From 66bb67a7bf29aad4510a0913a2dbc88da0013be8 Mon Sep 17 00:00:00 2001 From: Candace Savonen Date: Wed, 15 Apr 2020 09:40:00 -0400 Subject: [PATCH] Reconfigure how SNV-callers uses BED files for TMB calculations (#671) * Copies of old stuff that I can take pieces from * BED ranges set up * It's mostly working! * Few tinier edits before testing * Change back to normal files * Get rid of old scripts. * Development remnant * Run styler on it all! * Change back the indexing change that apparently HGG was dependent on * Retire old analyses from CircleCI * Add back TCGA processing * Incorporate @jashapiro doc and tidy suggestions * Add TODOs to things we won't do just yet. * genome_size -> region_size * Write down 256GB to figures README * Drop Panel samples in calculate TMB * Use a better fix for the Panel problem * Refresh plot --- .circleci/config.yml | 42 ++-- .../run_caller_consensus_analysis-pbta.sh | 42 +--- .../run_caller_consensus_analysis-tcga.sh | 21 +- .../snv-callers/scripts/02-merge_callers.R | 1 - .../snv-callers/scripts/03-calculate_tmb.R | 224 +++++++++++------- analyses/snv-callers/util/split_mnv.R | 2 +- analyses/snv-callers/util/tmb_functions.R | 107 ++++----- analyses/tmb-compare-tcga/compare-tmb.Rmd | 4 +- analyses/tmb-compare-tcga/compare-tmb.nb.html | 130 ++++------ figures/README.md | 24 +- figures/pngs/fig2-mutational-landscapes.png | Bin 1510491 -> 1263900 bytes figures/scripts/fig2-mutational-landscape.R | 4 +- 12 files changed, 273 insertions(+), 328 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 9e41309fd5..efe29dcd19 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -192,35 +192,35 @@ jobs: name: RNA-Seq composition command: ./scripts/run_in_ci.sh Rscript -e "rmarkdown::render('analyses/rna-seq-composition/rna-seq-composition.Rmd', clean = TRUE)" - # - run: - # name: TCGA SNV Caller Analysis - # command: ./scripts/run_in_ci.sh bash analyses/snv-callers/run_caller_consensus_analysis-tcga.sh + - run: + name: TCGA SNV Caller Analysis + command: ./scripts/run_in_ci.sh bash analyses/snv-callers/run_caller_consensus_analysis-tcga.sh - run: name: SNV Caller Analysis command: OPENPBTA_VAF_CUTOFF=0.5 ./scripts/run_in_ci.sh bash analyses/snv-callers/run_caller_consensus_analysis-pbta.sh - # - run: - # name: Tumor mutation burden with TCGA - # command: ./scripts/run_in_ci.sh Rscript -e "rmarkdown::render('analyses/tmb-compare-tcga/compare-tmb.Rmd', clean = TRUE)" - - # - run: - # name: PBTA vs TCGA explore - # command: ./scripts/run_in_ci.sh Rscript -e "rmarkdown::render('analyses/snv-callers/lancet-wxs-tests/explore-tcga-pbta.Rmd', clean = TRUE)" + - run: + name: Tumor mutation burden with TCGA + command: ./scripts/run_in_ci.sh Rscript -e "rmarkdown::render('analyses/tmb-compare-tcga/compare-tmb.Rmd', clean = TRUE)" + + # This analysis was used to explore the TCGA PBTA data when the BED files used to calculate TCGA + # were incorrect https://github.com/AlexsLemonade/OpenPBTA-analysis/issues/568 + #- run: + # name: PBTA vs TCGA explore + # command: ./scripts/run_in_ci.sh Rscript -e "rmarkdown::render('analyses/snv-callers/lancet-wxs-tests/explore-tcga-pbta.Rmd', clean = TRUE)" - # Retired analysis - # - run: - # name: Lancet WXS vs WGS test - # command: ./scripts/run_in_ci.sh Rscript -e "rmarkdown::render('analyses/snv-callers/lancet-wxs-tests/lancet-paired-WXS-WGS.Rmd', clean = TRUE)" + # This analysis arose from 'PBTA vs TCGA explore' and was used to explore Lancet's ability to handle WXS data #- run: + # name: Lancet WXS vs WGS test + # command: ./scripts/run_in_ci.sh Rscript -e "rmarkdown::render('analyses/snv-callers/lancet-wxs-tests/lancet-paired-WXS-WGS.Rmd', clean = TRUE)" - # Retired analysis - # - run: - # name: Lancet padded vs unpadded test - # command: ./scripts/run_in_ci.sh Rscript -e "rmarkdown::render('analyses/snv-callers/lancet-wxs-tests/lancet-padded-vs-unpadded.Rmd', clean = TRUE)" - + # This analysis arose from PBTA vs TCGA explore' and was used to explore Lancet's results with padded vs unpadded + #- run: + # name: Lancet padded vs unpadded test + # command: ./scripts/run_in_ci.sh Rscript -e "rmarkdown::render('analyses/snv-callers/lancet-wxs-tests/lancet-padded-vs-unpadded.Rmd', clean = TRUE)" - # This analysis was a side concept question and no longer needs to be run. - # - run: + # This analysis was a side concept question and no longer needs to be run. + # - run: # name: SNV Caller VAF Cutoff Experiment # command: ./scripts/run_in_ci.sh Rscript -e "rmarkdown::render('analyses/snv-callers/vaf_cutoff_experiment.Rmd', clean = TRUE)" diff --git a/analyses/snv-callers/run_caller_consensus_analysis-pbta.sh b/analyses/snv-callers/run_caller_consensus_analysis-pbta.sh index 4bd6f8c0bd..c5d40e6a07 100644 --- a/analyses/snv-callers/run_caller_consensus_analysis-pbta.sh +++ b/analyses/snv-callers/run_caller_consensus_analysis-pbta.sh @@ -16,10 +16,7 @@ consensus_file=analyses/snv-callers/results/consensus/pbta-snv-consensus-mutatio # BED and GTF file paths cds_file=scratch/gencode.v27.primary_assembly.annotation.bed -all_mut_wgs_bed=scratch/intersect_strelka_mutect_WGS.bed -all_mut_wxs_bed=data/WXS.hg38.100bp_padded.bed -coding_wgs_bed=scratch/intersect_cds_strelka_mutect_WGS.bed -coding_wxs_bed=scratch/intersect_cds_strelka_mutect_WXS.bed +wgs_bed=scratch/intersect_strelka_mutect_WGS.bed # Set a default for the VAF filter if none is specified vaf_cutoff=${OPENPBTA_VAF_CUTOFF:-0} @@ -54,7 +51,7 @@ python3 analyses/snv-callers/scripts/01-setup_db.py \ bedtools intersect \ -a data/WGS.hg38.strelka2.unpadded.bed \ -b data/WGS.hg38.mutect2.vardict.unpadded.bed \ - > $all_mut_wgs_bed + > $wgs_bed #################### Make coding regions file # Convert GTF to BED file for use in bedtools @@ -65,46 +62,13 @@ gunzip -c data/gencode.v27.primary_assembly.annotation.gtf.gz \ | sort -k 1,1 -k 2,2n \ | bedtools merge \ > $cds_file - -##################### Make WGS coding BED file -# Make WGS coding BED file for strelka -bedtools intersect \ - -a data/WGS.hg38.strelka2.unpadded.bed \ - -b $cds_file \ - > scratch/wgs_coding_strelka.bed - -# Make WGS coding BED file for mutect -bedtools intersect \ - -a data/WGS.hg38.mutect2.vardict.unpadded.bed \ - -b $cds_file \ - > scratch/wgs_coding_mutect.bed - -# Intersect the mutect and strelka coding beds into one -bedtools intersect \ - -a scratch/wgs_coding_mutect.bed \ - -b scratch/wgs_coding_strelka.bed \ - | sort -k 1,1 -k 2,2n \ - | bedtools merge \ - > $coding_wgs_bed - -##################### Make WXS coding BED file -# Intersect coding and WXS ranges, sort and merge -bedtools intersect \ - -a data/WXS.hg38.100bp_padded.bed \ - -b $cds_file \ - | sort -k 1,1 -k 2,2n \ - | bedtools merge \ - > $coding_wxs_bed ######################### Calculate consensus TMB ############################## Rscript analyses/snv-callers/scripts/03-calculate_tmb.R \ --db_file $dbfile \ --output analyses/snv-callers/results/consensus \ --metadata data/pbta-histologies.tsv \ - --all_bed_wgs $all_mut_wgs_bed \ - --all_bed_wxs $all_mut_wxs_bed \ - --coding_bed_wgs $coding_wgs_bed \ - --coding_bed_wxs $coding_wxs_bed \ + --coding_regions $cds_file \ --overwrite ########################## Compress consensus file ############################# diff --git a/analyses/snv-callers/run_caller_consensus_analysis-tcga.sh b/analyses/snv-callers/run_caller_consensus_analysis-tcga.sh index cbab0cb4dd..5365265b6c 100644 --- a/analyses/snv-callers/run_caller_consensus_analysis-tcga.sh +++ b/analyses/snv-callers/run_caller_consensus_analysis-tcga.sh @@ -17,13 +17,6 @@ consensus_file=analyses/snv-callers/results/consensus/tcga-snv-consensus-snv.maf # BED and GTF file paths cds_file=scratch/gencode.v27.primary_assembly.annotation.bed -# The WGS files are really just place holders and aren't used since the TCGA data is all WGS -all_mut_wgs_bed=analyses/snv-callers/ref_files/gencode.v19.basic.exome.hg38liftover.bed -coding_wgs_bed=analyses/snv-callers/ref_files/gencode.v19.basic.exome.hg38liftover.bed - -all_mut_wxs_bed=analyses/snv-callers/ref_files/gencode.v19.basic.exome.hg38liftover.bed -coding_wxs_bed=scratch/intersect_cds_gencode_liftover_WXS.bed - # Set a default for the VAF filter if none is specified vaf_cutoff=${OPENPBTA_VAF_CUTOFF:-0} @@ -61,24 +54,12 @@ gunzip -c data/gencode.v27.primary_assembly.annotation.gtf.gz \ | bedtools merge \ > $cds_file -# Make WXS coding BED file -# TODO: Update this BED when we get the target BEDs updated in v15/v16 -bedtools intersect \ - -a analyses/snv-callers/ref_files/gencode.v19.basic.exome.hg38liftover.bed \ - -b $cds_file \ - | sort -k 1,1 -k 2,2n \ - | bedtools merge \ - > $coding_wxs_bed - ######################### Calculate consensus TMB ############################## Rscript analyses/snv-callers/scripts/03-calculate_tmb.R \ --db_file $dbfile \ --output analyses/snv-callers/results/consensus \ --metadata data/pbta-tcga-manifest.tsv \ - --all_bed_wgs $all_mut_wgs_bed \ - --all_bed_wxs $all_mut_wxs_bed \ - --coding_bed_wgs $coding_wgs_bed \ - --coding_bed_wxs $coding_wxs_bed \ + --coding_regions $cds_file \ --overwrite \ --tcga diff --git a/analyses/snv-callers/scripts/02-merge_callers.R b/analyses/snv-callers/scripts/02-merge_callers.R index ce3537ed02..0b77777340 100644 --- a/analyses/snv-callers/scripts/02-merge_callers.R +++ b/analyses/snv-callers/scripts/02-merge_callers.R @@ -165,4 +165,3 @@ consensus_df %>% as.data.frame() %>% # Write to a TSV file, change NAs back to "." readr::write_tsv(opt$output_file, na = ".") - diff --git a/analyses/snv-callers/scripts/03-calculate_tmb.R b/analyses/snv-callers/scripts/03-calculate_tmb.R index 4744285b0a..c6949c2d7a 100644 --- a/analyses/snv-callers/scripts/03-calculate_tmb.R +++ b/analyses/snv-callers/scripts/03-calculate_tmb.R @@ -9,14 +9,8 @@ # --db_file : Path to sqlite database file made from 01-setup_db.py # --metadata : Relative file path to MAF file to be analyzed. Can be .gz compressed. # Assumes file path is given from top directory of 'OpenPBTA-analysis'. -# --all_bed_wgs : File path that specifies the BED regions file to be used for the -# denominator for all mutations TMB for WGS samples. -# --all_bed_wxs : File path that specifies the BED regions file to be used for the -# denominator for all mutations TMB for WXS samples. -# --coding_bed_wgs : File path that specifies the BED regions file to be used for the -# denominator for coding only TMB for WGS samples. -# --coding_bed_wxs : File path that specifies the BED regions file to be used for the -# denominator for coding only TMB for WXS samples. +# --coding_regions : File path that specifies the BED regions file that specifies +# coding regions that should be used for coding only TMB calculations. # --overwrite : If specified, will overwrite any files of the same name. Default is FALSE. # --tcga: If TRUE, will skip PBTA metadata specific steps # @@ -26,10 +20,7 @@ # --db_file scratch/testing_snv_db.sqlite \ # --output analyses/snv-callers/results/consensus \ # --metadata data/pbta-histologies.tsv \ -# --all_bed_wgs scratch/intersect_strelka_mutect_WGS.bed \ -# --all_bed_wxs data/WXS.hg38.100bp_padded.bed \ -# --coding_bed_wgs scratch/intersect_cds_lancet_strelka_mutect_WGS.bed \ -# --coding_bed_wxs scratch/intersect_cds_WXS.bed \ +# --coding_regions scratch/gencode.v27.primary_assembly.annotation.bed \ # --overwrite ################################ Initial Set Up ################################ @@ -67,27 +58,9 @@ option_list <- list( metavar = "character" ), make_option( - opt_str = "--all_bed_wgs", type = "character", default = "none", - help = "File path that specifies the BED regions file to be used for the - denominator for all mutations TMB for WGS samples.", - metavar = "character" - ), - make_option( - opt_str = "--all_bed_wxs", type = "character", default = "none", - help = "File path that specifies the BED regions file to be used for the - denominator for all mutations TMB for WXS samples.", - metavar = "character" - ), - make_option( - opt_str = "--coding_bed_wgs", type = "character", default = "none", - help = "File path that specifies the BED regions file to be used for the - denominator for coding only TMB for WXS samples. 