diff --git a/demo/Snakefile b/demo/Snakefile index 9b53e31f..40f0c193 100755 --- a/demo/Snakefile +++ b/demo/Snakefile @@ -85,4 +85,5 @@ rule all: rules._bwa_mem_all.input, rules._liftover_all.input, rules._controlfreec_all.input, - rules._ichorcna_all.input \ No newline at end of file + rules._ichorcna_all.input + \ No newline at end of file diff --git a/demo/config.yaml b/demo/config.yaml index f552ede2..4f5fb748 100755 --- a/demo/config.yaml +++ b/demo/config.yaml @@ -81,4 +81,5 @@ lcr-modules: ichorcna: inputs: sample_bam: "data/{sample_id}.bam" - sample_bai: "data/{sample_id}.bam.bai" \ No newline at end of file + sample_bai: "data/{sample_id}.bam.bai" + \ No newline at end of file diff --git a/modules/ichorcna/1.0/config/default.yaml b/modules/ichorcna/1.0/config/default.yaml index 9cadd5c5..5be9c5b7 100644 --- a/modules/ichorcna/1.0/config/default.yaml +++ b/modules/ichorcna/1.0/config/default.yaml @@ -12,43 +12,42 @@ lcr-modules: options: readcounter: - readCounterScript: "{MODSDIR}/src/readCounter" chrs: hg19: "1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y" grch37: "1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y" hs37d5: "1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y" hg38: "chr1,chr2,chr3,chr4,chr5,chr6,chr7,chr8,chr9,chr10,chr11,chr12,chr13,chr14,chr15,chr16,chr17,chr18,chr19,chr20,chr21,chr22,chrX,chrY" grch38: "chr1,chr2,chr3,chr4,chr5,chr6,chr7,chr8,chr9,chr10,chr11,chr12,chr13,chr14,chr15,chr16,chr17,chr18,chr19,chr20,chr21,chr22,chrX,chrY" - qual: 20 + qual: 20 # only includes reads with mapping quality greater than 20 binSize: 1000000 # set window size to compute coverage # available binSizes are: 1000000, 500000, 50000, 10000 run: - ichorCNA_libdir: "{MODSDIR}/src/" + ichorCNA_libdir: "" ichorCNA_rscript: "{MODSDIR}/src/runIchorCNA.R" # use panel matching same bin size (optional) ichorCNA_normalPanel: - "1000000": "{MODSDIR}/src/inst/extdata/HD_ULP_PoN_{genome_build}_1Mb_median_normAutosome_median.rds" - "500000": "{MODSDIR}/src/inst/extdata/HD_ULP_PoN_{genome_build}_500kb_median_normAutosome_median.rds" + "1000000": "inst/extdata/HD_ULP_PoN_{genome_build}_1Mb_median_normAutosome_median.rds" + "500000": "inst/extdata/HD_ULP_PoN_{genome_build}_500kb_median_normAutosome_median.rds" # must use gc wig file corresponding to same binSize (required) ichorCNA_gcWig: - "1000000": "{MODSDIR}/src/inst/extdata/gc_{genome_build}_1000kb.wig" - "500000": "{MODSDIR}/src/inst/extdata/gc_{genome_build}_500kb.wig" - "50000": "{MODSDIR}/src/inst/extdata/gc_{genome_build}_50kb.wig" - "10000": "{MODSDIR}/src/inst/extdata/gc_{genome_build}_10kb.wig" + "1000000": "inst/extdata/gc_{genome_build}_1000kb.wig" + "500000": "inst/extdata/gc_{genome_build}_500kb.wig" + "50000": "inst/extdata/gc_{genome_build}_50kb.wig" + "10000": "inst/extdata/gc_{genome_build}_10kb.wig" # must use map wig file corresponding to same binSize (required) ichorCNA_mapWig: - "1000000": "{MODSDIR}/src/inst/extdata/map_{genome_build}_1000kb.wig" - "500000": "{MODSDIR}/src/inst/extdata/map_{genome_build}_500kb.wig" - "50000": "{MODSDIR}/src/inst/extdata/map_{genome_build}_50kb.wig" - "10000": "{MODSDIR}/src/inst/extdata/map_{genome_build}_10kb.wig" + "1000000": "inst/extdata/map_{genome_build}_1000kb.wig" + "500000": "inst/extdata/map_{genome_build}_500kb.wig" + "50000": "inst/extdata/map_{genome_build}_50kb.wig" + "10000": "inst/extdata/map_{genome_build}_10kb.wig" # use bed file if sample has targeted regions, eg. exome data (optional) ichorCNA_exons: NULL ichorCNA_centromere: - grch37: "{MODSDIR}/src/inst/extdata/GRCh37.p13_centromere_UCSC-gapTable.txt" - hg19: "{MODSDIR}/src/inst/extdata/GRCh37.p13_centromere_UCSC-gapTable.txt" - hs37d5: "{MODSDIR}/src/inst/extdata/GRCh37.p13_centromere_UCSC-gapTable.txt" - grch38: "{MODSDIR}/src/inst/extdata/GRCh38.GCA_000001405.2_centromere_acen.txt" - hg38: "{MODSDIR}/src/inst/extdata/GRCh38.GCA_000001405.2_centromere_acen.txt" + grch37: "inst/extdata/GRCh37.p13_centromere_UCSC-gapTable.txt" + hg19: "inst/extdata/GRCh37.p13_centromere_UCSC-gapTable.txt" + hs37d5: "inst/extdata/GRCh37.p13_centromere_UCSC-gapTable.txt" + grch38: "inst/extdata/GRCh38.GCA_000001405.2_centromere_acen.txt" + hg38: "inst/extdata/GRCh38.GCA_000001405.2_centromere_acen.txt" ichorCNA_minMapScore: 0.75 ichorCNA_chrs: grch37: "c('1', '2', '3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','20','21','22','X')" @@ -56,7 +55,7 @@ lcr-modules: hs37d5: "c('1', '2', '3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','20','21','22','X')" grch38: "c('chr1', 'chr2', 'chr3','chr4','chr5','chr6','chr7','chr8','chr9','chr10','chr11','chr12','chr13','chr14','chr15','chr16','chr17','chr18','chr19','chr20','chr21','chr22','chrX')" hg38: "c('chr1', 'chr2', 'chr3','chr4','chr5','chr6','chr7','chr8','chr9','chr10','chr11','chr12','chr13','chr14','chr15','chr16','chr17','chr18','chr19','chr20','chr21','chr22','chrX')" - ichorCNA_fracReadsInChrYForMale: 0.002 + ichorCNA_fracReadsInChrYForMale: 0.002 # Threshold for fraction of reads in chrY to assign as male ichorCNA_genomeStyle: # can set this to UCSC or NCBI grch37: "NCBI" hg19: "NCBI" @@ -73,7 +72,7 @@ lcr-modules: # non-tumor fraction parameter restart values; higher values should be included for cfDNA ichorCNA_normal: "c(0.5,0.6,0.7,0.8,0.9,0.95)" # ploidy parameter restart values - ichorCNA_ploidy: "c(2,3)" + ichorCNA_ploidy: "c(2,3,4)" ichorCNA_estimateNormal: TRUE ichorCNA_estimatePloidy: TRUE ichorCNA_estimateClonality: TRUE @@ -89,7 +88,7 @@ lcr-modules: ichorCNA_maxFracCNASubclone: 0.7 # control segmentation - higher (e.g. 0.9999999) leads to higher specificity and fewer segments # lower (e.g. 0.99) leads to higher sensitivity and more segments - ichorCNA_txnE: 0.9999 + ichorCNA_txnE: 0.9399999 # control segmentation - higher (e.g. 10000000) leads to higher specificity and fewer segments # lower (e.g. 100) leads to higher sensitivity and more segments ichorCNA_txnStrength: 10000 @@ -99,7 +98,7 @@ lcr-modules: conda_envs: ichorcna: "{MODSDIR}/envs/ichorcna.env.yaml" - + hmmcopy_utils: "{MODSDIR}/envs/hmmcopy_utils.env.yaml" threads: readcounter: 4 diff --git a/modules/ichorcna/1.0/envs/ichorcna.env.yaml b/modules/ichorcna/1.0/envs/ichorcna.env.yaml index 241a3870..208fd501 100644 --- a/modules/ichorcna/1.0/envs/ichorcna.env.yaml +++ b/modules/ichorcna/1.0/envs/ichorcna.env.yaml @@ -1,6 +1,7 @@ name: null channels: - conda-forge + - dranew - bioconda - defaults - r @@ -37,6 +38,7 @@ dependencies: - gxx_impl_linux-64=9.3.0 - gxx_linux-64=9.3.0 - harfbuzz=2.8.0 + - hmmcopy_utils=0.0.1 - icu=68.1 - jpeg=9d - kernel-headers_linux-64=2.6.32 diff --git a/modules/ichorcna/1.0/ichorcna.smk b/modules/ichorcna/1.0/ichorcna.smk index 9f4acd9d..46115522 100644 --- a/modules/ichorcna/1.0/ichorcna.smk +++ b/modules/ichorcna/1.0/ichorcna.smk @@ -22,12 +22,27 @@ import oncopipe as op import glob import os -### Directories ### +# Check that the oncopipe dependency is up-to-date. Add all the following lines to any module that uses new features in oncopipe +min_oncopipe_version="1.0.11" +import pkg_resources +try: + from packaging import version +except ModuleNotFoundError: + sys.exit("The packaging module dependency is missing. Please install it ('pip install packaging') and ensure you are using the most up-to-date oncopipe version") + +# To avoid this we need to add the "packaging" module as a dependency for LCR-modules or oncopipe -resultsDir = "results/" -logDir = "log/" +current_version = pkg_resources.get_distribution("oncopipe").version +if version.parse(current_version) < version.parse(min_oncopipe_version): + logger.warning( + '\x1b[0;31;40m' + f'ERROR: oncopipe version installed: {current_version}' + "\n" f"ERROR: This module requires oncopipe version >= {min_oncopipe_version}. Please update oncopipe in your environment" + '\x1b[0m' + ) + sys.exit("Instructions for updating to the current version of oncopipe are available at https://lcr-modules.readthedocs.io/en/latest/ (use option 2)") +# End of dependency checking section +### Directories ### # Setup module and store module-specific configuration in `CFG`. CFG = op.setup_module( name = "ichorcna", @@ -44,8 +59,117 @@ localrules: ##### RULES ##### # ---------------------------------------------------------------------------- # -CFG["runs"]["binSize"] = str(CFG["options"]["readcounter"]["binSize"]) +### Set-up dependencies and packages ### +# Download github and all external files for ichorCNA: (needed since their extdata is not complete for all genome builds) +rule _install_ichorcna: + output: + complete = CFG["dirs"]["inputs"] + "ichorcna_dependencies_installed.success" + params: + outdir = CFG["dirs"]["inputs"] + "ichorCNA/" + conda: + CFG["conda_envs"]["ichorcna"] + shell: + op.as_one_line(""" + git clone git@github.com:broadinstitute/ichorCNA.git {params.outdir} && + touch {output.complete}""") + +# This defines the script/extdata directory used by ichorCNA in the subsequent rules: +ichorDir = CFG["dirs"]["inputs"] + "ichorCNA/inst/extdata/" + +# Symlinks the extdata appropriately +rule _setup_ichorcna_extdata: + input: + complete = CFG["dirs"]["inputs"] + "ichorcna_dependencies_installed.success" + params: + hg19_1Mb_rds = ichorDir + "HD_ULP_PoN_1Mb_median_normAutosome_mapScoreFiltered_median.rds", + hg19_500kb_rds = ichorDir + "HD_ULP_PoN_500kb_median_normAutosome_mapScoreFiltered_median.rds", + hg38_1Mb_rds = ichorDir + "HD_ULP_PoN_hg38_1Mb_median_normAutosome_median.rds", + hg38_500kb_rds = ichorDir + "HD_ULP_PoN_hg38_500kb_median_normAutosome_median.rds", + hg19_1000kb_gc = ichorDir + "gc_hg19_1000kb.wig", + hg19_500kb_gc = ichorDir + "gc_hg19_500kb.wig", + hg19_50kb_gc = ichorDir + "gc_hg19_50kb.wig", + hg19_10kb_gc = ichorDir + "gc_hg19_10kb.wig", + hg38_1000kb_gc = ichorDir + "gc_hg38_1000kb.wig", + hg38_500kb_gc = ichorDir + "gc_hg38_500kb.wig", + hg38_50kb_gc = ichorDir + "gc_hg38_50kb.wig", + hg38_10kb_gc = ichorDir + "gc_hg38_10kb.wig", + hg19_1000kb_map = ichorDir + "map_hg19_1000kb.wig", + hg19_500kb_map = ichorDir + "map_hg19_500kb.wig", + hg19_50kb_map = ichorDir + "map_hg19_50kb.wig", + hg19_10kb_map = ichorDir + "map_hg19_10kb.wig", + hg38_1000kb_map = ichorDir + "map_hg38_1000kb.wig", + hg38_500kb_map = ichorDir + "map_hg38_500kb.wig", + hg38_50kb_map = ichorDir + "map_hg38_50kb.wig", + hg38_10kb_map = ichorDir + "map_hg38_10kb.wig", + output: + hg19_1Mb_rds = ichorDir + "HD_ULP_PoN_hg19_1Mb_median_normAutosome_median.rds", + hg19_500kb_rds = ichorDir + "HD_ULP_PoN_hg19_500kb_median_normAutosome_median.rds", + grch37_1Mb_rds = ichorDir + "HD_ULP_PoN_grch37_1Mb_median_normAutosome_median.rds", + grch37_500kb_rds = ichorDir + "HD_ULP_PoN_grch37_500kb_normAutosome_median.rds", + hs37d5_1Mb_rds = ichorDir + "HD_ULP_PoN_hs37d5_1Mb_median_normAutosome_median.rds", + hs37d5_500kb_rds = ichorDir + "HD_ULP_PoN_hs37d5_500kb_normAutosome_median.rds", + grch38_1Mb_rds = ichorDir + "HD_ULP_PoN_grch38_1Mb_median_normAutosome_median.rds", + grch38_500kb_rds = ichorDir + "HD_ULP_PoN_grch38_500kb_median_normAutosome_median.rds", + grch37_1000kb_gc = ichorDir + "gc_grch37_1000kb.wig", + grch37_500kb_gc = ichorDir + "gc_grch37_500kb.wig", + grch37_50kb_gc = ichorDir + "gc_grch37_50kb.wig", + grch37_10kb_gc = ichorDir + "gc_grch37_10kb.wig", + hs37d5_1000kb_gc = ichorDir + "gc_hs37d5_1000kb.wig", + hs37d5_500kb_gc = ichorDir + "gc_hs37d5_500kb.wig", + hs37d5_50kb_gc = ichorDir + "gc_hs37d5_50kb.wig", + hs37d5_10kb_gc = ichorDir + "gc_hs37d5_10kb.wig", + grch38_1000kb_gc = ichorDir + "gc_grch38_1000kb.wig", + grch38_500kb_gc = ichorDir + "gc_grch38_500kb.wig", + grch38_50kb_gc = ichorDir + "gc_grch38_50kb.wig", + grch38_10kb_gc = ichorDir + "gc_grch38_10kb.wig", + grch37_1000kb_map = ichorDir + "map_grch37_1000kb.wig", + grch37_500kb_map = ichorDir + "map_grch37_500kb.wig", + grch37_50kb_map = ichorDir + "map_grch37_50kb.wig", + grch37_10kb_map = ichorDir + "map_grch37_10kb.wig", + hs37d5_1000kb_map = ichorDir + "map_hs37d5_1000kb.wig", + hs37d5_500kb_map = ichorDir + "map_hs37d5_500kb.wig", + hs37d5_50kb_map = ichorDir + "map_hs37d5_50kb.wig", + hs37d5_10kb_map = ichorDir + "map_hs37d5_10kb.wig", + grch38_1000kb_map = ichorDir + "map_grch38_1000kb.wig", + grch38_500kb_map = ichorDir + "map_grch38_500kb.wig", + grch38_50kb_map = ichorDir + "map_grch38_50kb.wig", + grch38_10kb_map = ichorDir + "map_grch38_10kb.wig", + complete = touch(ichorDir + "symlink.done") + run: + op.relative_symlink(params.hg19_1Mb_rds, output.hg19_1Mb_rds) + op.relative_symlink(params.hg19_500kb_rds, output.hg19_500kb_rds) + op.relative_symlink(params.hg19_1Mb_rds, output.grch37_1Mb_rds) + op.relative_symlink(params.hg19_1Mb_rds, output.hs37d5_1Mb_rds) + op.relative_symlink(params.hg19_500kb_rds, output.grch37_500kb_rds) + op.relative_symlink(params.hg19_500kb_rds, output.hs37d5_500kb_rds) + op.relative_symlink(params.hg38_1Mb_rds, output.grch38_1Mb_rds) + op.relative_symlink(params.hg38_500kb_rds, output.grch38_500kb_rds) + op.relative_symlink(params.hg19_1000kb_gc, output.grch37_1000kb_gc) + op.relative_symlink(params.hg19_500kb_gc, output.grch37_500kb_gc) + op.relative_symlink(params.hg19_50kb_gc, output.grch37_50kb_gc) + op.relative_symlink(params.hg19_10kb_gc, output.grch37_10kb_gc) + op.relative_symlink(params.hg19_1000kb_gc, output.hs37d5_1000kb_gc) + op.relative_symlink(params.hg19_500kb_gc, output.hs37d5_500kb_gc) + op.relative_symlink(params.hg19_50kb_gc, output.hs37d5_50kb_gc) + op.relative_symlink(params.hg19_10kb_gc, output.hs37d5_10kb_gc) + op.relative_symlink(params.hg38_1000kb_gc, output.grch38_1000kb_gc) + op.relative_symlink(params.hg38_500kb_gc, output.grch38_500kb_gc) + op.relative_symlink(params.hg38_50kb_gc, output.grch38_50kb_gc) + op.relative_symlink(params.hg38_10kb_gc, output.grch38_10kb_gc) + op.relative_symlink(params.hg19_1000kb_map, output.grch37_1000kb_map) + op.relative_symlink(params.hg19_500kb_map, output.grch37_500kb_map) + op.relative_symlink(params.hg19_50kb_map, output.grch37_50kb_map) + op.relative_symlink(params.hg19_10kb_map, output.grch37_10kb_map) + op.relative_symlink(params.hg19_1000kb_map, output.hs37d5_1000kb_map) + op.relative_symlink(params.hg19_500kb_map, output.hs37d5_500kb_map) + op.relative_symlink(params.hg19_50kb_map, output.hs37d5_50kb_map) + op.relative_symlink(params.hg19_10kb_map, output.hs37d5_10kb_map) + op.relative_symlink(params.hg38_1000kb_map, output.grch38_1000kb_map) + op.relative_symlink(params.hg38_500kb_map, output.grch38_500kb_map) + op.relative_symlink(params.hg38_50kb_map, output.grch38_50kb_map) + op.relative_symlink(params.hg38_10kb_map, output.grch38_10kb_map) +### Run ichorCNA ### # Symlinks the input files into the module results directory (under '00-inputs/') rule _ichorcna_input_bam: input: @@ -54,21 +178,20 @@ rule _ichorcna_input_bam: output: bam = CFG["dirs"]["inputs"] + "bam/{seq_type}--{genome_build}/{sample_id}.bam", bai = CFG["dirs"]["inputs"] + "bam/{seq_type}--{genome_build}/{sample_id}.bam.bai", # specific to readCounter - crai = CFG["dirs"]["inputs"] + "bam/{seq_type}--{genome_build}/{sample_id}.bam.crai" run: - op.relative_symlink(input.bam, output.bam) - op.relative_symlink(input.bai, output.bai) - op.relative_symlink(input.bai, output.crai) + op.absolute_symlink(input.bam, output.bam) + op.absolute_symlink(input.bai, output.bai) rule _ichorcna_read_counter: input: bam = CFG["dirs"]["inputs"] + "bam/{seq_type}--{genome_build}/{sample_id}.bam", - bai = CFG["dirs"]["inputs"] + "bam/{seq_type}--{genome_build}/{sample_id}.bam.bai" + bai = CFG["dirs"]["inputs"] + "bam/{seq_type}--{genome_build}/{sample_id}.bam.bai", + ichorcna_package = CFG["dirs"]["inputs"] + "ichorcna_dependencies_installed.success", + symlink_complete = ichorDir + "symlink.done" output: CFG["dirs"]["readDepth"] + "{seq_type}--{genome_build}/{binSize}/{sample_id}.bin{binSize}.wig" params: - readCounter = CFG["options"]["readcounter"]["readCounterScript"], binSize = CFG["options"]["readcounter"]["binSize"], qual = CFG["options"]["readcounter"]["qual"], chrs = op.switch_on_wildcard("genome_build", CFG["options"]["readcounter"]["chrs"]) @@ -79,13 +202,12 @@ rule _ichorcna_read_counter: log: CFG["logs"]["readDepth"] + "{seq_type}--{genome_build}/{binSize}/{sample_id}.bin{binSize}.log" shell: - "{params.readCounter} {input.bam} -c {params.chrs} -w {params.binSize} -q {params.qual} > {output} 2> {log}" + "readCounter {input.bam} -c {params.chrs} -w {params.binSize} -q {params.qual} > {output} 2> {log}" rule _run_ichorcna: input: tum = CFG["dirs"]["readDepth"] + "{seq_type}--{genome_build}/{binSize}/{tumour_id}.bin{binSize}.wig", - # norm = CFG["dirs"]["readDepth"] + "{seq_type}--{genome_build}/{binSize}/{normal_id}.bin{binSize}.wig" output: corrDepth = CFG["dirs"]["seg"] + "{seq_type}--{genome_build}/{binSize}/{tumour_id}--{normal_id}--{pair_status}/{tumour_id}.correctedDepth.txt", param = CFG["dirs"]["seg"] + "{seq_type}--{genome_build}/{binSize}/{tumour_id}--{normal_id}--{pair_status}/{tumour_id}.params.txt", @@ -93,8 +215,8 @@ rule _run_ichorcna: segTxt = CFG["dirs"]["seg"] + "{seq_type}--{genome_build}/{binSize}/{tumour_id}--{normal_id}--{pair_status}/{tumour_id}.seg.txt", seg = CFG["dirs"]["seg"] + "{seq_type}--{genome_build}/{binSize}/{tumour_id}--{normal_id}--{pair_status}/{tumour_id}.seg", plot = CFG["dirs"]["seg"] + "{seq_type}--{genome_build}/{binSize}/{tumour_id}--{normal_id}--{pair_status}/{tumour_id}/{tumour_id}_genomeWide.pdf", - #rdata = "results/ichorCNA/{sample_id}/{sample_id}.RData" params: + ichorDir = CFG["dirs"]["inputs"] + "ichorCNA/", outDir = CFG["dirs"]["seg"] + "{seq_type}--{genome_build}/{binSize}/{tumour_id}--{normal_id}--{pair_status}/", rscript = CFG["options"]["run"]["ichorCNA_rscript"], name = "{tumour_id}", @@ -123,7 +245,7 @@ rule _run_ichorcna: txnStrength = CFG["options"]["run"]["ichorCNA_txnStrength"], plotFileType = CFG["options"]["run"]["ichorCNA_plotFileType"], plotYlim = CFG["options"]["run"]["ichorCNA_plotYlim"], - libdir = CFG["options"]["run"]["ichorCNA_libdir"] + libdir = CFG["dirs"]["inputs"] + "ichorCNA/" + CFG["options"]["run"]["ichorCNA_libdir"] conda: CFG["conda_envs"]["ichorcna"] threads: CFG["threads"]["run"] resources: @@ -132,7 +254,7 @@ rule _run_ichorcna: stdout = CFG["logs"]["seg"] + "{seq_type}--{genome_build}/{binSize}/{tumour_id}--{normal_id}--{pair_status}.stdout.log", stderr = CFG["logs"]["seg"] + "{seq_type}--{genome_build}/{binSize}/{tumour_id}--{normal_id}--{pair_status}.stderr.log" shell: - "Rscript {params.rscript} --id {params.name} --libdir {params.libdir} --WIG {input.tum} --gcWig {params.gcwig} --mapWig {params.mapwig} --normalPanel {params.normalpanel} --ploidy \"{params.ploidy}\" --normal \"{params.normal}\" --maxCN {params.maxCN} --includeHOMD {params.includeHOMD} --chrs \"{params.chrs}\" --chrTrain \"{params.chrTrain}\" --genomeStyle {params.genomeStyle} --genomeBuild {params.genomeBuild} --estimateNormal {params.estimateNormal} --estimatePloidy {params.estimatePloidy} --estimateScPrevalence {params.estimateClonality} --scStates \"{params.scStates}\" --centromere {params.centromere} --exons.bed {params.exons} --txnE {params.txnE} --txnStrength {params.txnStrength} --minMapScore {params.minMapScore} --fracReadsInChrYForMale {params.fracReadsChrYMale} --maxFracGenomeSubclone {params.maxFracGenomeSubclone} --maxFracCNASubclone {params.maxFracCNASubclone} --plotFileType {params.plotFileType} --plotYLim \"{params.plotYlim}\" --outDir {params.outDir} > {log.stdout} 2> {log.stderr}" + "Rscript {params.rscript} --id {params.name} --libdir {params.libdir} --WIG {input.tum} --gcWig {params.ichorDir}{params.gcwig} --mapWig {params.ichorDir}{params.mapwig} --normalPanel {params.ichorDir}{params.normalpanel} --ploidy \"{params.ploidy}\" --normal \"{params.normal}\" --maxCN {params.maxCN} --includeHOMD {params.includeHOMD} --chrs \"{params.chrs}\" --chrTrain \"{params.chrTrain}\" --genomeStyle {params.genomeStyle} --genomeBuild {params.genomeBuild} --estimateNormal {params.estimateNormal} --estimatePloidy {params.estimatePloidy} --estimateScPrevalence {params.estimateClonality} --scStates \"{params.scStates}\" --centromere {params.ichorDir}{params.centromere} --exons.bed {params.exons} --txnE {params.txnE} --txnStrength {params.txnStrength} --minMapScore {params.minMapScore} --fracReadsInChrYForMale {params.fracReadsChrYMale} --maxFracGenomeSubclone {params.maxFracGenomeSubclone} --maxFracCNASubclone {params.maxFracCNASubclone} --plotFileType {params.plotFileType} --plotYLim \"{params.plotYlim}\" --outDir {params.outDir} > {log.stdout} 2> {log.stderr}" # Symlinks the final output files into the module results directory (under '99-outputs/') rule _ichorcna_output: @@ -151,12 +273,12 @@ rule _ichorcna_output: seg = CFG["dirs"]["outputs"] + "{seq_type}--{genome_build}/seg/{binSize}/{tumour_id}--{normal_id}--{pair_status}.seg", plot = CFG["dirs"]["outputs"] + "{seq_type}--{genome_build}/plot/{binSize}/{tumour_id}--{normal_id}--{pair_status}_genomeWide.pdf" run: - op.relative_symlink(input.corrDepth, output.corrDepth) - op.relative_symlink(input.param, output.param) - op.relative_symlink(input.cna, output.cna) - op.relative_symlink(input.segTxt, output.segTxt) - op.relative_symlink(input.seg, output.seg) - op.relative_symlink(input.plot, output.plot) + op.relative_symlink(input.corrDepth, output.corrDepth, in_module=True) + op.relative_symlink(input.param, output.param, in_module=True) + op.relative_symlink(input.cna, output.cna, in_module=True) + op.relative_symlink(input.segTxt, output.segTxt, in_module=True) + op.relative_symlink(input.seg, output.seg, in_module=True) + op.relative_symlink(input.plot, output.plot, in_module=True) # Generates the target sentinels for each run, which generate the symlinks rule _ichorcna_all: @@ -176,7 +298,7 @@ rule _ichorcna_all: pair_status=CFG["runs"]["pair_status"], tumour_id=CFG["runs"]["tumour_sample_id"], normal_id=CFG["runs"]["normal_sample_id"], - binSize=CFG["runs"]["binSize"]) + binSize=[CFG["options"]["readcounter"]["binSize"]] * len(CFG["runs"]["tumour_sample_id"])) @@ -185,4 +307,4 @@ rule _ichorcna_all: # Perform some clean-up tasks, including storing the module-specific # configuration on disk and deleting the `CFG` variable -op.cleanup_module(CFG) \ No newline at end of file +op.cleanup_module(CFG) diff --git a/modules/ichorcna/1.0/src/R/EM.R b/modules/ichorcna/1.0/src/R/EM.R deleted file mode 100644 index 65739b02..00000000 --- a/modules/ichorcna/1.0/src/R/EM.R +++ /dev/null @@ -1,555 +0,0 @@ -# file: EM.R -# author: Gavin Ha, Ph.D. -# Justin Rhoades -# Dana-Farber Cancer Institute -# Broad Institute -# contact: -# ULP-WGS website: http://www.broadinstitute.org/~gavinha/ULP-WGS/ -# HMMcopy website: http://compbio.bccrc.ca/software/hmmcopy/ and https://www.bioconductor.org/packages/release/bioc/html/HMMcopy.html -# date: Oct 26, 2016 -# description: Hidden Markov model (HMM) to analyze Ultra-low pass whole genome sequencing (ULP-WGS) data. -# This script is the main script to run the HMM. - -runEM <- function(copy, chr, chrTrain, param, maxiter, verbose = TRUE, - estimateNormal = TRUE, estimatePloidy = TRUE, estimatePrecision = TRUE, - estimateTransition = TRUE, estimateInitDist = TRUE, estimateSubclone = TRUE, - likChangeConvergence = 1e-3) { - - if (nrow(copy) != length(chr) || nrow(copy) != length(chrTrain)) { - stop("runEM: Length of inputs do not match for one of: copy, chr, chrTrain") - } - - if (is.null(param$ct) || is.null(param$lambda) || is.null(param$nu) || - is.null(param$kappa)) { - stop("runEM: Parameter missing, ensure all parameters exist as columns in", - "data frame: ct, lambda, nu, kappa") - } - - S <- param$numberSamples - K <- length(param$ct) - Z <- sum(param$ct.sc) #number of subclonal states (# repeated copy number states) - KS <- K ^ S - N <- nrow(copy) - rho <- matrix(0, KS, N) - py <- matrix(0, KS, N) # Local evidence - mus <- array(0, dim = c(KS, S, maxiter)) # State means - lambdas <- array(0, dim = c(K, S, maxiter)) # State Variances - phi <- matrix(NA, length(param$phi_0), maxiter) # Tumour ploidy - n <- matrix(NA, S, maxiter) # Normal contamination - sp <- matrix(NA, S, maxiter) # cellular prevalence (tumor does not contain event) - piG <- matrix(0, KS, maxiter) # Initial state distribution - converged <- FALSE # Flag for convergence - Zcounts <- matrix(0, KS, KS) - loglik <- rep(0, maxiter) - - ptmTotal <- proc.time() # start total timer - - # SET UP - # Set up the chromosome indices and make cell array of chromosome indicies - chrs <- levels(chr) # WARNING, gets resorted here... - chrsI <- vector('list', length(chrs)) - # initialise the chromosome index and the init state distributions - for(i in 1:length(chrs)) { - chrsI[i] <- list(which(chr == chrs[i])) - } - - # INITIALIZATION - if (verbose) { message("runEM: Initialization") } - i <- 1 - piG[, i] <- normalize(param$kappa) - n[, i] <- param$n_0 - sp[, i] <- param$sp_0 - phi[, i] <- param$phi_0 - lambdas[, , i] <- param$lambda #matrix(param$lambda, nrow = K, ncol = S, byrow = TRUE) - lambdasKS <- as.matrix(expand.grid(as.data.frame(lambdas[, , i]))) #as.matrix(expand.grid(rep(list(param$lambda), S))) - mus[, , i] <- as.matrix(get2and3ComponentMixture(param$jointCNstates, param$jointSCstatus, n[, i], sp[, i], phi[, i])) - - # Likelihood # - for (ks in 1:KS) { - probs <- tdistPDF(copy, mus[ks, , i], lambdasKS[ks, ], param$nu) - py[ks, ] <- apply(probs, 1, prod) # multiply across samples for each data point to get joint likelihood. - } - - # initialize transition prior # - A <- normalize(param$A) - A_prior <- A - dirPrior <- param$dirPrior - - loglik[i] <- -Inf - - while(!converged && (i < maxiter)) { - ptm <- proc.time() - #if (verbose) { message(paste("runEM: EM iteration:", i, "Log likelihood:", loglik[i])) } - i <- i + 1 - - ################ E-step #################### - if (verbose) { message("runEM iter", i-1 , ": Expectation") } - Zcounts <- matrix(0, KS, KS) - for (j in 1:length(chrsI)) { - I <- intersect(chrsI[[j]], which(chrTrain)) - if (length(I) > 0){ - output <- .Call("forward_backward", piG[, i - 1], A, py[, I],PACKAGE = "HMMcopy") - rho[, I] <- output$rho - loglik[i] <- loglik[i] + output$loglik - Zcounts <- Zcounts + t(colSums(aperm(output$xi, c(3, 2, 1)))) - } - } - - ## marginalize rho by sample ## - #rhoS <- list() - #for (s in 1:S){ - # rhoS[[s]] <- matrix(0, nrow = K, ncol = N) - # for (k in 1:K){ - # rhoS[[s]][k, ] <- colSums(rho[param$jointCNstates[, s] == k, chrTrain]) - # } - #} - - ################ M-step #################### - if (verbose) { message("runEM iter", i-1 , ": Maximization") } - output <- estimateParamsMap(copy[chrTrain, ], n[, i - 1], sp[, i - 1], phi[, i - 1], - lambdas[, , i - 1], piG[, i - 1], A, param, - rho[, chrTrain], Zcounts, - estimateNormal = estimateNormal, estimatePloidy = estimatePloidy, - estimatePrecision = estimatePrecision, estimateTransition = estimateTransition, - estimateInitDist = estimateInitDist, estimateSubclone = estimateSubclone) - if (verbose == TRUE) { - for (s in 1:S){ - message("Sample", s, " n=", signif(output$n[s], digits = 4), ", sp=", signif(output$sp[s], digits = 4), - ", phi=", signif(output$phi[s], digits = 4), - ", lambda=", paste0(signif(output$lambda[, s], digits=5), collapse = ","), - ", F=", signif(output$F, digits=4)) - } - } - n[, i] <- output$n - sp[, i] <- output$sp - phi[, i] <- output$phi - lambdas[, , i] <- output$lambda - piG[, i] <- output$piG - A <- output$A - estF <- output$F - - # Recalculate the likelihood - lambdasKS <- as.matrix(expand.grid(as.data.frame(lambdas[, , i]))) - mus[, , i] <- as.matrix(get2and3ComponentMixture(param$jointCNstates, param$jointSCstatus, n[, i], sp[, i], phi[, i])) - for (ks in 1:KS) { - probs <- tdistPDF(copy, mus[ks, , i], lambdasKS[ks, ], param$nu) - py[ks, ] <- apply(probs, 1, prod) # multiply across samples for each data point to get joint likelihood. - } - - prior <- priorProbs(n[, i], sp[, i], phi[, i], lambdas[, , i], piG[, i], A, param, - estimateNormal = estimateNormal, estimatePloidy = estimatePloidy, - estimatePrecision = estimatePrecision, estimateTransition = estimateTransition, - estimateInitDist = estimateInitDist, estimateSubclone = estimateSubclone) - - - # check converence - loglik[i] <- loglik[i] + prior$prior - elapsedTime <- proc.time() - ptm - if (verbose) { - message(paste("runEM iter", i-1, " Log likelihood:", loglik[i])) - message("runEM iter", i-1, " Time: ", format(elapsedTime[3] / 60, digits = 2), " min.") - } - if ((abs(loglik[i] - loglik[i - 1]) / abs(loglik[i])) < likChangeConvergence){#} && loglik[i] > loglik[i - 1]) { - converged = 1 - } - if (loglik[i] < loglik[i - 1]){ - #message("LIKELIHOOD DECREASED!!!") - #message("Using previous iteration ", i-2) - i <- i - 1 - converged = 1 - } - } - - if (converged) { - # Perform one last round of E-step to get latest responsibilities - #E-step - if (verbose) { message("runEM iter", i-1 ,": Re-calculating responsibilties from converged parameters.") } - for (j in 1:length(chrsI)) { - I <- chrsI[[j]] - output <- .Call("forward_backward", piG[, i], A, py[, I], PACKAGE = "HMMcopy") - rho[, I] <- output$rho - } - } - - if (verbose) { - totalTime <- proc.time() - ptmTotal - message("runEM: Using optimal parameters from iter", i-1) - message("runEM: Total elapsed time: ", format(totalTime[3] / 60, digits = 2), "min.") - } - - #### Return parameters #### - n <- n[, 1:i, drop=FALSE] - sp <- sp[, 1:i, drop=FALSE] - phi <- phi[, 1:i, drop=FALSE] - mus <- mus[, , 1:i, drop=FALSE] - lambdas <- lambdas[, , 1:i, drop=FALSE] - piG <- piG[, 1:i, drop=FALSE] - loglik = loglik[1:i] - - output <- vector('list', 0); - output$n <- n - output$sp <- sp - output$phi <- phi - output$mus <- mus - output$lambdas <- lambdas - output$pi <- piG - output$A <- A - output$loglik <- loglik - output$rho <- rho - output$param <- param - output$py <- py - output$iter <- i - - return(output) -} - -get2and3ComponentMixture <- function(ct, ct.sc.status, n, sp, phi){ - S <- length(n) - cn <- 2 - mu <- matrix(NA, nrow = nrow(ct), ncol = ncol(ct)) - for (s in 1:S){ - #subclonal 