/team113/sd11/reference/GenomeFiles/battenberg_impute/impute_info.txt"
G1000PREFIX = "/lustre/scratch116/casm/team113/sd11/reference/GenomeFiles/battenberg_1000genomesloci2012/1000genomesAlleles2012_chr"
IMPUTE_EXE = "impute2"
# General SNP6 specific
PROBLEMLOCI = NA
SNP6_REF_INFO_FILE = "/lustre/scratch116/casm/team113/sd11/reference/GenomeFiles/battenberg_snp6/snp6_ref_info_file.txt"
APT_PROBESET_GENOTYPE_EXE = "apt-probeset-genotype"
APT_PROBESET_SUMMARIZE_EXE = "apt-probeset-summarize"
NORM_GENO_CLUST_EXE = "normalize_affy_geno_cluster.pl"
BIRDSEED_REPORT_FILE = "birdseed.report.txt" # No control over the name of this file, as it is automatically generated by APT within cel2baf.logr
# Parameters
PLATFORM_GAMMA = 0.55
PHASING_GAMMA = 1
SEGMENTATION_GAMMA = 10
SEGMENTATIIN_KMIN = 3
PHASING_KMIN = 1
CLONALITY_DIST_METRIC = 0
ASCAT_DIST_METRIC = 1
MIN_PLOIDY = 1.6 #1.6
MAX_PLOIDY = 4.8 #4.8
MIN_RHO = 0.13 #0.1
MAX_RHO = 1.02 #1
MIN_GOODNESS_OF_FIT = 0.63
BALANCED_THRESHOLD = 0.51
MIN_NORMAL_DEPTH = 10
CALC_SEG_BAF_OPTION = 1
HETEROZYGOUSFILTER = "none"
# Change to work directory and load the chromosome information
setwd(RUN_DIR)
battenberg(tumourname=TUMOURNAME,
normalname=NORMALNAME,
tumour_data_file=TUMOURCEL,
normal_data_file=NORMALCEL,
imputeinfofile=IMPUTEINFOFILE,
g1000prefix=G1000PREFIX,
problemloci=PROBLEMLOCI,
data_type="snp6",
impute_exe=IMPUTE_EXE,
nthreads=NTHREADS,
platform_gamma=PLATFORM_GAMMA,
phasing_gamma=PHASING_GAMMA,
segmentation_gamma=SEGMENTATION_GAMMA,
segmentation_kmin=SEGMENTATIIN_KMIN,
phasing_kmin=PHASING_KMIN,
clonality_dist_metric=CLONALITY_DIST_METRIC,
ascat_dist_metric=ASCAT_DIST_METRIC,
min_ploidy=MIN_PLOIDY,
max_ploidy=MAX_PLOIDY,
min_rho=MIN_RHO,
min_goodness=MIN_GOODNESS_OF_FIT,
uninformative_BAF_threshold=BALANCED_THRESHOLD,
calc_seg_baf_option=CALC_SEG_BAF_OPTION,
skip_preprocessing=SKIP_PREPROCESSING,
skip_phasing=SKIP_PHASING,
snp6_reference_info_file=SNP6_REF_INFO_FILE,
apt.probeset.genotype.exe=APT_PROBESET_GENOTYPE_EXE,
apt.probeset.summarize.exe=APT_PROBESET_SUMMARIZE_EXE,
norm.geno.clust.exe=NORM_GENO_CLUST_EXE,
birdseed_report_file=BIRDSEED_REPORT_FILE,
heterozygousFilter=HETEROZYGOUSFILTER)
# # Change to work directory and load the chromosome information
# setwd(RUN_DIR)
# chrom_names = get.chrom.names(IMPUTEINFOFILE, TRUE)
#
# # Setup for parallel computing
# clp = makeCluster(NTHREADS)
# registerDoParallel(clp)
#
# # Extract the LogR and BAF from both tumour and normal cel files.
# cel2baf.logr(normal_cel_file=NORMALCEL,
# tumour_cel_file=TUMOURCEL,
# output_file=paste(TUMOURNAME, "_lrr_baf.txt", sep=""),
# snp6_reference_info_file=SNP6_REF_INFO_FILE,
# apt.probeset.genotype.exe=APT_PROBESET_GENOTYPE_EXE,
# apt.probeset.summarize.exe=APT_PROBESET_SUMMARIZE_EXE,
# norm.geno.clust.exe=NORM_GENO_CLUST_EXE)
#
# gc.correct(samplename=TUMOURNAME,
# infile.logr.baf=paste(TUMOURNAME, "_lrr_baf.txt", sep=""),
# outfile.tumor.LogR=paste(TUMOURNAME, "_mutantLogR.tab", sep=""),
# outfile.tumor.BAF=paste(TUMOURNAME, "_mutantBAF.tab", sep=""),
# outfile.normal.LogR=paste(TUMOURNAME, "_germlineLogR.tab", sep=""),
# outfile.normal.BAF=paste(TUMOURNAME, "_germlineBAF.tab", sep=""),
# outfile.probeBAF=paste(TUMOURNAME, "_probeBAF.txt", sep=""),
# snp6_reference_info_file=SNP6_REF_INFO_FILE,
# birdseed_report_file=BIRDSEED_REPORT_FILE,
# chr_names=chrom_names)
#
# # Infer what the gender is
# gender = infer_gender_birdseed(BIRDSEED_REPORT_FILE)
# is_male = gender == "male"
# chrom_names = get.chrom.names(IMPUTEINFOFILE, is_male)
#
# foreach(chrom=1:length(chrom_names), .export=c("generate.impute.input.snp6","run.impute","combine.impute.output","GetChromosomeBAFs_SNP6","plot.haplotype.data")) %dopar% {
#
# # Transform input into a format that Impute2 takes
# generate.impute.input.snp6(infile.germlineBAF=paste(TUMOURNAME, "_germlineBAF.tab", sep=""),
# infile.tumourBAF=paste(TUMOURNAME, "_mutantBAF.tab", sep=""),
# outFileStart=paste(TUMOURNAME, "_impute_input_chr", sep=""),
# chrom=chrom,
# chr_names=chrom_names,
# problemLociFile=PROBLEMLOCI,
# snp6_reference_info_file=SNP6_REF_INFO_FILE,
# imputeinfofile=IMPUTEINFOFILE,
# is.male=is_male,
# heterozygousFilter=HETEROZYGOUSFILTER)
#
# # Run impute on the files
# run.impute(inputfile=paste(TUMOURNAME, "_impute_input_chr", chrom, ".txt", sep=""),
# outputfile.prefix=paste(TUMOURNAME, "_impute_output_chr", chrom, ".txt", sep=""),
# is.male=is_male,
# imputeinfofile=IMPUTEINFOFILE,
# chrom=chrom,
# impute.exe=IMPUTE_EXE)
#
# # As impute runs in windows across a chromosome we need to assemble the output
# combine.impute.output(inputfile.prefix=paste(TUMOURNAME, "_impute_output_chr", chrom, ".txt", sep=""),
# outputfile=paste(TUMOURNAME, "_impute_output_chr", chrom, "_allHaplotypeInfo.txt", sep=""),
# is.male=is_male,
# imputeinfofile=IMPUTEINFOFILE,
# region.size=5000000,
# chrom=chrom)
#
# # Transform the impute output into haplotyped BAFs
# GetChromosomeBAFs_SNP6(chrom=chrom,
# alleleFreqFile=paste(TUMOURNAME, "_impute_input_chr", chrom, "_withAlleleFreq.csv", sep=""),
# haplotypeFile=paste(TUMOURNAME, "_impute_output_chr", chrom, "_allHaplotypeInfo.txt", sep=""),
# samplename=TUMOURNAME,
# outputfile=paste(TUMOURNAME, "_chr", chrom, "_heterozygousMutBAFs_haplotyped.txt", sep=""),
# chr_names=chrom_names)
#
# # Plot what we have until this point
# plot.haplotype.data(haplotyped.baf.file=paste(TUMOURNAME, "_chr", chrom, "_heterozygousMutBAFs_haplotyped.txt", sep=""),