3 component - mu[ct.sc.status[, s] == TRUE, s] <- (((1 - n[s]) * (1 - sp[s]) * ct[ct.sc.status[, s] == TRUE, s]) + - ((1 - n[s]) * sp[s] * cn) + (n[s] * cn)) / ((1 - n[s]) * phi[s] + n[s] * cn) - #clonal 2 component - mu[ct.sc.status[ ,s] == FALSE, s] <- ((1 - n[s]) * ct[ct.sc.status[, s] == FALSE, s] + n[s] * cn) / ((1 - n[s]) * phi[s] + n[s] * cn) - } - return(log(mu)) -} - -get2ComponentMixture <- function(ct, n, phi){ - if (length(phi) > 1 && length(phi) != length(n)){ - stop("get2ComponentMixture: length(n) not equal to length(phi)") - } - S <- length(n) - #if (length(phi) == 1){ - # phi <- rep(phi, length(n)) - #} - cn <- 2 - #mu <- ((1 - n) * ct + n * cn) / ((1 - n) * phi + n * cn) - mu <- NULL - for (s in 1:S){ - mu <- cbind(mu, ((1 - n[s]) * ct[, s] + n[s] * cn) / ((1 - n[s]) * phi[s] + n[s] * cn)) - } - return(log(mu)) -} - - -# Dirichlet probability density function, returns the probability of vector -# x under the Dirichlet distribution with parameter vector alpha -# Author: David Ross http://www.cs.toronto.edu/~dross/code/dirichletpdf.m -dirichletpdf <- function(x, alpha) { - if (any(x < 0)) { - return(0); - } - if (abs(sum(x) - 1) > 1e-3) { - stop("Dirichlet PDF: probabilities do not sum to 1") - return(0); - } - p <- exp(lgamma(sum(alpha)) - sum(lgamma(alpha))) * prod(x ^ (alpha - 1)) - return(p) -} - -dirichletpdflog <- function(x, k) { - c <- lgamma(sum(k, na.rm = TRUE)) - sum(lgamma(k), na.rm = TRUE) #normalizing constant - l <- sum((k - 1) * log(x), na.rm = TRUE) #likelihood - y <- c + l - return(y) -} - -gammapdflog <- function(x, a, b) { #rate and scale parameterization - c <- a * log(b) - lgamma(a) # normalizing constant - l <- (a - 1) * log(x) + (-b * x) #likelihood - y <- c + l - return(y) -} - -betapdflog <- function(x, a, b) { - y = -lbeta(a, b) + (a - 1) * log(x) + (b - 1) * log(1 - x) - return(y) -} - -tdistPDF <- function(x, mu, lambda, nu) { - S <- ncol(x) - if (!is.null(S)){ - p <- NULL - for (s in 1:S){ - tpdf <- (gamma(nu / 2 + 0.5)/gamma(nu / 2)) * ((lambda[s] / (pi * nu)) ^ (0.5)) * - (1 + (lambda[s] * (x[, s] - mu[s]) ^ 2) / nu) ^ (-0.5 * nu - 0.5) - p <- cbind(p, tpdf) - } - }else{ - nu <- rep(nu, length(mu)) - p <- (gamma(nu / 2 + 0.5)/gamma(nu / 2)) * ((lambda / (pi * nu)) ^ (0.5)) * - (1 + (lambda * (x - mu) ^ 2) / nu) ^ (-0.5 * nu - 0.5) - } - p[is.na(p)] <- 1 - return(p) -} - - -estimateParamsMap <- function(D, n_prev, sp_prev, phi_prev, lambda_prev, pi_prev, A_prev, - params, rho, Zcounts, - estimateNormal = TRUE, estimatePloidy = TRUE, - estimatePrecision = TRUE, estimateInitDist = TRUE, - estimateTransition = TRUE, estimateSubclone = TRUE, - verbose = TRUE) { - KS <- nrow(rho) - K <- length(params$ct) - T <- ncol(rho) - S <- length(n_prev) - #dimen <- ncol(D) - intervalNormal <- c(1e-6, 1 - 1e-6) - intervalSubclone <- c(1e-6, 1 - 1e-6) - intervalPhi <- c(.Machine$double.eps, 10) - intervalLambda <- c(1e-5, 1e4) - - # initialize params to be estimated - n_hat <- n_prev - sp_hat <- sp_prev - phi_hat <- phi_prev - lambda_hat <- lambda_prev - pi_hat <- pi_prev - A_hat <- A_prev - - # Update transition matrix A - if (estimateTransition){ - for (k in 1:KS) { - A_hat[k, ] <- Zcounts[k, ] + params$dirPrior[k, ] - A_hat[k, ] <- normalize(A_hat[k, ]) - } - } - # map estimate for pi (initial state dist) - if (estimateInitDist){ - pi_hat <- estimateMixWeightsParamMap(rho, params$kappa) - } - - # map estimate for normal - if (estimateNormal){ - suppressWarnings( - estNorm <- optim(n_prev, fn = completeLikelihoodFun, pType = rep("n", S), n = n_prev, sp = sp_prev, phi = phi_prev, - lambda = lambda_prev, piG = pi_hat, A = A_hat, - params = params, D = D, rho = rho, Zcounts = Zcounts, - estimateNormal = estimateNormal, estimatePloidy = estimatePloidy, - estimatePrecision = estimatePrecision, estimateInitDist = estimateInitDist, - estimateTransition = estimateTransition, estimateSubclone = estimateSubclone, - method = "L-BFGS-B", - lower = intervalNormal[1], upper = intervalNormal[2], - control = list(trace = 0, fnscale = -1)) - ) - n_hat <- estNorm$par - } - if (estimateSubclone){ - suppressWarnings( - estSubclone <- optim(sp_prev, fn = completeLikelihoodFun, pType = rep("sp", S), n = n_hat, sp = sp_prev, - phi = phi_prev, lambda = lambda_prev, piG = pi_hat, A = A_hat, - params = params, D = D, rho = rho, Zcounts = Zcounts, - estimateNormal = estimateNormal, estimatePloidy = estimatePloidy, - estimatePrecision = estimatePrecision, estimateInitDist = estimateInitDist, - estimateTransition = estimateTransition, estimateSubclone = estimateSubclone, - method = "L-BFGS-B", - lower = intervalNormal[1], upper = intervalNormal[2], - control = list(trace = 0, fnscale = -1)) - ) - sp_hat <- estSubclone$par - } - if (estimatePloidy){ - suppressWarnings( - estPhi <- optim(phi_prev, fn = completeLikelihoodFun, pType = rep("phi", length(phi_prev)), n = n_hat, sp = sp_hat, - phi = phi_prev, lambda = lambda_prev, piG = pi_hat, A = A_hat, - params = params, D = D, rho = rho, Zcounts = Zcounts, - estimateNormal = estimateNormal, estimatePloidy = estimatePloidy, - estimatePrecision = estimatePrecision, estimateInitDist = estimateInitDist, - estimateTransition = estimateTransition, estimateSubclone = estimateSubclone, - method = "L-BFGS-B", - lower = intervalPhi[1], upper = intervalPhi[2], - control = list(trace = 0, fnscale = -1)) - ) - phi_hat <- estPhi$par - } - if (estimatePrecision){ - suppressWarnings( - estLambda <- optim(c(lambda_prev), fn = completeLikelihoodFun, pType = rep("lambda", K*S), n = n_hat, sp = sp_hat, - phi = phi_hat, lambda = lambda_prev, piG = pi_hat, A = A_hat, - params = params, D = D, rho = rho, Zcounts = Zcounts, - estimateNormal = estimateNormal, estimatePloidy = estimatePloidy, - estimatePrecision = estimatePrecision, estimateInitDist = estimateInitDist, - estimateTransition = estimateTransition, estimateSubclone = estimateSubclone, - method = "L-BFGS-B", lower = intervalLambda[1], - control = list(trace = 0, fnscale = -1)) - ) - lambda_hat <- matrix(estLambda$par, ncol = S, byrow = FALSE) - } - - #} - #} - - #map estimate for phi (ploidy) - # if (estimatePloidy) { - # suppressWarnings( - # estPhi <- optim(phi_prev, fn = phiLikelihoodFun, n = n_prev, lambda = lambda_prev, - # params = params, D = D, rho = rhoS, method = "L-BFGS-B", - # control = list(fnscale = -1, ndeps = 1e-5), lower = intervalPhi[1])#, upper = intervalPhi[2]) - # ) - # phi_hat <- estPhi$par - #} - - # map estimate for lambda (Student's t precision) - # if (estimatePrecision){ - #for (s in 1:S){ - # paramsS <- params - # paramsS$betaLambda <- paramsS$betaLambda[s] - # for (k in 1:K){ - # suppressWarnings( - # estLambda <- optim(lambda_prev[k, s], fn = lambdaLikelihoodFun, n = n_prev[s], phi = phi_prev, - # params = paramsS, D = D[, s], rho = rhoS[[s]][k, , drop = FALSE], k = k, - # control = list(fnscale = -1), method = "L-BFGS-B", - # lower = intervalLambda[1])#, upper = intervalLambda[2]) - - - #lambda_hat <- matrix(estLambda$par, ncol = S, byrow = TRUE) - # } - #} - #} - #lambda_hat <- estimatePrecisionParamMap(lambda_prev, n_prev, phi_prev, params, D, rho) - - - #rm(a, b, c, d, e) - gc(verbose = FALSE, reset = TRUE) - #if (verbose == TRUE) { - # message("n=", format(n_hat, digits = 2), ", phi=", format(phi_hat, digits = 4), ", lambda=", paste0(format(lambda_hat, digits=2), collapse = " ", sep = ",")) - #} - return(list(n = n_hat, sp = sp_hat, phi = phi_hat, lambda = lambda_hat, piG = pi_hat, A = A_hat, F = estLambda$value)) -} - -# Student's t likelihood function # -stLikelihood <- function(n, sp, phi, lambda, params, D, rho){ - KS <- nrow(rho) - lik <- 0 - # Recalculate the likelihood - lambdaKS <- as.matrix(expand.grid(as.data.frame(lambda))) - mus <- as.matrix(get2and3ComponentMixture(params$jointCNstates, params$jointSCstatus, n, sp, phi)) - for (ks in 1:KS) { - probs <- log(tdistPDF(D, mus[ks, ], lambdaKS[ks, ], params$nu)) - # multiply across samples for each data point to get joint likelihood. - l <- rho[ks, ] %*% rowSums(as.data.frame(probs)) - lik <- lik + as.numeric(l) - } - return(lik) -} - -## length of x will depend on which pararmeters are being estimated -## x values should be same as n, phi, lambda, pi, but may not necessarily -## include all of those variables -completeLikelihoodFun <- function(x, pType, n, sp, phi, lambda, piG, A, params, D, rho, Zcounts, - estimateNormal = TRUE, estimatePloidy = TRUE, - estimatePrecision = TRUE, estimateTransition = FALSE, - estimateInitDist = TRUE, estimateSubclone = TRUE){ - KS <- nrow(rho) - N <- ncol(rho) - S <- params$numberSamples - K <- length(params$ct) - - lambda <- matrix(lambda, ncol = S, byrow = FALSE) - - if (estimatePrecision && sum(pType == "lambda") > 0){ - lambda <- matrix(x[pType == "lambda"], ncol = S, byrow = FALSE) - } - if (estimateNormal && sum(pType == "n") > 0){ - n <- x[pType == "n"] - } - if (estimateSubclone && sum(pType == "sp") > 0){ - sp <- x[pType == "sp"] - } - if (estimatePloidy && sum(pType == "phi") > 0){ - phi <- x[pType == "phi"] - } - - ## prior probabilities ## - prior <- priorProbs(n, sp, phi, lambda, piG, A, params, - estimateNormal = estimateNormal, estimatePloidy = estimatePloidy, - estimatePrecision = estimatePrecision, estimateTransition = estimateTransition, - estimateInitDist = estimateInitDist, estimateSubclone = estimateSubclone) - ## likelihood terms ## - likObs <- stLikelihood(n, sp, phi, lambda, params, D, rho) - likInit <- rho[, 1] %*% log(piG) - likTxn <- 0 - for (ks in 1:KS){ - likTxn <- likTxn + Zcounts[ks, ] %*% log(A[ks, ]) - } - - ## sum together ## - lik <- likObs + likInit + likTxn - prior <- prior$prior - f <- lik + prior - return(f) -} - -priorProbs <- function(n, sp, phi, lambda, piG, A, params, - estimateNormal = TRUE, estimatePloidy = TRUE, - estimatePrecision = TRUE, estimateTransition = TRUE, - estimateInitDist = TRUE, estimateSubclone = TRUE){ - S <- params$numberSamples - K <- length(params$ct) - KS <- K ^ S - ## prior terms ## - priorA <- 0 - if (estimateTransition){ - for (ks in 1:KS){ - priorA <- priorA + dirichletpdflog(A[ks, ], params$dirPrior[ks, ]) - } - } - priorLambda <- 0 - if (estimatePrecision){ - for (s in 1:S){ - for (k in 1:K){ - priorLambda <- priorLambda + - gammapdflog(as.data.frame(lambda)[k, s], params$alphaLambda[k], params$betaLambda[k, s]) - } - } - } - priorLambda <- as.numeric(priorLambda) - priorN <- 0 - if (estimateNormal){ - priorN <- sum(betapdflog(n, params$alphaN, params$betaN)) - } - priorSP <- 0 - if (estimateNormal){ - priorSP <- sum(betapdflog(sp, params$alphaSp, params$betaSp)) - } - priorPhi <- 0 - if (estimatePloidy){ - priorPhi <- sum(gammapdflog(phi, params$alphaPhi, params$betaPhi)) - } - priorPi <- 0 - if (estimateInitDist){ - priorPi <- dirichletpdflog(piG, params$kappa) - } - prior <- priorA + priorLambda + priorN + priorSP + priorPhi + priorPi - return(list(prior = prior, priorA = priorA, priorLambda = priorLambda, - priorN = priorN, priorSP = priorSP, priorPhi = priorPhi, priorPi = priorPi)) -} - -estimatePrecisionParamMap <- function(lambda, n, phi, params, D, rho){ - mu <- get2ComponentMixture(params$ct, n, phi) - mu_0 <- get2ComponentMixture(params$ct, params$n_0, params$phi_0) - yr <- t(matrix(D, length(D), length(mu))) # Vectorize parameter - u <- (1 + params$nu) / (((yr - mu) ^ 2) * lambda + params$nu); # scale parameter - - # Calculate the precision - lambda_hat <- (rowSums(rho, na.rm = TRUE) + params$alphaLambda + 1) / - (rowSums(rho * u * ((yr - mu) ^ 2), na.rm = TRUE) + - (params$eta * (mu - mu_0) ^ 2 + params$betaLambda)) - return(lambda_hat) -} - -estimateMixWeightsParamMap <- function(rho, kappa) { - K <- nrow(rho) - #pi <- (rho[, 1] + kappa - 1) / (sum(rho[, 1]) + sum(kappa) - K) - pi <- (rowSums(rho, na.rm = TRUE) + kappa - 1) / - # (ncol(rho) + sum(kappa) - K) - (sum(rowSums(rho, na.rm = TRUE)) + sum(kappa) - K) - - return(pi) -} - diff --git a/modules/ichorcna/1.0/src/R/output.R b/modules/ichorcna/1.0/src/R/output.R deleted file mode 100644 index 79ac9d58..00000000 --- a/modules/ichorcna/1.0/src/R/output.R +++ /dev/null @@ -1,127 +0,0 @@ -# file: output.R -# author: Gavin Ha, Ph.D. -# Fred Hutchinson Cancer Research Center -# contact: -# author: Justin Rhoades -# Dana-Farber Cancer Institute -# Broad Institute -# https://github.com/broadinstitute/ichorCNA -# date: July 12, 2019 -# description: Hidden Markov model (HMM) to analyze Ultra-low pass whole genome sequencing (ULP-WGS) data. - -################################################## -###### FUNCTION TO GET OUTPUT HMM RESULTS ######## -################################################## -outputHMM <- function(cna, segs, results, patientID = NULL, outDir = "."){ - names <- c("HOMD","HETD","NEUT","GAIN","AMP","HLAMP",paste0(rep("HLAMP", 8), 2:25)) - - S <- results$param$numberSamples - - segout <- NULL - shuffle <- NULL - if (is.null(patientID)){ - patientID <- names(cna)[1] - } - ### PRINT OUT SEGMENTS FOR EACH SAMPLE TO SEPARATE FILES ### - for (s in 1:S){ - id <- names(cna)[s] - bin_size <- as.numeric(cna[[s]][1,"end"]) - as.numeric(cna[[s]][1,"start"]) + 1 - markers <- (segs[[s]]$end - segs[[s]]$start + 1) / bin_size - segTmp <- cbind(sample = as.character(id), segs[[s]][, 1:3], - event = names[segs[[s]]$copy.number + 1], - copy.number = segs[[s]]$copy.number, - bins = markers, median = segs[[s]]$median, - subclone.status=segs[[s]]$subclone.status, - segs[[s]][, 9:ncol(segs[[s]])]) - segout <- rbind(segout, segTmp[, 1:8]) - ### Re-ordering the columns for output ### - shuffleTmp <- segTmp[, c(1, 2, 3, 4, 7, 8, 6, 5, 9:ncol(segTmp))] - colnames(shuffleTmp)[1:9] <- c("ID", "chrom", "start", "end", - "num.mark", "seg.median.logR", "copy.number", "call", "subclone.status") - shuffle <- rbind(shuffle, shuffleTmp) - } - - shu_out = paste(outDir,"/", patientID,".seg.txt",sep="") - seg_out = paste(outDir,"/", patientID,".seg",sep="") - message("Writing segments to ", seg_out) - write.table(shuffle, file = shu_out, quote = FALSE, sep = "\t", row.names = FALSE) - write.table(segout, file = seg_out, quote = FALSE, sep = "\t", row.names = FALSE) - ### PRINT OUT BIN-LEVEL DATA FOR ALL SAMPLES IN SAME FILE ## - cnaout <- cbind(cna[[1]][, -c(1)]) - colnames(cnaout)[4:ncol(cnaout)] <- paste0(names(cna)[1], ".", colnames(cnaout)[4:ncol(cnaout)]) - if (S >= 2) { - for (s in 2:S){ - id <- names(cna)[s] - cnaTmp <- cna[[s]][, -c(1)] - colnames(cnaTmp)[4:ncol(cnaTmp)] <- paste0(id, ".", colnames(cnaTmp)[4:ncol(cnaTmp)]) - cnaout <- merge(cnaout, cnaTmp, by = c("chr", "start", "end")) - } - } - cnaout$chr <- factor(cnaout$chr, levels = unique(cna[[1]]$chr)) - cnaout <- cnaout[order(cnaout[, 1], cnaout[, 2], cnaout[, 3]), ] - cna_out = paste(outDir,"/", patientID,".cna.seg",sep="") - message("Outputting to bin-level results to ", cna_out) - write.table(cnaout, file = cna_out, quote = FALSE, sep = "\t", row.names = FALSE) -} - -outputParametersToFile <- function(hmmResults, file){ - S <- hmmResults$results$param$numberSamples - x <- hmmResults$results - i <- x$iter - fc <- file(file, "w+") - outMat <- as.data.frame(cbind(`Tumor Fraction` = signif(1 - x$n[, i], digits = 4), Ploidy = signif(x$phi[, i], digits = 4))) - outMat <- cbind(Sample = names(hmmResults$cna), outMat) - rownames(outMat) <- names(hmmResults$cna) - write.table(outMat, file = fc, col.names = TRUE, row.names = FALSE, quote = FALSE, sep = "\t") - write.table("\n", file = fc, col.names = FALSE, row.names = FALSE, quote = FALSE, sep = "\t") - for (s in 1:S){ - id <- names(hmmResults$cna)[s] - write.table(id, file = fc, col.names = FALSE, row.names = FALSE, quote = FALSE, sep = "\t") - write.table(paste0("Gender:\t", x$gender), file = fc, col.names = FALSE, - row.names = FALSE, quote = FALSE, sep = "\t") - write.table(paste0("Tumor Fraction:\t", signif(1 - x$n[s, i], digits = 4)), file = fc, col.names = FALSE, - row.names = FALSE, quote = FALSE, sep = "\t") - ploidy <- (1 - x$n[i]) * x$phi[i] + x$n[i] * 2 - write.table(paste0("Ploidy:\t", signif(x$phi[s, i], digits = 4)), file = fc, col.names = FALSE, - row.names = FALSE, quote = FALSE, sep = "\t") - subcloneGenomeFrac <- sum(hmmResults.cor$cna[[s]]$subclone.status) / nrow(hmmResults.cor$cna[[s]]) - subcloneCNAFrac <- sum(hmmResults.cor$cna[[s]]$subclone.status) / sum(hmmResults.cor$cna[[s]]$copy.num != 2) - scFrac <- 1 - x$sp[s, i] - if (subcloneGenomeFrac == 0){ - scFrac <- NA - }else{ - scFrac <- signif(scFrac, digits = 4) - } - #if (sum(x$param$ct.sc.status) != 0){ - write.table(paste0("Subclone Fraction:\t", scFrac), file = fc, col.names = FALSE, - row.names = FALSE, quote = FALSE, sep = "\t") - write.table(paste0("Fraction Genome Subclonal:\t", signif(subcloneGenomeFrac, digits = 4)), file = fc, col.names = FALSE, row.names = FALSE, quote = FALSE, sep = "\t") - write.table(paste0("Fraction CNA Subclonal:\t", signif(subcloneCNAFrac, digits = 4)), file = fc, col.names = FALSE, row.names = FALSE, quote = FALSE, sep = "\t") - #} - if (is.null(x$coverage)){ - coverage <- NA - }else{ - coverage <- signif(coverage, digits = 4) - } - write.table(paste0("Coverage:\t", coverage), file = fc, col.names = FALSE, - row.names = FALSE, quote = FALSE, sep = "\t") - - if (!is.null(x$chrYCov)){ - write.table(paste0("ChrY coverage fraction:\t", signif(x$chrYCov[s], digits = 4)), file = fc, col.names = FALSE, - row.names = FALSE, quote = FALSE, sep = "\t") - } - if (!is.null(x$chrXMedian)){ - write.table(paste0("ChrX median log ratio:\t", signif(x$chrXMedian[s], digits = 4)), file = fc, col.names = FALSE, - row.names = FALSE, quote = FALSE, sep = "\t") - } - write.table(paste0("Student's t mean: ", paste0(signif(x$mus[,s,i], digits = 2), collapse = ", ")), - file = fc, col.names = FALSE, row.names = FALSE, quote = FALSE, sep = "\t") - write.table(paste0("Student's t precision: ", paste0(signif(x$lambdas[,s,i], digits = 2), collapse = ", ")), file = fc, col.names = FALSE, row.names = FALSE, quote = FALSE, sep = "\t") - write.table(paste0("Gamma Rate Init:\t", signif(hmmResults.cor$results$param$betaLambda[1], digits=2)), file=fc, col.names=FALSE, row.names=FALSE, quote=FALSE, sep="\t") - write.table(paste0("GC-Map correction MAD:\t", format(mad(diff(2^as.numeric(hmmResults$cna[[s]][,"logR"])), na.rm=T), digits=4)), file = fc, col.names = FALSE, row.names = FALSE, quote = FALSE, sep = "\t") - write.table("\n", file = fc, col.names = FALSE, row.names = FALSE, quote = FALSE, sep = "\t") - } - write.table(x$loglik, file = fc, col.names = TRUE, row.names = FALSE, quote = FALSE, sep = "\t") - close(fc) - invisible() -} diff --git a/modules/ichorcna/1.0/src/R/plotting.R b/modules/ichorcna/1.0/src/R/plotting.R deleted file mode 100644 index 7977919c..00000000 --- a/modules/ichorcna/1.0/src/R/plotting.R +++ /dev/null @@ -1,431 +0,0 @@ -# file: plotting.R -# author: Gavin Ha, Ph.D. -# Fred Hutchinson Cancer Research Center -# contact: -# -# author: Justin Rhoades, Broad Institute -# -# ichorCNA website: https://github.com/broadinstitute/ichorCNA -# date: August 12, 2019 -# description: Hidden Markov model (HMM) to analyze Ultra-low pass whole genome sequencing (ULP-WGS) data. -# This file contains R functions for plotting. - -## plot solutions for all samples -plotSolutions <- function(hmmResults.cor, tumour_copy, logR.column = "logR", call.column = "event", - chrs, outDir, plotSegs = TRUE, - numSamples=1, plotFileType="pdf", plotYLim=c(-2,2), seqinfo = NULL, - estimateScPrevalence=FALSE, maxCN){ - ## for each sample ## - for (s in 1:numSamples){ - iter <- hmmResults.cor$results$iter - id <- names(hmmResults.cor$cna)[s] - ploidyEst <- hmmResults.cor$results$phi[s, iter] - normEst <- hmmResults.cor$results$n[s, iter] - purityEst <- 1 - normEst - ploidyAll <- (1 - normEst) * ploidyEst + normEst * 2 - - outPlotFile <- paste0(outDir, "/", id, "/", id, "_genomeWide") - plotGWSolution(hmmResults.cor, s, outPlotFile, seqinfo, - logR.column = logR.column, call.column = call.column, - plotFileType=plotFileType, plotYLim=plotYLim, - plotSegs=plotSegs, estimateScPrevalence=estimateScPrevalence, main=id) - - ### PLOT THE LOG RATIO DATA ALONG WITH COLOUR-CODING FOR PREDICTED CALLS ### - for (i in chrs){ - ## PLOT CNA BY CHROMOSOME ## - outPlot <- paste0(outDir,"/",id,"/",id,"_CNA_chr",i) - if (plotFileType == "png"){ - outPlot <- paste0(outPlot, ".png") - png(outPlot,width=15,height=5,units="in",res=300) - }else{ - outPlot <- paste0(outPlot, ".pdf") - pdf(outPlot,width=15,height=5) - } - par(mfrow=c(1,1)) - plotCNlogRByChr(hmmResults.cor$cna[[s]], segs=hmmResults.cor$results$segs[[s]], chr=i, - ploidy = ploidyAll, plotSegs=plotSegs, seqinfo = seqinfo, - logR.column = logR.column, call.column = call.column, - cytoBand=T, yrange=plotYLim, cex=0.75, spacing=8) - dev.off() - } - - ### PLOT THE CORRECTION COMPARISONS ### - outPlotFile <- paste0(outDir,"/",id,"/",id,"_correct") - if (plotFileType == "png"){ - outPlotFile <- paste0(outPlotFile, ".png") - png(outPlotFile,width=10,height=12,units="in",res=300) - }else{ - outPlotFile <- paste0(outPlotFile, ".pdf") - pdf(outPlotFile,width=10,height=12) - } - plotCorrectionGenomeWide(tumour_copy[[s]], seqinfo = seqinfo, pch = ".") - dev.off() - - ### PLOT THE BIAS ### - outPlotFile <- paste0(outDir,"/",id,"/",id,"_bias") - if (plotFileType == "png"){ - outPlotFile <- paste0(outPlotFile, ".png") - png(outPlotFile,width=7,height=7,units="in",res=300) - }else{ - outPlotFile <- paste0(outPlotFile, ".pdf") - pdf(outPlotFile,width=7,height=7) - } - try(plotBias(tumour_copy[[s]], pch = 20, cex = 0.5), silent=TRUE) - dev.off() - - ### PLOT TPDF ## - outPlotFile <- paste0(outDir,"/",id,"/",id,"_tpdf.pdf") - pdf(outPlotFile) - plotParam(mus = unique(hmmResults.cor$results$mus[, s, iter]), - lambdas = hmmResults.cor$results$lambdas[, s, iter], - subclone = hmmResults.cor$results$param$ct.sc.status, - nu = hmmResults.cor$results$param$nu, copy.states = 1:maxCN) - dev.off() - - } -} - - -plotGWSolution <- function(hmmResults.cor, s, outPlotFile, plotFileType="pdf", - logR.column = "logR", call.column = "event", - seqinfo = NULL, plotSegs = TRUE, - plotYLim=c(-2,2), estimateScPrevalence, main, - turnDevOn=TRUE, turnDevOff=TRUE){ - ## plot genome wide figures for each solution ## - iter <- hmmResults.cor$results$iter - ploidyEst <- hmmResults.cor$results$phi[s, iter] - normEst <- hmmResults.cor$results$n[s, iter] - purityEst <- 1 - normEst - ploidyAll <- (1 - normEst) * ploidyEst + normEst * 2 - subclone <- 1 - hmmResults.cor$results$sp[s, iter] - #outPlotFile <- paste0(outDir, "/", id, "/", id, "_genomeWide") - if (turnDevOn){ - if (plotFileType == "png"){ - outPlotFile <- paste0(outPlotFile, ".png") - png(outPlotFile,width=20,height=6,units="in",res=300) - }else{ - outPlotFile <- paste0(outPlotFile, ".pdf") - pdf(outPlotFile,width=20,height=6) - } - } - if (plotSegs){ - segsToUse <- hmmResults.cor$results$segs[[s]] - }else{ - segsToUse <- NULL - } - plotCNlogRByChr(hmmResults.cor$cna[[s]], segs = segsToUse, plotSegs=plotSegs, seqinfo=seqinfo, - param = hmmResults.cor$results$param, chr=NULL, - logR.column = logR.column, call.column = call.column, - ploidy = ploidyAll, cytoBand=T, yrange=plotYLim, main=main) #ylim for plot - annotStr <- paste0("Tumor Fraction: ", signif(purityEst, digits=4), ", Ploidy: ", signif(ploidyEst, digits=3)) - if (!is.null(coverage)){ - annotStr <- paste0(annotStr, ", Coverage: ", signif(coverage, digits=2)) - } - mtext(line=-1, annotStr, cex=1.5) - if (estimateScPrevalence){ - sampleBins <- hmmResults.cor$cna[[s]] - #sampleBins <- sampleBins[sampleBins$chr %in% chrTrain, ] - subBinCount <- sum(sampleBins$subclone.status) - fracGenomeSub <- subBinCount / nrow(sampleBins) - fracCNAsub <- subBinCount / sum(sampleBins$copy.number != 2) - if (fracGenomeSub > 0){ - annotSubStr <- paste0("Subclone Fraction: ", signif(subclone, digits=3), - ", Frac. Genome Subclonal: ", format(round(fracGenomeSub, 2), nsmall = 2), - ", Frac. CNA Subclonal: ", format(round(fracCNAsub, 2), nsmall = 2)) - mtext(line=-2, annotSubStr, cex=1.5) - } - } - if (turnDevOff){ - dev.off() - } -} - - -#data is the output format of HMMcopy (*.cna.txt) -#cytoBand = {T, F} -#alphaVal = [0,1] -#geneAnnot is a dataframe with 4 columns: geneSymbol, chr, start, stop -#spacing is the distance between each track -plotCNlogRByChr <- function(dataIn, segs, param = NULL, logR.column = "logR", call.column = "event", plotSegs = TRUE, seqinfo=NULL, chr=NULL, ploidy = NULL, geneAnnot=NULL, yrange=c(-4,6), xlim=NULL, xaxt = "n", cex = 0.5, gene.cex = 0.5, plot.title = NULL, spacing=4, cytoBand=T, alphaVal=1, main){ - #color coding - alphaVal <- ceiling(alphaVal * 255); class(alphaVal) = "hexmode" - alphaSubcloneVal <- ceiling(alphaVal / 2 * 255); class(alphaVal) = "hexmode" - cnCol <- c("#00FF00","#006400","#0000FF","#8B0000",rep("#FF0000", 26)) - subcloneCol <- c("#00FF00") - cnCol <- paste(cnCol,alphaVal,sep="") - names(cnCol) <- c("HOMD","HETD","NEUT","GAIN","AMP","HLAMP",paste0(rep("HLAMP", 8), 2:25)) -# segCol <- cnCol -# ## add in colors for subclone if param provided -# if (!is.null(param)){ -# ind <- ((which.max(param$ct) + 1) : length(param$ct)) + 1 -# cnCol[ind] <- paste0(cnCol[ind], alphaSubcloneVal / 2) -# segCol[ind] <- "#00FF00" -# } - # adjust for ploidy # - if (!is.null(ploidy)){ - dataIn[, logR.column] <- as.numeric(dataIn[, logR.column]) + log2(ploidy / 2) - - if (!is.null(segs)){ - segs[, "median"] <- segs[, "median"] + log2(ploidy / 2) - } - } - - - if (!is.null(chr)){ - for (i in chr){ - - dataByChr <- dataIn[dataIn[,"chr"]==as.character(i),] - - #plot the data - #if (outfile!=""){ pdf(outfile,width=10,height=6) } - par(mar=c(spacing,8,4,2)) - #par(xpd=NA) - coord <- (as.numeric(dataByChr[,"end"]) + as.numeric(dataByChr[,"start"]))/2 - if (is.null(xlim)){ - xlim <- c(1,as.numeric(dataByChr[dim(dataByChr)[1],"start"])) - xaxt <- "n" - } - if (is.null(plot.title)){ - plot.title <- paste("Chromosome ",i,sep="") - } - ## plot logR for bins ## - plot(coord,as.numeric(dataByChr[, logR.column]),col=cnCol[dataByChr[, call.column]], - pch=16, ylim=yrange, - xlim=xlim, xaxt = xaxt, xlab="",ylab="Copy Number (log2 ratio)", - cex.lab=1.5,cex.axis=1.5, cex=cex,las=1) - title(plot.title, line = 1.25, xpd=NA, cex.main=1.5) - ## plot centre line ## - lines(c(1,as.numeric(dataByChr[dim(dataByChr)[1],3])),rep(0,2),type="l",col="grey",lwd=0.75) - if (!is.null(segs) & plotSegs){ - segsByChr <- segs[segs[,"chr"]==as.character(i),,drop=FALSE] - ind <- segsByChr$subclone.status == FALSE - apply(segsByChr[ind, ], 1, function(x){ - lines(x[c("start","end")], rep(x["median"], 2), col = cnCol[x[call.column]], lwd = 3) - invisible() - }) - if (sum(!ind) > 0){ - apply(segsByChr[!ind, ], 1, function(x){ - lines(x[c("start","end")], rep(x["median"], 2), col = subcloneCol, lwd = 3) - invisible() - }) - } - } - - if (!is.null(geneAnnot)){ - #par(xpd=F) - colnames(geneAnnot) <- c("Gene","Chr","Start","Stop") - geneAnnot <- geneAnnot[geneAnnot[,"Chr"]==chr,] - for (g in 1:dim(geneAnnot)[1]){ - print(geneAnnot[g,"Gene"]) - abline(v=as.numeric(geneAnnot[g,"Start"]),col="black",lty=3,xpd=F) - abline(v=as.numeric(geneAnnot[g,"Stop"]),col="black",lty=3,xpd=F) - atP <- (as.numeric(geneAnnot[g,"Stop"]) - as.numeric(geneAnnot[g,"Start"]))/2 + as.numeric(geneAnnot[g,"Start"]) - if (atP < dataByChr[1,"start"]){ atP <- dataByChr[1,"start"] } - else if (atP > dataByChr[dim(dataByChr)[1],"start"]){ atP <- dataByChr[dim(dataByChr)[1],"start"] } - mtext(geneAnnot[g,"Gene"],side=3,line=0,at=atP,cex=gene.cex) - - } - } - } - }else{ #plot for all chromosomes - par(mar=c(spacing,8,2,2)) - #midpt <- (as.numeric(dataIn[,"end"]) + as.numeric(dataIn[,"start"]))/2 - #coord <- getGenomeWidePositions(dataIn[,"chr"],midpt) - coord <- getGenomeWidePositions(dataIn[,"chr"],dataIn[,"end"], seqinfo) - plot(coord$posns,as.numeric(dataIn[, logR.column]), - col=cnCol[as.character(dataIn[,call.column])],pch=16,xaxt="n", ylim=yrange, - xlim=c(1,as.numeric(coord$posns[length(coord$posns)])), - xlab="",ylab="Copy Number (log2 ratio)", - cex.lab=1.5,cex.axis=1.5,cex=0.5,las=1,bty="n", - #main=dataIn[1,"sample"]) - main=main) - #plot segments - - if (plotSegs){ - coordEnd <- getGenomeWidePositions(segs[, "chr"], segs[, "end"], seqinfo) - coordStart <- coordEnd$posns - (segs[, "end"] - segs[, "start"] + 1) - xlim <- as.numeric(c(1, coordEnd$posns[length(coordEnd$posns)])) - #col <- cnCol[as.numeric(segs[, "state"] + 1)] - col <- cnCol[segs[, call.column]] - #write.table(segs, "~/Documents/multisample/segs_debug.seg", quote=F, sep="\t", row.names=F) ## debug - value <- as.numeric(segs[, "median"]) - sc.status <- as.logical(segs[, "subclone.status"]) - mat <- as.data.frame(cbind(coordStart, coordEnd$posns, value, sc.status, col)) - rownames(mat) <- 1:nrow(mat) - ## clonal CN - ind <- mat$sc.status == FALSE - apply(mat[ind, ], 1, function(x){ - lines(x[1:2], rep(x[3], 2), col = x[5], lwd = 3) - invisible() - }) - ## subclonal CN - if (sum(!ind) > 0){ - apply(mat[!ind, ], 1, function(x){ - lines(x[1:2], rep(x[3], 2), col = subcloneCol, lwd = 3) - invisible() - }) - } - } - lines(as.numeric(c(1,coord$posns[length(coord$posns)])),rep(0,2),type="l",col="grey",lwd=2) - if (plotSegs){ - plotChrLines(dataIn[,"chr"],coordEnd$chrBkpt,yrange) - }else{ - plotChrLines(dataIn[,"chr"],coord$chrBkpt,yrange) - } - } -} - - -plotCorrectionGenomeWide <- function(correctOutput, seqinfo = NULL, ...) { - - midpt <- (start(correctOutput) + end(correctOutput))/2 - coord <- getGenomeWidePositions(seqnames(correctOutput),midpt, seqinfo) - - - par(mfrow = c(3, 1)) - #correctOutput <- correctOutput[paste(chr)] - pos <- coord$posns - from <- min(coord$chrBkpt) - to <- max(coord$chrBkpt) - - copy <- correctOutput$reads / median(correctOutput$reads, na.rm = TRUE) - top <- quantile(copy, 0.99) - bot <- quantile(copy, 0.01) - - set <- which(correctOutput$valid & pos >= from & pos <= to & - copy >= bot & copy <= top) - - y <- copy[set] - m <- signif(mad(y, na.rm = TRUE), digits = 3) - r <- c(min(y, na.rm = TRUE), max(y, na.rm = TRUE)) - plot(pos[set], y, ylab = "Estimated Copy", xaxt="n", - main = paste("Uncorrected Readcount, MAD = ", m), ylim = r, ...) - plotChrLines(as.vector(unique(seqnames(correctOutput))),coord$chrBkpt,yrange=r+c(-0.5,0.5)) - m <- signif(mad(correctOutput$cor.gc[set], na.rm = TRUE), digits = 3) - plot(pos[set], correctOutput$cor.gc[set], xaxt="n", - ylab = "Estimated Copy", - main = paste("CG-corrected Readcount, MAD = ", m), ylim = r, ...) - plotChrLines(as.vector(unique(seqnames(correctOutput))),coord$chrBkpt,yrange=r+c(-0.5,0.5)) - m <- signif(mad(correctOutput$cor.map[set], na.rm = TRUE), digits = 3) - plot(pos[set], correctOutput$cor.map[set], xaxt="n", ylab = "Estimated Copy", - main = paste("Mappability and GC-corrected Readcount, MAD = ", m), - ylim = r, ...) - plotChrLines(as.vector(unique(seqnames(correctOutput))),coord$chrBkpt,yrange=r+c(-0.5,0.5)) -} - -################################################## -### HELPER FUNCTION TO GET GENOME-WIDE COORDS #### -################################################## -plotChrLines <- function(chrs,chrBkpt,yrange){ - #plot vertical chromosome lines - for (j in 1:length(chrBkpt)){ - lines(rep(chrBkpt[j],2),yrange,type="l",lty=2,col="black",lwd=0.75) - } - numLines <- length(chrBkpt) - mid <- (chrBkpt[1:(numLines-1)]+chrBkpt[2:numLines])/2 - #chrs <- mapSeqlevels(as.vector(chrs), style = "NCBI") - if (seqlevelsStyle(chrs)[1] != "NCBI"){ - seqlevelsStyle(chrs) <- "NCBI" - } - chrs <- sortSeqlevels(unique(chrs)) - axis(side=1,at=mid,labels=c(chrs),cex.axis=1.5,tick=FALSE) -} - -# getGenomeWidePositions <- function(chrs,posns){ -# #create genome coordinate scaffold -# positions <- as.numeric(posns) -# chrsNum <- unique(chrs) -# chrBkpt <- rep(0,length(chrsNum)+1) -# for (i in 2:length(chrsNum)){ -# chrInd <- which(chrs==chrsNum[i]) -# prevChrPos <- positions[chrInd[1]-1] -# chrBkpt[i] = prevChrPos -# positions[chrInd] = positions[chrInd] + prevChrPos -# } -# chrBkpt[i+1] <- positions[length(positions)] -# return(list(posns=positions,chrBkpt=chrBkpt)) -# } - -getGenomeWidePositions <- function(chrs, posns, seqinfo = NULL) { - # create genome coordinate scaffold - positions <- as.numeric(posns) - chrs <- as.character(chrs) - chrsNum <- unique(chrs) - chrBkpt <- rep(0, length(chrsNum) + 1) - prevChrPos <- 0 - i <- 1 - if (length(chrsNum) > 1){ - for (i in 2:length(chrsNum)) { - chrInd <- which(chrs == chrsNum[i]) - if (!is.null(seqinfo)){ - prevChrPos <- seqlengths(seqinfo)[i-1] + prevChrPos - }else{ - prevChrPos <- positions[chrInd[1] - 1] - } - chrBkpt[i] = prevChrPos - positions[chrInd] = positions[chrInd] + prevChrPos - } - } - chrBkpt[i + 1] <- positions[length(positions)] - return(list(posns = positions, chrBkpt = chrBkpt)) -} - -#stateCols <- function() { -# return(c("#74C476", "#238B45", "#00008B", "#A50F15", "#DE2D26", "#FB6A4A", "#FB6A4A", "#FB6A4A", "#FB6A4A", "#FB6A4A", "#FB6A4A", "#FB6A4A")) -#} - -# plotSegments <- function(correctOutput, segmentOutput, -# chr = space(correctOutput)[1], ...){ -# if (is.null(segmentOutput$segs)) { -# warning("Processed segments now found, automatically processing") -# segmentOutput$segs <- processSegments(segments$segs, -# space(correctOutput), start(correctOutput), end(correctOutput), -# correctOutput$copy) -# } -# -# segs <- segmentOutput$segs -# correctOutput$state <- segmentOutput$state -# cols <- stateCols() -# range <- quantile(correctOutput$copy, na.rm = TRUE, prob = c(0.01, 0.99)) -# -# a <- correctOutput[as.character(chr)] -# b <- segs[segs$chr == chr, ] -# plot(start(a), a$copy, -# col = cols[as.numeric(as.character(a$state))], ylim = range, ...) -# for (k in 1:nrow(b)){ -# lines(c(b$start[k], b$end[k]), rep(b$median[k], 2), lwd = 3, -# col = "green") -# } -# } - -#### TODO: SUBCLONAL STATES ##### -plotParam <- function(mus, lambdas, nu, subclone = NULL, copy.states = 0:6, ...) { - #cols <- stateCols() - cols <- c("#00FF00","#006400","#0000FF","#8B0000",rep("#FF0000", 10)) - cols <- cols[copy.states + 1] - #last <- ncol(mus) - #lastmu <- mus[, last] - #lastlambda <- lambdas[, last] - - domain <- (max(mus) - min(mus)) / 2 - left <- min(mus) - domain - right <- max(mus) + domain - x = seq(left * 10, right * 10, by = 0.01); - height = 0 - - for(state in 1:length(mus)) { - #y = tdistPDF(x, param$mu[state,1], param$lambda[state,1], param$param$nu); - z = tdistPDF(x, mus[state], lambdas[state], nu); - height <- max(height, z) - } - - plot(c(left, right), c(0, height), type = "n", xlab = "Normalized Log2 Ratios", - ylab = "Student-t Density", ...); - - for(state in 1:length(mus)) { - #y = tdistPDF(x, param$mu[state,1], param$lambda[state,1], param$param$nu); - z = tdistPDF(x, mus[state], lambdas[state], nu); - #lines(x, y, col = cols[state], lwd = 2, lty = 3); - lines(x, z, col = cols[state], lwd = 3); - } -} diff --git a/modules/ichorcna/1.0/src/R/segmentation.R b/modules/ichorcna/1.0/src/R/segmentation.R deleted file mode 100755 index 583e9b46..00000000 --- a/modules/ichorcna/1.0/src/R/segmentation.R +++ /dev/null @@ -1,342 +0,0 @@ -# file: segmentation.R -# author: Gavin Ha, Ph.D. -# Justin Rhoades -# Dana-Farber Cancer Institute -# Broad Institute -# contact: -# ULP-WGS website: http://www.broadinstitute.org/~gavinha/ULP-WGS/ -# HMMcopy website: http://compbio.bccrc.ca/software/hmmcopy/ and https://www.bioconductor.org/packages/release/bioc/html/HMMcopy.html -# date: Oct 26, 2016 -# description: Hidden Markov model (HMM) to analyze Ultra-low pass whole genome sequencing (ULP-WGS) data. -# This script is the main script to run the HMM. - -HMMsegment <- function(x, validInd = NULL, dataType = "copy", param = NULL, - chrTrain = c(1:22), maxiter = 50, estimateNormal = TRUE, estimatePloidy = TRUE, - estimatePrecision = TRUE, estimateSubclone = TRUE, estimateTransition = TRUE, - estimateInitDist = TRUE, logTransform = FALSE, verbose = TRUE) { - chr <- as.factor(seqnames(x[[1]])) - # setup columns for multiple samples # - dataMat <- as.matrix(as.data.frame(lapply(x, function(y) { mcols(x[[1]])[, dataType] }))) - - # normalize by median and log data # - if (logTransform){ - dataMat <- apply(dataMat, 2, function(x){ log(x / median(x, na.rm = TRUE)) }) - }else{ - dataMat <- log(2^dataMat) - } - ## update variable x with loge instead of log2 - for (i in 1:length(x)){ - mcols(x[[i]])[, dataType] <- dataMat[, i] - } - if (!is.null(chrTrain)) { - chrInd <- chr %in% chrTrain - }else{ - chrInd <- !logical(length(chr)) - } - if (!is.null(validInd)){ - chrInd <- chrInd & validInd - } - - if (is.null(param)){ - param <- getDefaultParameters(dataMat[chrInd]) - } - #if (param$n_0 == 0){ - # param$n_0 <- .Machine$double.eps - #} - ####### RUN EM ########## - convergedParams <- runEM(dataMat, chr, chrInd, param, maxiter, - verbose, estimateNormal = estimateNormal, estimatePloidy = estimatePloidy, - estimateSubclone = estimateSubclone, estimatePrecision = estimatePrecision, - estimateTransition = estimateTransition, estimateInitDist = estimateInitDist) - # Calculate likelihood using converged params - # S <- param$numberSamples - # K <- length(param$ct) - # KS <- K ^ S - # py <- matrix(0, KS, nrow(dataMat)) - # iter <- convergedParams$iter - # lambdasKS <- as.matrix(expand.grid(as.data.frame(convergedParams$lambda[, , iter]))) - # for (ks in 1:KS) { - # probs <- tdistPDF(dataMat, convergedParams$mus[ks, , iter], lambdasKS[ks, ], param$nu) - # py[ks, ] <- apply(probs, 1, prod) # multiply across samples for each data point to get joint likelihood. - # } - # - viterbiResults <- runViterbi(convergedParams, chr) - - # setup columns for multiple samples # - segs <- segmentData(x, validInd, viterbiResults$states, convergedParams) - #output$segs <- processSegments(output$segs, chr, start(x), end(x), x$DataToUse) - names <- c("HOMD","HETD","NEUT","GAIN","AMP","HLAMP",paste0(rep("HLAMP", 8), 2:25)) - #if (c(0) %in% param$ct){ #if state 0 HOMD is IN params# - #names <- c("HOMD", names) - # shift states to start at 2 (HETD) - #tmp <- lapply(segs, function(x){ x$state <- x$state + 1; x}) - #viterbiResults$states <- as.numeric(viterbiResults$states) + 1 - #} - ### PUTTING TOGETHER THE COLUMNS IN THE OUTPUT ### - cnaList <- list() - S <- length(x) - for (s in 1:S){ - id <- names(x)[s] - copyNumber <- param$jointCNstates[viterbiResults$state, s] - subclone.status <- param$jointSCstatus[viterbiResults$state, s] - cnaList[[id]] <- data.frame(cbind(sample = as.character(id), - chr = as.character(seqnames(x[[s]])), - start = start(x[[s]]), end = end(x[[s]]), - copy.number = copyNumber, - event = names[copyNumber + 1], - logR = round(log2(exp(dataMat[,s])), digits = 4), - subclone.status = as.numeric(subclone.status) - )) - - cnaList[[id]] <- transform(cnaList[[id]], - start = as.integer(as.character(start)), - end = as.integer(as.character(end)), - copy.number = as.numeric(copy.number), - logR = as.numeric(as.character(logR)), - subclone.status = as.numeric(subclone.status)) - - ## order by chromosome ## - chrOrder <- unique(chr) #c(1:22,"X","Y") - cnaList[[id]] <- cnaList[[id]][order(match(cnaList[[id]][, "chr"],chrOrder)),] - ## remove MT chr ## - cnaList[[id]] <- cnaList[[id]][cnaList[[id]][,"chr"] %in% chrOrder, ] - - ## segment mean loge -> log2 - #segs[[s]]$median.logR <- log2(exp(segs[[s]]$median.logR)) - segs[[s]]$median <- log2(exp(segs[[s]]$median)) - ## add subclone status - segs[[s]]$subclone.status <- param$jointSCstatus[segs[[s]]$state, s] - } - convergedParams$segs <- segs - return(list(cna = cnaList, results = convergedParams, viterbiResults = viterbiResults)) -} - -getTransitionMatrix <- function(K, e, strength){ - A <- matrix(0, K, K) - for (j in 1:K) { - A[j, ] <- (1 - e[1]) / (K - 1) - A[j, j] <- e[1] - } - A <- normalize(A) - A_prior <- A - dirPrior <- A * strength[1] - return(list(A=A, dirPrior=dirPrior)) -} - -getDefaultParameters <- function(x, maxCN = 5, ct.sc = NULL, ploidy = 2, e = 0.9999999, e.sameState = 10, strength = 10000000, includeHOMD = FALSE){ - if (includeHOMD){ - ct <- 0:maxCN - }else{ - ct <- 1:maxCN - } - param <- list( - strength = strength, e = e, - ct = c(ct, ct.sc), - ct.sc.status = c(rep(FALSE, length(ct)), rep(TRUE, length(ct.sc))), - phi_0 = 2, alphaPhi = 4, betaPhi = 1.5, - n_0 = 0.5, alphaN = 2, betaN = 2, - sp_0 = 0.5, alphaSp = 2, betaSp = 2, - lambda = as.matrix(rep(100, length(ct)+length(ct.sc)), ncol=1), - nu = 2.1, - kappa = rep(75, length(ct)), - alphaLambda = 5 - ) - K <- length(param$ct) - ## initialize hyperparameters for precision using observed data ## - if (!is.null(dim(x))){ # multiple samples (columns) - param$numberSamples <- ncol(x) - #betaLambdaVal <- ((apply(x, 2, function(x){ sd(diff(x), na.rm=TRUE) }) / sqrt(length(param$ct))) ^ 2) - betaLambdaVal <- ((apply(x, 2, sd, na.rm = TRUE) / sqrt(length(param$ct))) ^ 2) - }else{ # only 1 sample - param$numberSamples <- 1 - betaLambdaVal <- ((sd(x, na.rm = TRUE) / sqrt(length(param$ct))) ^ 2) - } - param$betaLambda <- matrix(betaLambdaVal, ncol = param$numberSamples, nrow = length(param$ct), byrow = TRUE) - param$alphaLambda <- rep(param$alphaLambda, K) - - # increase prior precision for -1, 0, 1 copies at ploidy - #param$lambda[param$ct %in% c(1,2,3)] <- 1000 # HETD, NEUT, GAIN - #param$lambda[param$ct == 4] <- 100 - #param$lambda[which.max(param$ct)] <- 50 #highest CN - #param$lambda[param$ct == 0] <- 1 #HOMD - S <- param$numberSamples - logR.var <- 1 / ((apply(x, 2, sd, na.rm = TRUE) / sqrt(length(param$ct))) ^ 2) - if (!is.null(dim(x))){ # multiple samples (columns) - param$lambda <- matrix(logR.var, nrow=K, ncol=S, byrow=T, dimnames=list(c(),colnames(x))) - }else{ # only 1 sample - #logR.var <- 1 / ((sd(x, na.rm = TRUE) / sqrt(length(param$ct))) ^ 2) - param$lambda <- matrix(logR.var, length(param$ct)) - param$lambda[param$ct %in% c(2)] <- logR.var - param$lambda[param$ct %in% c(1,3)] <- logR.var - param$lambda[param$ct >= 4] <- logR.var / 5 - param$lambda[param$ct == max(param$ct)] <- logR.var / 15 - param$lambda[param$ct.sc.status] <- logR.var / 10 - } - # define joint copy number states # - param$jointCNstates <- expand.grid(rep(list(param$ct), S)) - param$jointSCstatus <- expand.grid(rep(list(param$ct.sc.status), S)) - colnames(param$jointCNstates) <- paste0("Sample.", 1:param$numberSamples) - colnames(param$jointSCstatus) <- paste0("Sample.", 1:param$numberSamples) - - # Initialize transition matrix to the prior - txn <- getTransitionMatrix(K ^ S, e, strength) - ## set higher transition probs for same CN states across samples ## - # joint states where at least "tol" fraction of samples with the same CN state - #apply(param$jointCNstates, 1, function(x){ sum(duplicated(as.numeric(x))) > 0 }) - cnStateDiff <- apply(param$jointCNstates, 1, function(x){ (abs(max(x) - min(x)))}) - if (e.sameState > 0 & S > 1){ - txn$A[, cnStateDiff == 0] <- txn$A[, cnStateDiff == 0] * e.sameState * K - txn$A[, cnStateDiff >= 3] <- txn$A[, cnStateDiff >=3] / e.sameState / K - } - for (i in 1:nrow(txn$A)){ - for (j in 1:ncol(txn$A)){ - if (i == j){ - txn$A[i, j] <- e - } - } - } - txn$A <- normalize(txn$A) - param$A <- txn$A - param$dirPrior <- txn$A * strength[1] - param$A[, param$ct.sc.status] <- param$A[, param$ct.sc.status] / 10 - param$A <- normalize(param$A) - param$dirPrior[, param$ct.sc.status] <- param$dirPrior[, param$ct.sc.status] / 10 - - if (includeHOMD){ - K <- length(param$ct) - param$A[1, 2:K] <- param$A[1, 2:K] * 1e-5; param$A[2:K, 1] <- param$A[2:K, 1] * 1e-5; - param$A[1, 1] <- param$A[1, 1] * 1e-5 - param$A <- normalize(param$A); param$dirPrior <- param$A * param$strength - } - - param$kappa <- rep(75, K ^ S) - param$kappa[cnStateDiff == 0] <- param$kappa[cnStateDiff == 0] + 125 - param$kappa[cnStateDiff >=3] <- param$kappa[cnStateDiff >=3] - 50 - param$kappa[which(rowSums(param$jointCNstates==2) == S)] <- 800 - - return(param) -} - - - -segmentData <- function(dataGR, validInd, states, convergedParams){ - if (sum(convergedParams$param$ct == 0) ==0){ - includeHOMD <- FALSE - }else{ - includeHOMD <- TRUE - } - if (!includeHOMD){ - names <- c("HETD","NEUT","GAIN","AMP","HLAMP",paste0(rep("HLAMP", 8), 2:25)) - }else{ - names <- c("HOMD","HETD","NEUT","GAIN","AMP","HLAMP",paste0(rep("HLAMP", 8), 2:25)) - } - states <- states[validInd] - S <- length(dataGR) - jointStates <- convergedParams$param$jointCNstates - jointSCstatus <- convergedParams$param$jointSCstatus - colNames <- c("seqnames", "start", "end", "copy") - segList <- list() - for (i in 1:S){ - id <- names(dataGR)[i] - dataIn <- dataGR[[i]][validInd, ] - rleResults <- t(sapply(runValue(seqnames(dataIn)), function(x){ - ind <- as.character(seqnames(dataIn)) == x - r <- rle(states[ind]) - })) - rleLengths <- unlist(rleResults[, "lengths"]) - rleValues <- unlist(rleResults[, "values"]) - sampleDF <- as.data.frame(dataIn) - numSegs <- length(rleLengths) - segs <- as.data.frame(matrix(NA, ncol = 7, nrow = numSegs, - dimnames = list(c(), c("chr", "start", "end", "state", "event", "median", "copy.number")))) - prevInd <- 0 - for (j in 1:numSegs){ - start <- prevInd + 1 - end <- prevInd + rleLengths[j] - segDF <- sampleDF[start:end, colNames] - prevInd <- end - numR <- nrow(segDF) - segs[j, "chr"] <- as.character(segDF[1, "seqnames"]) - segs[j, "start"] <- segDF[1, "start"] - segs[j, "state"] <- rleValues[j] - segs[j, "copy.number"] <- jointStates[rleValues[j], i] - if (segDF[1, "seqnames"] == segDF[numR, "seqnames"]){ - segs[j, "end"] <- segDF[numR, "end"] - segs[j, "median"] <- round(median(segDF$copy, na.rm = TRUE), digits = 6) - if (includeHOMD){ - segs[j, "event"] <- names[segs[j, "copy.number"] + 1] - }else{ - segs[j, "event"] <- names[segs[j, "copy.number"]] - } - }else{ # segDF contains 2 different chromosomes - print(j) - } - } - segList[[id]] <- segs - } - return(segList) -} - - -runViterbi <- function(convergedParams, chr){ - message("runViterbi: Segmenting and classifying") - chrs <- levels(chr) - chrsI <- vector('list', length(chrs)) - # initialise the chromosome index and the init state distributions - for(i in 1:length(chrs)) { - chrsI[i] <- list(which(chr == chrs[i])) - } - segs <- vector('list', length(chrs)) - py <- convergedParams$py - N <- ncol(py) - Z <- rep(0, N) - convergeIter <- convergedParams$iter - piG <- convergedParams$pi[, convergeIter] - A <- convergedParams$A - - - for(c in 1:length(chrsI)) { - I <- chrsI[[c]] - output <- .Call("viterbi", log(piG), log(A), log(py[, I]), PACKAGE = "HMMcopy") - Z[I] <- output$path - segs[[c]] <- output$seg - } - return(list(segs=segs, states=Z)) -} - -# Normalize a given array to sum to 1 -normalize <- function(A) { - vectorNormalize <- function(x){ x / (sum(x) + (sum(x) == 0)) } - if (length(dim(A)) < 2){ - M <- vectorNormalize(A) - }else{ - M <- t(apply(A, 1, vectorNormalize)) - } - return(M); -} - - -# processSegments <- function(seg, chr, start, end, copy) { -# segment <- data.frame() -# chromosomes <- levels(chr) -# for (i in 1:length(chromosomes)) { -# seg_length = dim(seg[[i]])[1] -# chr_name <- rep(chromosomes[i], seg_length) -# chr_index <- which(chr == chromosomes[i]) -# chr_start <- start[chr_index][seg[[i]][, 1]] -# chr_stop <- end[chr_index][seg[[i]][, 2]] -# chr_state <- seg[[i]][, 3] -# chr_median <- rep(0, seg_length) -# for(j in 1:seg_length) { -# chr_median[j] <- -# median(na.rm = TRUE, log2(exp(copy[chr_index][seg[[i]][j, 1]:seg[[i]][j, 2]]))) -# } -# segment <- rbind(segment, cbind(chr = chr_name, -# start = as.numeric(chr_start), end = chr_stop, state = chr_state, -# median = chr_median)) -# } -# segment <- transform(segment, start = as.numeric(as.character(start)), -# end = as.numeric(as.character(end)), as.numeric(as.character(state)), -# median = as.numeric(as.character(median))) -# return(segment) -# } diff --git a/modules/ichorcna/1.0/src/R/utils.R b/modules/ichorcna/1.0/src/R/utils.R deleted file mode 100644 index 15ce0112..00000000 --- a/modules/ichorcna/1.0/src/R/utils.R +++ /dev/null @@ -1,441 +0,0 @@ -# file: utils.R -# author: Gavin Ha, Ph.D. -# Justin Rhoades -# Dana-Farber Cancer Institute -# Broad Institute -# contact: -# ULP-WGS website: http://www.broadinstitute.org/~gavinha/ULP-WGS/ -# HMMcopy website: http://compbio.bccrc.ca/software/hmmcopy/ and https://www.bioconductor.org/packages/release/bioc/html/HMMcopy.html -# date: Oct 26, 2016 -# description: Hidden Markov model (HMM) to analyze Ultra-low pass whole genome sequencing (ULP-WGS) data. -# This script is the main script to run the HMM. - -#################################### -##### FUNCTION TO FILTER CHRS ###### -#################################### -# updated for GRanges # -keepChr <- function(tumour_reads, chrs = c(1:22,"X","Y")){ - tumour_reads <- keepSeqlevels(tumour_reads, chrs, pruning.mode="coarse") - sortSeqlevels(tumour_reads) - return(sort(tumour_reads)) -} - -filterEmptyChr <- function(gr){ - require(plyr) - ind <- daply(as.data.frame(gr), .variables = "seqnames", .fun = function(x){ - rowInd <- apply(x[, 6:ncol(x), drop = FALSE], 1, function(y){ - sum(is.na(y)) == length(y) - }) - sum(rowInd) == nrow(x) - }) - return(keepSeqlevels(gr, value = names(which(!ind)))) -} - -#################################### -##### FUNCTION GET SEQINFO ###### -#################################### -getSeqInfo <- function(genomeBuild = "hg19", genomeStyle = "NCBI"){ - bsg <- paste0("BSgenome.Hsapiens.UCSC.", genomeBuild) - if (!require(bsg, character.only=TRUE, quietly=TRUE, warn.conflicts=FALSE)) { - seqinfo <- Seqinfo(genome=genomeBuild) - } else { - seqinfo <- seqinfo(get(bsg)) - } - seqlevelsStyle(seqinfo) <- genomeStyle - seqinfo <- keepSeqlevels(seqinfo, value = chrs) - #seqinfo <- cbind(seqnames = seqnames(seqinfo), as.data.frame(seqinfo)) - return(seqinfo) -} - -################################################## -##### FUNCTION TO FILTER CENTROMERE REGIONS ###### -################################################## -excludeCentromere <- function(x, centromere, flankLength = 0, genomeStyle = "NCBI"){ - require(GenomeInfoDb) - colnames(centromere)[1:3] <- c("seqnames","start","end") - centromere$start <- centromere$start - flankLength - centromere$end <- centromere$end + flankLength - centromere <- as(centromere, "GRanges") - seqlevelsStyle(centromere) <- genomeStyle - centromere <- sort(centromere) - hits <- findOverlaps(query = x, subject = centromere) - ind <- queryHits(hits) - message("Removed ", length(ind), " bins near centromeres.") - if (length(ind) > 0){ - x <- x[-ind, ] - } - return(x) -} - -################################################## -##### FUNCTION TO USE NCBI CHROMOSOME NAMES ###### -################################################## -## deprecated ## -setGenomeStyle <- function(x, genomeStyle = "NCBI", species = "Homo_sapiens"){ - require(GenomeInfoDb) - #chrs <- genomeStyles(species)[c("NCBI","UCSC")] - if (!genomeStyle %in% seqlevelsStyle(as.character(x))){ - x <- suppressWarnings(mapSeqlevels(as.character(x), - genomeStyle, drop = FALSE)[1,]) - } - - autoSexMChr <- extractSeqlevelsByGroup(species = species, - style = genomeStyle, group = "all") - x <- x[x %in% autoSexMChr] - return(x) -} - -wigToGRanges <- function(wigfile, verbose = TRUE){ - if (verbose) { message(paste("Slurping:", wigfile)) } - input <- readLines(wigfile, warn = FALSE) - breaks <- c(grep("fixedStep", input), length(input) + 1) - temp <- NULL - span <- NULL - for (i in 1:(length(breaks) - 1)) { - data_range <- (breaks[i] + 1):(breaks[i + 1] - 1) - track_info <- input[breaks[i]] - if (verbose) { message(paste("Parsing:", track_info)) } - tokens <- strsplit( - sub("fixedStep chrom=(\\S+) start=(\\d+) step=(\\d+) span=(\\d+)", - "\\1 \\2 \\3 \\4", track_info, perl = TRUE), " ")[[1]] - span <- as.integer(tokens[4]) - chr <- rep.int(tokens[1], length(data_range)) - pos <- seq(from = as.integer(tokens[2]), by = as.integer(tokens[3]), - length.out = length(data_range)) - val <- as.numeric(input[data_range]) - temp <- c(temp, list(data.frame(chr, pos, val))) - } - if (verbose) { message("Sorting by decreasing chromosome size") } - lengths <- as.integer(lapply(temp, nrow)) - temp <- temp[order(lengths, decreasing = TRUE)] - temp = do.call("rbind", temp) - output <- GenomicRanges::GRanges(ranges = IRanges(start = temp$pos, width = span), - seqnames = temp$chr, value = temp$val) - return(output) -} - - -loadReadCountsFromWig <- function(counts, chrs = c(1:22, "X", "Y"), gc = NULL, map = NULL, centromere = NULL, flankLength = 100000, targetedSequences = NULL, genomeStyle = "NCBI", applyCorrection = TRUE, mapScoreThres = 0.9, chrNormalize = c(1:22, "X", "Y"), fracReadsInChrYForMale = 0.002, chrXMedianForMale = -0.5, useChrY = TRUE){ - require(HMMcopy) - require(GenomeInfoDb) - seqlevelsStyle(counts) <- genomeStyle - counts.raw <- counts - counts <- keepChr(counts, chrs) - - if (!is.null(gc)){ - seqlevelsStyle(gc) <- genomeStyle - counts$gc <- keepChr(gc, chrs)$value - } - if (!is.null(map)){ - seqlevelsStyle(map) <- genomeStyle - counts$map <- keepChr(map, chrs)$value - } - colnames(values(counts))[1] <- c("reads") - - # remove centromeres - if (!is.null(centromere)){ - counts <- excludeCentromere(counts, centromere, flankLength = flankLength, genomeStyle=genomeStyle) - } - # keep targeted sequences - if (!is.null(targetedSequences)){ - colnames(targetedSequences)[1:3] <- c("chr", "start", "end") - targetedSequences.GR <- as(targetedSequences, "GRanges") - seqlevelsStyle(targetedSequences.GR) <- genomeStyle - countsExons <- filterByTargetedSequences(counts, targetedSequences.GR) - counts <- counts[countsExons$ix,] - } - gender <- NULL - if (applyCorrection){ - ## correct read counts ## - counts <- correctReadCounts(counts, chrNormalize = chrNormalize) - if (!is.null(map)) { - ## filter bins by mappability - counts <- filterByMappabilityScore(counts, map=map, mapScoreThres = mapScoreThres) - } - ## get gender ## - gender <- getGender(counts.raw, counts, gc, map, fracReadsInChrYForMale = fracReadsInChrYForMale, - chrXMedianForMale = chrXMedianForMale, useChrY = useChrY, - centromere=centromere, flankLength=flankLength, targetedSequences = targetedSequences, - genomeStyle = genomeStyle) - } - return(list(counts = counts, gender = gender)) -} - -filterByMappabilityScore <- function(counts, map, mapScoreThres = 0.9){ - message("Filtering low uniqueness regions with mappability score < ", mapScoreThres) - counts <- counts[counts$map >= mapScoreThres, ] - return(counts) -} - -filterByTargetedSequences <- function(counts, targetedSequences){ - ### for targeted sequencing (e.g. exome capture), - ### ignore bins with 0 for both tumour and normal - ### targetedSequence = GRanges object - ### containing list of targeted regions to consider; - ### 3 columns: chr, start, end - - hits <- findOverlaps(query = counts, subject = targetedSequences) - keepInd <- unique(queryHits(hits)) - - return(list(counts=counts, ix=keepInd)) -} - -selectFemaleChrXSolution <- function(){ - -} - -################################################## -### FUNCTION TO DETERMINE GENDER ################# -################################################## -getGender <- function(rawReads, normReads, gc, map, fracReadsInChrYForMale = 0.002, chrXMedianForMale = -0.5, useChrY = TRUE, - centromere=NULL, flankLength=1e5, targetedSequences=NULL, genomeStyle="NCBI"){ - chrXStr <- grep("X", runValue(seqnames(normReads)), value = TRUE) - chrYStr <- grep("Y", runValue(seqnames(rawReads)), value = TRUE) - chrXInd <- as.character(seqnames(normReads)) == chrXStr - if (sum(chrXInd) > 1){ ## if no X - chrXMedian <- median(normReads[chrXInd, ]$copy, na.rm = TRUE) - # proportion of reads in chrY # - tumY <- loadReadCountsFromWig(rawReads, chrs=chrYStr, genomeStyle=genomeStyle, - gc=gc, map=map, applyCorrection = FALSE, centromere=centromere, flankLength=flankLength, - targetedSequences=targetedSequences)$counts - chrYCov <- sum(tumY$reads) / sum(rawReads$value) - if (chrXMedian < chrXMedianForMale){ - if (useChrY && (chrYCov < fracReadsInChrYForMale)){ #trumps chrX if using chrY - gender <- "female" - }else{ - gender <- "male" # satisfies decreased chrX log ratio and/or increased chrY coverage - } - }else{ - gender <- "female" # chrX is provided but does not satisfies male critera - } - }else{ - gender <- "unknown" # chrX is not provided - chrYCov <- NA - chrXMedian <- NULL - } - return(list(gender=gender, chrYCovRatio=chrYCov, chrXMedian=chrXMedian)) -} - - -normalizeByPanelOrMatchedNormal <- function(tumour_copy, chrs = c(1:22, "X", "Y"), - normal_panel = NULL, normal_copy = NULL, gender = "female", normalizeMaleX = FALSE){ - genomeStyle <- seqlevelsStyle(tumour_copy)[1] - seqlevelsStyle(chrs) <- genomeStyle - ### COMPUTE LOG RATIO FROM MATCHED NORMAL OR PANEL AND HANDLE CHRX ### - ## NO PANEL - # matched normal but NO panel, then just normalize by matched normal (WES) - ## WHY DO WE NOT NORMALIZE BY NORMAL WITH PANEL? ## - chrXInd <- grep("X", as.character(seqnames(tumour_copy))) - chrXMedian <- median(tumour_copy[chrXInd, ]$copy, na.rm = TRUE) - if (!is.null(normal_copy) && is.null(normal_panel)){ - message("Normalizing Tumour by Normal") - tumour_copy$copy <- tumour_copy$copy - normal_copy$copy - rm(normal_copy) - } - # matched normal and panel and male, then compute normalized chrX median (WES) - if (!is.null(normal_copy) && !is.null(normal_panel) && gender=="male"){ - message("Normalizing by matched normal for ChrX") - chrX.MNnorm <- tumour_copy$copy[chrXInd] - normal_copy$copy[chrXInd] - chrXMedian.MNnorm <- median(chrX.MNnorm, na.rm = TRUE) - } - # if male, then just normalize chrX to median (ULP and WES) - if (is.null(normal_copy) && gender=="male" && !gender.mismatch && normalizeMaleX){ - tumour_copy$copy[chrXInd] <- tumour_copy$copy[chrXInd] - chrXMedian - } - # PANEL, then normalize by panel instead of matched normal (ULP and WES) - if (!is.null(normal_panel)){ - ## load in IRanges object, then convert to GRanges - panel <- readRDS(normal_panel) - seqlevelsStyle(panel) <- genomeStyle - panel <- keepChr(panel, chr = chrs) - # intersect bins in sample and panel - hits <- findOverlaps(tumour_copy, panel, type="equal") - tumour_copy <- tumour_copy[queryHits(hits),] - panel <- panel[subjectHits(hits),] - # subtract out panel median - tumour_copy$copy <- tumour_copy$copy - panel$Median - # if male, then shift chrX by +chrXMedian.MNnorm - if (gender == "male" && exists("chrXMedian.MNnorm")){ - tumour_copy$copy[chrXInd] <- tumour_copy$copy[chrXInd] + chrXMedian.MNnorm - } - } - return(tumour_copy) -} - -################################################## -###### FUNCTION TO CORRECT GC/MAP BIASES ######## -################################################## -correctReadCounts <- function(x, chrNormalize = c(1:22), mappability = 0.9, samplesize = 50000, - verbose = TRUE) { - if (length(x$reads) == 0 | length(x$gc) == 0) { - stop("Missing one of required columns: reads, gc") - } - chrInd <- as.character(seqnames(x)) %in% chrNormalize - if(verbose) { message("Applying filter on data...") } - x$valid <- TRUE - x$valid[x$reads <= 0 | x$gc < 0] <- FALSE - x$ideal <- TRUE - routlier <- 0.01 - range <- quantile(x$reads[x$valid & chrInd], prob = c(0, 1 - routlier), na.rm = TRUE) - doutlier <- 0.001 - domain <- quantile(x$gc[x$valid & chrInd], prob = c(doutlier, 1 - doutlier), na.rm = TRUE) - if (length(x$map) != 0) { - x$ideal[!x$valid | x$map < mappability | x$reads <= range[1] | - x$reads > range[2] | x$gc < domain[1] | x$gc > domain[2]] <- FALSE - } else { - x$ideal[!x$valid | x$reads <= range[1] | - x$reads > range[2] | x$gc < domain[1] | x$gc > domain[2]] <- FALSE - } - - if (verbose) { message("Correcting for GC bias...") } - set <- which(x$ideal & chrInd) - select <- sample(set, min(length(set), samplesize)) - rough = loess(x$reads[select] ~ x$gc[select], span = 0.03) - i <- seq(0, 1, by = 0.001) - final = loess(predict(rough, i) ~ i, span = 0.3) - x$cor.gc <- x$reads / predict(final, x$gc) - - if (length(x$map) != 0) { - if (verbose) { message("Correcting for mappability bias...") } - coutlier <- 0.01 - range <- quantile(x$cor.gc[which(x$valid & chrInd)], prob = c(0, 1 - coutlier), na.rm = TRUE) - set <- which(x$cor.gc < range[2] & chrInd) - select <- sample(set, min(length(set), samplesize)) - final = approxfun(lowess(x$map[select], x$cor.gc[select])) - x$cor.map <- x$cor.gc / final(x$map) - } else { - x$cor.map <- x$cor.gc - } - x$copy <- x$cor.map - x$copy[x$copy <= 0] = NA - x$copy <- log(x$copy, 2) - return(x) -} - -## Recompute integer CN for high-level amplifications ## -## compute logR-corrected copy number ## -correctIntegerCN <- function(cn, segs, callColName = "event", - purity, ploidy, cellPrev, maxCNtoCorrect.autosomes = NULL, - maxCNtoCorrect.X = NULL, correctHOMD = TRUE, minPurityToCorrect = 0.2, gender = "male", chrs = c(1:22, "X")){ - names <- c("HOMD","HETD","NEUT","GAIN","AMP","HLAMP", rep("HLAMP", 1000)) - - ## set up chromosome style - autosomeStr <- grep("X|Y", chrs, value=TRUE, invert=TRUE) - chrXStr <- grep("X", chrs, value=TRUE) - - if (is.null(maxCNtoCorrect.autosomes)){ - maxCNtoCorrect.autosomes <- max(segs[segs$chr %in% autosomeStr, "copy.number"], na.rm = TRUE) - } - if (is.null(maxCNtoCorrect.X) & gender == "female" & length(chrXStr) > 0){ - maxCNtoCorrect.X <- max(segs[segs$chr == chrXStr, "copy.number"], na.rm=TRUE) - } - ## correct log ratio and compute corrected CN - cellPrev.seg <- rep(1, nrow(segs)) - cellPrev.seg[as.logical(segs$subclone.status)] <- cellPrev - segs$logR_Copy_Number <- logRbasedCN(segs[["median"]], purity, ploidy, cellPrev.seg, cn=2) - if (gender == "male" & length(chrXStr) > 0){ ## analyze chrX separately - ind.cnChrX <- which(segs$chr == chrXStr) - segs$logR_Copy_Number[ind.cnChrX] <- logRbasedCN(segs[["median"]][ind.cnChrX], purity, ploidy, cellPrev.seg[ind.cnChrX], cn=1) - } - - ## assign copy number to use - Corrected_Copy_Number - # 1) ame ichorCNA calls for autosomes - no change in copy number - segs$Corrected_Copy_Number <- as.integer(segs$copy.number) - segs$Corrected_Call <- segs[[callColName]] - - ind.change <- c() - if (purity >= minPurityToCorrect){ - # 2) ichorCNA calls adjusted for >= copies - HLAMP - # perform on all chromosomes - ind.cn <- which(segs$copy.number >= maxCNtoCorrect.autosomes | - (segs$logR_Copy_Number >= maxCNtoCorrect.autosomes * 1.2 & !is.infinite(segs$logR_Copy_Number))) - segs$Corrected_Copy_Number[ind.cn] <- as.integer(round(segs$logR_Copy_Number[ind.cn])) - segs$Corrected_Call[ind.cn] <- names[segs$Corrected_Copy_Number[ind.cn] + 1] - ind.change <- c(ind.change, ind.cn) - - # 3) ichorCNA calls adjust for HOMD - if (correctHOMD){ - ind.cn <- which(segs$chr %in% chrs & - (segs$copy.number == 0 | segs$logR_Copy_Number == 1/2^6)) - segs$Corrected_Copy_Number[ind.cn] <- as.integer(round(segs$logR_Copy_Number[ind.cn])) - segs$Corrected_Call[ind.cn] <- names[segs$Corrected_Copy_Number[ind.cn] + 1] - ind.change <- c(ind.change, ind.cn) - } - # 4) Re-adjust chrX copy number for males (females already handled above) - if (gender == "male" & length(chrXStr) > 0){ - ind.cn <- which(segs$chr == chrXStr & - (segs$copy.number >= maxCNtoCorrect.X | segs$logR_Copy_Number >= maxCNtoCorrect.X * 1.2)) - segs$Corrected_Copy_Number[ind.cn] <- as.integer(round(segs$logR_Copy_Number[ind.cn])) - segs$Corrected_Call[ind.cn] <- names[segs$Corrected_Copy_Number[ind.cn] + 2] - ind.change <- c(ind.change, ind.cn) - } - } - ## adjust the bin level data ## - # 1) assign the original calls - cn$Corrected_Copy_Number <- as.integer(cn$copy.number) - cn$Corrected_Call <- cn[[callColName]] - cellPrev.cn <- rep(1, nrow(cn)) - cellPrev.cn[as.logical(cn$subclone.status)] <- cellPrev - cn$logR_Copy_Number <- logRbasedCN(cn[["logR"]], purity, ploidy, cellPrev.cn, cn=2) - if (gender == "male" & length(chrXStr) > 0){ ## analyze chrX separately - ind.cnChrX <- which(cn$chr == chrXStr) - cn$logR_Copy_Number[ind.cnChrX] <- logRbasedCN(cn[["logR"]][ind.cnChrX], purity, ploidy, cellPrev.cn[ind.cnChrX], cn=1) - } - if (purity >= minPurityToCorrect){ - # 2) correct bins overlapping adjusted segs - ind.change <- unique(ind.change) - ind.overlapSegs <- c() - if (length(ind.change) > 0){ - cn.gr <- as(cn, "GRanges") - segs.gr <- as(segs, "GRanges") - hits <- findOverlaps(query = cn.gr, subject = segs.gr[ind.change]) - cn$Corrected_Copy_Number[queryHits(hits)] <- segs$Corrected_Copy_Number[ind.change][subjectHits(hits)] - cn$Corrected_Call[queryHits(hits)] <- segs$Corrected_Call[ind.change][subjectHits(hits)] - ind.overlapSegs <- queryHits(hits) - } - # 3) correct bins that are missed as high level amplifications - ind.hlamp <- which(cn$copy.number >= maxCNtoCorrect.autosomes | - (cn$logR_Copy_Number >= maxCNtoCorrect.autosomes * 1.2 & !is.infinite(cn$logR_Copy_Number))) - ind.cn <- unique(ind.hlamp, ind.overlapSegs) - cn$Corrected_Copy_Number[ind.cn] <- as.integer(round(cn$logR_Copy_Number[ind.cn])) - cn$Corrected_Call[ind.cn] <- names[cn$Corrected_Copy_Number[ind.cn] + 1] - } - - return(list(cn = cn, segs = segs)) -} - - -## compute copy number using corrected log ratio ## -logRbasedCN <- function(x, purity, ploidyT, cellPrev=NA, cn = 2){ - if (length(cellPrev) == 1 && is.na(cellPrev)){ - cellPrev <- 1 - }else{ #if cellPrev is a vector - cellPrev[is.na(cellPrev)] <- 1 - } - ct <- (2^x - * (cn * (1 - purity) + purity * ploidyT * (cn / 2)) - - (cn * (1 - purity)) - - (cn * purity * (1 - cellPrev))) - ct <- ct / (purity * cellPrev) - ct <- sapply(ct, max, 1/2^6) - return(ct) -} - - -computeBIC <- function(params){ - iter <- params$iter - KS <- nrow(params$rho) # num states - N <- ncol(params$rho) # num data points - NP <- nrow(params$n) + nrow(params$phi) # normal + ploidy - L <- 1 # precision (lambda) - numParams <- KS * (KS - 1) + KS * (L + NP) - 1 - b <- -2 * params$loglik[iter] + numParams * log(N) - return(b) -} - - -############################################################# -## function to compute power from ULP-WGS purity/ploidy ##### -############################################################# -# current assumptions: power for clonal heterozygous mutations - diff --git a/modules/ichorcna/1.0/src/inst/extdata/GRCh37.p13_centromere_UCSC-gapTable.txt b/modules/ichorcna/1.0/src/inst/extdata/GRCh37.p13_centromere_UCSC-gapTable.txt deleted file mode 100644 index c74fb414..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/GRCh37.p13_centromere_UCSC-gapTable.txt +++ /dev/null @@ -1,25 +0,0 @@ -Chr Start End GapType -1 121535434 124535434 centromere -2 92326171 95326171 centromere -3 90504854 93504854 centromere -4 49660117 52660117 centromere -5 46405641 49405641 centromere -6 58830166 61830166 centromere -7 58054331 61054331 centromere -8 43838887 46838887 centromere -9 47367679 50367679 centromere -10 39254935 42254935 centromere -11 51644205 54644205 centromere -12 34856694 37856694 centromere -13 16000000 19000000 centromere -14 16000000 19000000 centromere -15 17000000 20000000 centromere -16 35335801 38335801 centromere -17 22263006 25263006 centromere -18 15460898 18460898 centromere -19 24681782 27681782 centromere -20 26369569 29369569 centromere -21 11288129 14288129 centromere -22 13000000 16000000 centromere -X 58632012 61632012 centromere -Y 10104553 13104553 centromere diff --git a/modules/ichorcna/1.0/src/inst/extdata/GRCh38.GCA_000001405.2_centromere_acen.txt b/modules/ichorcna/1.0/src/inst/extdata/GRCh38.GCA_000001405.2_centromere_acen.txt deleted file mode 100644 index c5c49a79..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/GRCh38.GCA_000001405.2_centromere_acen.txt +++ /dev/null @@ -1,25 +0,0 @@ -Chr Start End GapType -chr1 121700000 125100000 centromere -chr2 91800000 96000000 centromere -chr3 87800000 94000000 centromere -chr4 48200000 51800000 centromere -chr5 46100000 51400000 centromere -chr6 58500000 62600000 centromere -chr7 58100000 62100000 centromere -chr8 43200000 47200000 centromere -chr9 42200000 45500000 centromere -chr10 38000000 41600000 centromere -chr11 51000000 55800000 centromere -chr12 33200000 37800000 centromere -chr13 16500000 18900000 centromere -chr14 16100000 18200000 centromere -chr15 17500000 20500000 centromere -chr16 35300000 38400000 centromere -chr17 22700000 27400000 centromere -chr18 15400000 21500000 centromere -chr19 24200000 28100000 centromere -chr20 25700000 30400000 centromere -chr21 10900000 13000000 centromere -chr22 13700000 17400000 centromere -chrX 58100000 63800000 centromere -chrY 10300000 10600000 centromere diff --git a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_grch37_1Mb_median_normAutosome_median.rds b/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_grch37_1Mb_median_normAutosome_median.rds deleted file mode 120000 index 8f465a8d..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_grch37_1Mb_median_normAutosome_median.rds +++ /dev/null @@ -1 +0,0 @@ -HD_ULP_PoN_hg19_1Mb_median_normAutosome_median.rds \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_grch37_500kb_normAutosome_median.rds b/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_grch37_500kb_normAutosome_median.rds deleted file mode 120000 index b6a2d983..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_grch37_500kb_normAutosome_median.rds +++ /dev/null @@ -1 +0,0 @@ -HD_ULP_PoN_hg19_500kb_normAutosome_median.rds \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_grch38_1Mb_median_normAutosome_median.rds b/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_grch38_1Mb_median_normAutosome_median.rds deleted file mode 120000 index f2dc78ea..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_grch38_1Mb_median_normAutosome_median.rds +++ /dev/null @@ -1 +0,0 @@ -HD_ULP_PoN_hg38_1Mb_median_normAutosome_median.rds \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_grch38_500kb_median_normAutosome_median.rds b/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_grch38_500kb_median_normAutosome_median.rds deleted file mode 120000 index 89f6c8a3..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_grch38_500kb_median_normAutosome_median.rds +++ /dev/null @@ -1 +0,0 @@ -HD_ULP_PoN_hg38_500kb_median_normAutosome_median.rds \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_hg19_1Mb_median_normAutosome_median.rds b/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_hg19_1Mb_median_normAutosome_median.rds deleted file mode 100644 index 2fe1b952..00000000 Binary files a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_hg19_1Mb_median_normAutosome_median.rds and /dev/null differ diff --git a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_hg19_500kb_normAutosome_median.rds b/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_hg19_500kb_normAutosome_median.rds deleted file mode 100644 index 81be50ae..00000000 Binary files a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_hg19_500kb_normAutosome_median.rds and /dev/null differ diff --git a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_hg38_1Mb_median_normAutosome_median.rds b/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_hg38_1Mb_median_normAutosome_median.rds deleted file mode 100644 index dba87c8c..00000000 Binary files a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_hg38_1Mb_median_normAutosome_median.rds and /dev/null differ diff --git a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_hg38_500kb_median_normAutosome_median.rds b/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_hg38_500kb_median_normAutosome_median.rds deleted file mode 100644 index 03b95a6f..00000000 Binary files a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_hg38_500kb_median_normAutosome_median.rds and /dev/null differ diff --git a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_hs37d5_1Mb_median_normAutosome_median.rds b/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_hs37d5_1Mb_median_normAutosome_median.rds deleted file mode 120000 index 8f465a8d..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_hs37d5_1Mb_median_normAutosome_median.rds +++ /dev/null @@ -1 +0,0 @@ -HD_ULP_PoN_hg19_1Mb_median_normAutosome_median.rds \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_hs37d5_500kb_normAutosome_median.rds b/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_hs37d5_500kb_normAutosome_median.rds deleted file mode 120000 index b6a2d983..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/HD_ULP_PoN_hs37d5_500kb_normAutosome_median.rds +++ /dev/null @@ -1 +0,0 @@ -HD_ULP_PoN_hg19_500kb_normAutosome_median.rds \ No newline at end of file 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q21.2 gneg -chr7 93300000 98400000 q21.3 gpos75 -chr7 98400000 104200000 q22.1 gneg -chr7 104200000 104900000 q22.2 gpos50 -chr7 104900000 107800000 q22.3 gneg -chr7 107800000 115000000 q31.1 gpos75 -chr7 115000000 117700000 q31.2 gneg -chr7 117700000 121400000 q31.31 gpos75 -chr7 121400000 124100000 q31.32 gneg -chr7 124100000 127500000 q31.33 gpos75 -chr7 127500000 129600000 q32.1 gneg -chr7 129600000 130800000 q32.2 gpos25 -chr7 130800000 132900000 q32.3 gneg -chr7 132900000 138500000 q33 gpos50 -chr7 138500000 143400000 q34 gneg -chr7 143400000 148200000 q35 gpos75 -chr7 148200000 152800000 q36.1 gneg -chr7 152800000 155200000 q36.2 gpos25 -chr7 155200000 159345973 q36.3 gneg -chr7_GL383534v2_alt 0 119183 gneg -chr7_KI270803v1_alt 0 1111570 gneg -chr7_KI270804v1_alt 0 157952 gneg -chr7_KI270805v1_alt 0 209988 gneg -chr7_KI270806v1_alt 0 158166 gneg -chr7_KI270807v1_alt 0 126434 gneg -chr7_KI270808v1_alt 0 271455 gneg -chr7_KI270809v1_alt 0 209586 gneg -chr7_KI270899v1_alt 0 190869 gneg -chr8 0 2300000 p23.3 gneg -chr8 2300000 6300000 p23.2 gpos75 -chr8 6300000 12800000 p23.1 gneg -chr8 12800000 19200000 p22 gpos100 -chr8 19200000 23500000 p21.3 gneg -chr8 23500000 27500000 p21.2 gpos50 -chr8 27500000 29000000 p21.1 gneg -chr8 29000000 36700000 p12 gpos75 -chr8 36700000 38500000 p11.23 gneg -chr8 38500000 39900000 p11.22 gpos25 -chr8 39900000 43200000 p11.21 gneg -chr8 43200000 45200000 p11.1 acen -chr8 45200000 47200000 q11.1 acen -chr8 47200000 51300000 q11.21 gneg -chr8 51300000 51700000 q11.22 gpos75 -chr8 51700000 54600000 q11.23 gneg -chr8 54600000 60600000 q12.1 gpos50 -chr8 60600000 61300000 q12.2 gneg -chr8 61300000 65100000 q12.3 gpos50 -chr8 65100000 67100000 q13.1 gneg -chr8 67100000 69600000 q13.2 gpos50 -chr8 69600000 72000000 q13.3 gneg -chr8 72000000 74600000 q21.11 gpos100 -chr8 74600000 74700000 q21.12 gneg -chr8 74700000 83500000 q21.13 gpos75 -chr8 83500000 85900000 q21.2 gneg -chr8 85900000 92300000 q21.3 gpos100 -chr8 92300000 97900000 q22.1 gneg -chr8 97900000 100500000 q22.2 gpos25 -chr8 100500000 105100000 q22.3 gneg -chr8 105100000 109500000 q23.1 gpos75 -chr8 109500000 111100000 q23.2 gneg -chr8 111100000 116700000 q23.3 gpos100 -chr8 116700000 118300000 q24.11 gneg -chr8 118300000 121500000 q24.12 gpos50 -chr8 121500000 126300000 q24.13 gneg -chr8 126300000 130400000 q24.21 gpos50 -chr8 130400000 135400000 q24.22 gneg -chr8 135400000 138900000 q24.23 gpos75 -chr8 138900000 145138636 q24.3 gneg -chr8_KI270810v1_alt 0 374415 gneg -chr8_KI270811v1_alt 0 292436 gneg -chr8_KI270812v1_alt 0 282736 gneg -chr8_KI270813v1_alt 0 300230 gneg -chr8_KI270814v1_alt 0 141812 gneg -chr8_KI270815v1_alt 0 132244 gneg -chr8_KI270816v1_alt 0 305841 gneg -chr8_KI270817v1_alt 0 158983 gneg -chr8_KI270818v1_alt 0 145606 gneg -chr8_KI270819v1_alt 0 133535 gneg -chr8_KI270820v1_alt 