# imageFileName=paste(TUMOURNAME,"_chr",chrom_names[chrom],"_heterozygousData.png",sep=""),
# samplename=TUMOURNAME,
# chrom=chrom,
# chr_names=chrom_names)
#
# # Cleanup temp Impute output
# unlink(paste(TUMOURNAME, "_impute_output_chr", chrom, ".txt_*K.txt*", sep=""))
# }
#
# # Kill the threads as from here its all single core
# stopCluster(clp)
#
# # Combine all the BAF output into a single file
# combine.baf.files(inputfile.prefix=paste(TUMOURNAME, "_chr", sep=""),
# inputfile.postfix="_heterozygousMutBAFs_haplotyped.txt",
# outputfile=paste(TUMOURNAME, "_heterozygousMutBAFs_haplotyped.txt", sep=""),
# no.chrs=length(chrom_names))
#
# # Segment the phased and haplotyped BAF data
# # Call segment.baf.phased.sv when SVs are available - not relevant for SNP6 data
# segment.baf.phased(samplename=TUMOURNAME,
# inputfile=paste(TUMOURNAME, "_heterozygousMutBAFs_haplotyped.txt", sep=""),
# outputfile=paste(TUMOURNAME, ".BAFsegmented.txt", sep=""),
# gamma=SEGMENTATION_GAMMA,
# phasegamma=PHASING_GAMMA,
# kmin=3,
# phasekmin=1,
# calc_seg_baf_option=CALC_SEG_BAF_OPTION)
#
# # Fit a clonal copy number profile
# fit.copy.number(samplename=TUMOURNAME,
# outputfile.prefix=paste(TUMOURNAME, "_", sep=""),
# inputfile.baf.segmented=paste(TUMOURNAME, ".BAFsegmented.txt", sep=""),
# inputfile.baf=paste(TUMOURNAME,"_mutantBAF.tab", sep=""),
# inputfile.logr=paste(TUMOURNAME,"_mutantLogR.tab", sep=""),
# dist_choice=CLONALITY_DIST_METRIC,
# ascat_dist_choice=ASCAT_DIST_METRIC,
# min.ploidy=MIN_PLOIDY,
# max.ploidy=MAX_PLOIDY,
# min.rho=MIN_RHO,
# max.rho=MAX_RHO,
# min.goodness=MIN_GOODNESS_OF_FIT,
# uninformative_BAF_threshold=BALANCED_THRESHOLD,
# gamma_param=PLATFORM_GAMMA,
# use_preset_rho_psi=F,
# preset_rho=NA,
# preset_psi=NA,
# read_depth=30)
#
# # Go over all segments, determine which segements are a mixture of two states and fit a second CN state
# callSubclones(sample.name=TUMOURNAME,
# baf.segmented.file=paste(TUMOURNAME, ".BAFsegmented.txt", sep=""),
# logr.file=paste(TUMOURNAME,"_mutantLogR.tab", sep=""),
# rho.psi.file=paste(TUMOURNAME, "_rho_and_psi.txt",sep=""),
# output.file=paste(TUMOURNAME,"_subclones.txt", sep=""),
# output.figures.prefix=paste(TUMOURNAME,"_subclones_chr", sep=""),
# output.gw.figures.prefix=paste(TUMOURNAME,"_BattenbergProfile", sep=""),
# masking_output_file=paste(TUMOURNAME,"_segment_masking_details.txt", sep=""),
# chr_names=chrom_names,
# gamma=PLATFORM_GAMMA,
# segmentation.gamma=NA,
# siglevel=0.05,
# maxdist=0.01,
# noperms=1000,
# max_allowed_state=250,
# sv_breakpoints_file="NA",
# calc_seg_baf_option=CALC_SEG_BAF_OPTION)
#
# # Make some post-hoc plots
# make_posthoc_plots(samplename=TUMOURNAME,
# logr_file=paste(TUMOURNAME, "_mutantLogR.tab", sep=""),
# subclones_file=paste(TUMOURNAME, "_subclones.txt", sep=""),
# rho_psi_file=paste(TUMOURNAME, "_rho_and_psi.txt", sep=""),
# bafsegmented_file=paste(TUMOURNAME, ".BAFsegmented.txt", sep=""),
# logrsegmented_file=paste(TUMOURNAME, ".logRsegmented.txt", sep=""),
# allelecounts_file=NULL)
#
# # Save refit suggestions for a future rerun
# cnfit_to_refit_suggestions(samplename=TUMOURNAME,
# subclones_file=paste(TUMOURNAME, "_subclones.txt", sep=""),
# rho_psi_file=paste(TUMOURNAME, "_rho_and_psi.txt", sep=""),
# gamma_param=PLATFORM_GAMMA)
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