0 36640 gneg -chr8_KI270821v1_alt 0 985506 gneg -chr8_KI270822v1_alt 0 624492 gneg -chr8_KI270900v1_alt 0 318687 gneg -chr8_KI270901v1_alt 0 136959 gneg -chr8_KI270926v1_alt 0 229282 gneg -chr9 0 2200000 p24.3 gneg -chr9 2200000 4600000 p24.2 gpos25 -chr9 4600000 9000000 p24.1 gneg -chr9 9000000 14200000 p23 gpos75 -chr9 14200000 16600000 p22.3 gneg -chr9 16600000 18500000 p22.2 gpos25 -chr9 18500000 19900000 p22.1 gneg -chr9 19900000 25600000 p21.3 gpos100 -chr9 25600000 28000000 p21.2 gneg -chr9 28000000 33200000 p21.1 gpos100 -chr9 33200000 36300000 p13.3 gneg -chr9 36300000 37900000 p13.2 gpos25 -chr9 37900000 39000000 p13.1 gneg -chr9 39000000 40000000 p12 gpos50 -chr9 40000000 42200000 p11.2 gneg -chr9 42200000 43000000 p11.1 acen -chr9 43000000 45500000 q11 acen -chr9 45500000 61500000 q12 gvar -chr9 61500000 65000000 q13 gneg -chr9 65000000 69300000 q21.11 gpos25 -chr9 69300000 71300000 q21.12 gneg -chr9 71300000 76600000 q21.13 gpos50 -chr9 76600000 78500000 q21.2 gneg -chr9 78500000 81500000 q21.31 gpos50 -chr9 81500000 84300000 q21.32 gneg -chr9 84300000 87800000 q21.33 gpos50 -chr9 87800000 89200000 q22.1 gneg -chr9 89200000 91200000 q22.2 gpos25 -chr9 91200000 93900000 q22.31 gneg -chr9 93900000 96500000 q22.32 gpos25 -chr9 96500000 99800000 q22.33 gneg -chr9 99800000 105400000 q31.1 gpos100 -chr9 105400000 108500000 q31.2 gneg -chr9 108500000 112100000 q31.3 gpos25 -chr9 112100000 114900000 q32 gneg -chr9 114900000 119800000 q33.1 gpos75 -chr9 119800000 123100000 q33.2 gneg -chr9 123100000 127500000 q33.3 gpos25 -chr9 127500000 130600000 q34.11 gneg -chr9 130600000 131100000 q34.12 gpos25 -chr9 131100000 133100000 q34.13 gneg -chr9 133100000 134500000 q34.2 gpos25 -chr9 134500000 138394717 q34.3 gneg -chr9_GL383539v1_alt 0 162988 gneg -chr9_GL383540v1_alt 0 71551 gneg -chr9_GL383541v1_alt 0 171286 gneg -chr9_GL383542v1_alt 0 60032 gneg -chr9_KI270717v1_random 0 40062 gneg -chr9_KI270718v1_random 0 38054 gneg -chr9_KI270719v1_random 0 176845 gneg -chr9_KI270720v1_random 0 39050 gneg -chr9_KI270823v1_alt 0 439082 gneg -chrM 0 16569 gneg -chrUn_GL000195v1 0 182896 gneg -chrUn_GL000213v1 0 164239 gneg -chrUn_GL000214v1 0 137718 gneg -chrUn_GL000216v2 0 176608 gneg -chrUn_GL000218v1 0 161147 gneg -chrUn_GL000219v1 0 179198 gneg -chrUn_GL000220v1 0 161802 gneg -chrUn_GL000224v1 0 179693 gneg -chrUn_GL000226v1 0 15008 gneg -chrUn_KI270302v1 0 2274 gneg -chrUn_KI270303v1 0 1942 gneg -chrUn_KI270304v1 0 2165 gneg -chrUn_KI270305v1 0 1472 gneg -chrUn_KI270310v1 0 1201 gneg -chrUn_KI270311v1 0 12399 gneg -chrUn_KI270312v1 0 998 gneg -chrUn_KI270315v1 0 2276 gneg -chrUn_KI270316v1 0 1444 gneg -chrUn_KI270317v1 0 37690 gneg -chrUn_KI270320v1 0 4416 gneg -chrUn_KI270322v1 0 21476 gneg -chrUn_KI270329v1 0 1040 gneg -chrUn_KI270330v1 0 1652 gneg -chrUn_KI270333v1 0 2699 gneg -chrUn_KI270334v1 0 1368 gneg -chrUn_KI270335v1 0 1048 gneg -chrUn_KI270336v1 0 1026 gneg -chrUn_KI270337v1 0 1121 gneg -chrUn_KI270338v1 0 1428 gneg -chrUn_KI270340v1 0 1428 gneg -chrUn_KI270362v1 0 3530 gneg -chrUn_KI270363v1 0 1803 gneg -chrUn_KI270364v1 0 2855 gneg -chrUn_KI270366v1 0 8320 gneg -chrUn_KI270371v1 0 2805 gneg -chrUn_KI270372v1 0 1650 gneg -chrUn_KI270373v1 0 1451 gneg -chrUn_KI270374v1 0 2656 gneg -chrUn_KI270375v1 0 2378 gneg -chrUn_KI270376v1 0 1136 gneg -chrUn_KI270378v1 0 1048 gneg -chrUn_KI270379v1 0 1045 gneg -chrUn_KI270381v1 0 1930 gneg -chrUn_KI270382v1 0 4215 gneg -chrUn_KI270383v1 0 1750 gneg -chrUn_KI270384v1 0 1658 gneg -chrUn_KI270385v1 0 990 gneg -chrUn_KI270386v1 0 1788 gneg -chrUn_KI270387v1 0 1537 gneg -chrUn_KI270388v1 0 1216 gneg -chrUn_KI270389v1 0 1298 gneg -chrUn_KI270390v1 0 2387 gneg -chrUn_KI270391v1 0 1484 gneg -chrUn_KI270392v1 0 971 gneg -chrUn_KI270393v1 0 1308 gneg -chrUn_KI270394v1 0 970 gneg -chrUn_KI270395v1 0 1143 gneg -chrUn_KI270396v1 0 1880 gneg -chrUn_KI270411v1 0 2646 gneg -chrUn_KI270412v1 0 1179 gneg -chrUn_KI270414v1 0 2489 gneg -chrUn_KI270417v1 0 2043 gneg -chrUn_KI270418v1 0 2145 gneg -chrUn_KI270419v1 0 1029 gneg -chrUn_KI270420v1 0 2321 gneg -chrUn_KI270422v1 0 1445 gneg -chrUn_KI270423v1 0 981 gneg -chrUn_KI270424v1 0 2140 gneg -chrUn_KI270425v1 0 1884 gneg -chrUn_KI270429v1 0 1361 gneg -chrUn_KI270435v1 0 92983 gneg -chrUn_KI270438v1 0 112505 gneg -chrUn_KI270442v1 0 392061 gneg -chrUn_KI270448v1 0 7992 gneg -chrUn_KI270465v1 0 1774 gneg -chrUn_KI270466v1 0 1233 gneg -chrUn_KI270467v1 0 3920 gneg -chrUn_KI270468v1 0 4055 gneg -chrUn_KI270507v1 0 5353 gneg -chrUn_KI270508v1 0 1951 gneg -chrUn_KI270509v1 0 2318 gneg -chrUn_KI270510v1 0 2415 gneg -chrUn_KI270511v1 0 8127 gneg -chrUn_KI270512v1 0 22689 gneg -chrUn_KI270515v1 0 6361 gneg -chrUn_KI270516v1 0 1300 gneg -chrUn_KI270517v1 0 3253 gneg -chrUn_KI270518v1 0 2186 gneg -chrUn_KI270519v1 0 138126 gneg -chrUn_KI270521v1 0 7642 gneg -chrUn_KI270522v1 0 5674 gneg -chrUn_KI270528v1 0 2983 gneg -chrUn_KI270529v1 0 1899 gneg -chrUn_KI270530v1 0 2168 gneg -chrUn_KI270538v1 0 91309 gneg -chrUn_KI270539v1 0 993 gneg -chrUn_KI270544v1 0 1202 gneg -chrUn_KI270548v1 0 1599 gneg -chrUn_KI270579v1 0 31033 gneg -chrUn_KI270580v1 0 1553 gneg -chrUn_KI270581v1 0 7046 gneg -chrUn_KI270582v1 0 6504 gneg -chrUn_KI270583v1 0 1400 gneg -chrUn_KI270584v1 0 4513 gneg -chrUn_KI270587v1 0 2969 gneg -chrUn_KI270588v1 0 6158 gneg -chrUn_KI270589v1 0 44474 gneg -chrUn_KI270590v1 0 4685 gneg -chrUn_KI270591v1 0 5796 gneg -chrUn_KI270593v1 0 3041 gneg -chrUn_KI270741v1 0 157432 gneg -chrUn_KI270742v1 0 186739 gneg -chrUn_KI270743v1 0 210658 gneg -chrUn_KI270744v1 0 168472 gneg -chrUn_KI270745v1 0 41891 gneg -chrUn_KI270746v1 0 66486 gneg -chrUn_KI270747v1 0 198735 gneg -chrUn_KI270748v1 0 93321 gneg -chrUn_KI270749v1 0 158759 gneg -chrUn_KI270750v1 0 148850 gneg -chrUn_KI270751v1 0 150742 gneg -chrUn_KI270752v1 0 27745 gneg -chrUn_KI270753v1 0 62944 gneg -chrUn_KI270754v1 0 40191 gneg -chrUn_KI270755v1 0 36723 gneg -chrUn_KI270756v1 0 79590 gneg -chrUn_KI270757v1 0 71251 gneg -chrX 0 4400000 p22.33 gneg -chrX 4400000 6100000 p22.32 gpos50 -chrX 6100000 9600000 p22.31 gneg -chrX 9600000 17400000 p22.2 gpos50 -chrX 17400000 19200000 p22.13 gneg -chrX 19200000 21900000 p22.12 gpos50 -chrX 21900000 24900000 p22.11 gneg -chrX 24900000 29300000 p21.3 gpos100 -chrX 29300000 31500000 p21.2 gneg -chrX 31500000 37800000 p21.1 gpos100 -chrX 37800000 42500000 p11.4 gneg -chrX 42500000 47600000 p11.3 gpos75 -chrX 47600000 50100000 p11.23 gneg -chrX 50100000 54800000 p11.22 gpos25 -chrX 54800000 58100000 p11.21 gneg -chrX 58100000 61000000 p11.1 acen -chrX 61000000 63800000 q11.1 acen -chrX 63800000 65400000 q11.2 gneg -chrX 65400000 68500000 q12 gpos50 -chrX 68500000 73000000 q13.1 gneg -chrX 73000000 74700000 q13.2 gpos50 -chrX 74700000 76800000 q13.3 gneg -chrX 76800000 85400000 q21.1 gpos100 -chrX 85400000 87000000 q21.2 gneg -chrX 87000000 92700000 q21.31 gpos100 -chrX 92700000 94300000 q21.32 gneg -chrX 94300000 99100000 q21.33 gpos75 -chrX 99100000 103300000 q22.1 gneg -chrX 103300000 104500000 q22.2 gpos50 -chrX 104500000 109400000 q22.3 gneg -chrX 109400000 117400000 q23 gpos75 -chrX 117400000 121800000 q24 gneg -chrX 121800000 129500000 q25 gpos100 -chrX 129500000 131300000 q26.1 gneg -chrX 131300000 134500000 q26.2 gpos25 -chrX 134500000 138900000 q26.3 gneg -chrX 138900000 141200000 q27.1 gpos75 -chrX 141200000 143000000 q27.2 gneg -chrX 143000000 148000000 q27.3 gpos100 -chrX 148000000 156040895 q28 gneg -chrX_KI270880v1_alt 0 284869 gneg -chrX_KI270881v1_alt 0 144206 gneg -chrX_KI270913v1_alt 0 274009 gneg -chrY 0 300000 p11.32 gneg -chrY 300000 600000 p11.31 gpos50 -chrY 600000 10300000 p11.2 gneg -chrY 10300000 10400000 p11.1 acen -chrY 10400000 10600000 q11.1 acen -chrY 10600000 12400000 q11.21 gneg -chrY 12400000 17100000 q11.221 gpos50 -chrY 17100000 19600000 q11.222 gneg -chrY 19600000 23800000 q11.223 gpos50 -chrY 23800000 26600000 q11.23 gneg -chrY 26600000 57227415 q12 gvar -chrY_KI270740v1_random 0 37240 gneg diff --git a/modules/ichorcna/1.0/src/inst/extdata/gc_grch37_1000kb.wig b/modules/ichorcna/1.0/src/inst/extdata/gc_grch37_1000kb.wig deleted file mode 120000 index 2bebad69..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/gc_grch37_1000kb.wig +++ /dev/null @@ -1 +0,0 @@ -gc_hg19_1000kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/gc_grch37_10kb.wig b/modules/ichorcna/1.0/src/inst/extdata/gc_grch37_10kb.wig deleted file mode 120000 index b05e4304..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/gc_grch37_10kb.wig +++ /dev/null @@ -1 +0,0 @@ -gc_hg19_10kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/gc_grch37_500kb.wig b/modules/ichorcna/1.0/src/inst/extdata/gc_grch37_500kb.wig deleted file mode 120000 index 4eda72be..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/gc_grch37_500kb.wig +++ /dev/null @@ -1 +0,0 @@ -gc_hg19_500kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/gc_grch37_50kb.wig b/modules/ichorcna/1.0/src/inst/extdata/gc_grch37_50kb.wig deleted file mode 120000 index 5dbf6f7a..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/gc_grch37_50kb.wig +++ /dev/null @@ -1 +0,0 @@ -gc_hg19_50kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/gc_grch38_1000kb.wig b/modules/ichorcna/1.0/src/inst/extdata/gc_grch38_1000kb.wig deleted file mode 120000 index 5104a3a2..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/gc_grch38_1000kb.wig +++ /dev/null @@ -1 +0,0 @@ -gc_hg38_1000kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/gc_grch38_10kb.wig b/modules/ichorcna/1.0/src/inst/extdata/gc_grch38_10kb.wig deleted file mode 120000 index 546faccf..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/gc_grch38_10kb.wig +++ /dev/null @@ -1 +0,0 @@ -gc_hg38_10kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/gc_grch38_500kb.wig b/modules/ichorcna/1.0/src/inst/extdata/gc_grch38_500kb.wig deleted file mode 120000 index 7dd1c778..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/gc_grch38_500kb.wig +++ /dev/null @@ -1 +0,0 @@ -gc_hg38_500kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/gc_grch38_50kb.wig b/modules/ichorcna/1.0/src/inst/extdata/gc_grch38_50kb.wig deleted file mode 120000 index 83dd8f08..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/gc_grch38_50kb.wig +++ /dev/null @@ -1 +0,0 @@ -gc_hg38_50kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/gc_hg19_1000kb.wig b/modules/ichorcna/1.0/src/inst/extdata/gc_hg19_1000kb.wig deleted file mode 100644 index 776b5dde..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/gc_hg19_1000kb.wig +++ /dev/null @@ -1,3137 +0,0 @@ -fixedStep chrom=1 start=1 step=1000000 span=1000000 --1 -0.568915 --1 --1 -0.482201 -0.469288 -0.51752 -0.479461 -0.444356 -0.50197 -0.478274 -0.500043 -0.469178 --1 -0.431262 -0.48383 -0.496197 --1 -0.491613 -0.487321 -0.473806 -0.460534 -0.491468 -0.472911 -0.464707 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a/modules/ichorcna/1.0/src/inst/extdata/gc_hs37d5_1000kb.wig b/modules/ichorcna/1.0/src/inst/extdata/gc_hs37d5_1000kb.wig deleted file mode 120000 index 2bebad69..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/gc_hs37d5_1000kb.wig +++ /dev/null @@ -1 +0,0 @@ -gc_hg19_1000kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/gc_hs37d5_10kb.wig b/modules/ichorcna/1.0/src/inst/extdata/gc_hs37d5_10kb.wig deleted file mode 120000 index b05e4304..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/gc_hs37d5_10kb.wig +++ /dev/null @@ -1 +0,0 @@ -gc_hg19_10kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/gc_hs37d5_500kb.wig b/modules/ichorcna/1.0/src/inst/extdata/gc_hs37d5_500kb.wig deleted file mode 120000 index 4eda72be..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/gc_hs37d5_500kb.wig +++ /dev/null @@ -1 +0,0 @@ -gc_hg19_500kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/gc_hs37d5_50kb.wig b/modules/ichorcna/1.0/src/inst/extdata/gc_hs37d5_50kb.wig deleted file mode 120000 index 5dbf6f7a..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/gc_hs37d5_50kb.wig +++ /dev/null @@ -1 +0,0 @@ -gc_hg19_50kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/map_grch37_1000kb.wig b/modules/ichorcna/1.0/src/inst/extdata/map_grch37_1000kb.wig deleted file mode 120000 index 6002177c..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/map_grch37_1000kb.wig +++ /dev/null @@ -1 +0,0 @@ -map_hg19_1000kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/map_grch37_10kb.wig b/modules/ichorcna/1.0/src/inst/extdata/map_grch37_10kb.wig deleted file mode 120000 index 22314616..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/map_grch37_10kb.wig +++ /dev/null @@ -1 +0,0 @@ -map_hg19_10kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/map_grch37_500kb.wig b/modules/ichorcna/1.0/src/inst/extdata/map_grch37_500kb.wig deleted file mode 120000 index f6bd4be0..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/map_grch37_500kb.wig +++ /dev/null @@ -1 +0,0 @@ -map_hg19_500kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/map_grch37_50kb.wig b/modules/ichorcna/1.0/src/inst/extdata/map_grch37_50kb.wig deleted file mode 120000 index f8f28af0..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/map_grch37_50kb.wig +++ /dev/null @@ -1 +0,0 @@ -map_hg19_50kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/map_grch38_1000kb.wig b/modules/ichorcna/1.0/src/inst/extdata/map_grch38_1000kb.wig deleted file mode 120000 index 1a80568e..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/map_grch38_1000kb.wig +++ /dev/null @@ -1 +0,0 @@ -map_hg38_1000kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/map_grch38_10kb.wig b/modules/ichorcna/1.0/src/inst/extdata/map_grch38_10kb.wig deleted file mode 120000 index d1fb4df4..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/map_grch38_10kb.wig +++ /dev/null @@ -1 +0,0 @@ -map_hg38_10kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/map_grch38_500kb.wig b/modules/ichorcna/1.0/src/inst/extdata/map_grch38_500kb.wig deleted file mode 120000 index d373ee72..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/map_grch38_500kb.wig +++ /dev/null @@ -1 +0,0 @@ -map_hg38_500kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/map_grch38_50kb.wig b/modules/ichorcna/1.0/src/inst/extdata/map_grch38_50kb.wig deleted file mode 120000 index 8caf2b0b..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/map_grch38_50kb.wig +++ /dev/null @@ -1 +0,0 @@ -map_hg38_50kb.wig \ No newline at end of file diff --git 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/dev/null @@ -1 +0,0 @@ -map_hg19_500kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/inst/extdata/map_hs37d5_50kb.wig b/modules/ichorcna/1.0/src/inst/extdata/map_hs37d5_50kb.wig deleted file mode 120000 index f8f28af0..00000000 --- a/modules/ichorcna/1.0/src/inst/extdata/map_hs37d5_50kb.wig +++ /dev/null @@ -1 +0,0 @@ -map_hg19_50kb.wig \ No newline at end of file diff --git a/modules/ichorcna/1.0/src/readCounter b/modules/ichorcna/1.0/src/readCounter deleted file mode 100755 index aadf8d88..00000000 Binary files a/modules/ichorcna/1.0/src/readCounter and /dev/null differ