'OpenPBTA-analysis'", - metavar = "character" - ), - make_option( - opt_str = "--coding_bed_wxs", type = "character", default = "none", - help = "File path that specifies the BED regions file to be used for the - denominator for coding only TMB for WXS samples. 'OpenPBTA-analysis'", + opt_str = "--coding_regions", type = "character", default = "none", + help = "File path that specifies the BED regions file that specifies what + coding regions should be used for coding only TMB.", metavar = "character" ), make_option( @@ -109,15 +82,11 @@ opt <- parse_args(OptionParser(option_list = option_list)) # Make everything relative to root path opt$metadata <- file.path(root_dir, opt$metadata) opt$db_file <- file.path(root_dir, opt$db_file) -opt$all_bed_wgs <- file.path(root_dir, opt$all_bed_wgs) -opt$all_bed_wxs <- file.path(root_dir, opt$all_bed_wxs) -opt$coding_bed_wgs <- file.path(root_dir, opt$coding_bed_wgs) -opt$coding_bed_wxs <- file.path(root_dir, opt$coding_bed_wxs) +opt$coding_regions <- file.path(root_dir, opt$coding_regions) ########### Check that the files we need are in the paths specified ############ needed_files <- c( - opt$metadata, opt$db_file, opt$all_bed_wgs, opt$all_bed_wxs, - opt$coding_bed_wgs, opt$coding_bed_wxs + opt$metadata, opt$db_file, opt$coding_regions ) # Get list of which files were found @@ -180,15 +149,16 @@ join_cols <- c( # Create the consensus for non-MNVs strelka_mutect_maf_df <- strelka %>% # We'll keep the Strelka2 columns and drop Mutect2 columns - dplyr::inner_join(mutect %>% - dplyr::select(join_cols), - by = join_cols, - copy = TRUE) %>% + dplyr::inner_join(mutect %>% + dplyr::select(join_cols), + by = join_cols, + copy = TRUE + ) %>% as.data.frame() # Get Multi-nucleotide calls from mutect as SNVs -split_mutect_df <- split_mnv(mutect) %>% - dplyr::select(join_cols) +split_mutect_df <- split_mnv(mutect) %>% + dplyr::select(join_cols) # join MNV calls with strelka strelka_mutect_mnv <- strelka %>% @@ -208,8 +178,8 @@ if (opt$tcga) { # Merge in the MNVs strelka_mutect_maf_df <- strelka_mutect_maf_df %>% dplyr::union(strelka_mutect_mnv, - by = join_cols - ) + by = join_cols + ) ########################### Set up metadata columns ############################ # Print progress message @@ -220,19 +190,33 @@ if (opt$tcga) { # Format two fields of metadata for use with functions metadata <- readr::read_tsv(opt$metadata) %>% dplyr::mutate( - experimental_strategy = "WXS", # This field doesn't exist for this data, but all is WXS - short_histology = Primary_diagnosis + short_histology = Primary_diagnosis, + target_bed_path = file.path(root_dir, "data", BED_In_Use), + experimental_strategy = "WXS" + ) %>% + dplyr::rename( + Tumor_Sample_Barcode = tumorID, + target_bed = BED_In_Use ) # This field is named differently # Manifest files only have first 12 letters of the barcode so we gotta chop the end off strelka_mutect_maf_df <- strelka_mutect_maf_df %>% dplyr::mutate(Tumor_Sample_Barcode = substr(Tumor_Sample_Barcode, 0, 12)) -} else { # pbta data +} else { # pbta data # Isolate metadata to only the samples that are in the datasets metadata <- readr::read_tsv(opt$metadata) %>% dplyr::filter(Kids_First_Biospecimen_ID %in% strelka_mutect_maf_df$Tumor_Sample_Barcode) %>% dplyr::distinct(Kids_First_Biospecimen_ID, .keep_all = TRUE) %>% - dplyr::rename(Tumor_Sample_Barcode = Kids_First_Biospecimen_ID) + dplyr::rename(Tumor_Sample_Barcode = Kids_First_Biospecimen_ID) %>% + # Make a Target BED regions column + dplyr::mutate( + target_bed = dplyr::recode(experimental_strategy, + "WGS" = "scratch/intersect_strelka_mutect_WGS.bed", + "WXS" = "data/WXS.hg38.100bp_padded.bed" + #TODO: make a padded/unpadded script option + ), + target_bed_path = file.path(root_dir, target_bed) + ) # Make sure that we have metadata for all these samples. if (!all(unique(strelka_mutect_maf_df$Tumor_Sample_Barcode) %in% metadata$Tumor_Sample_Barcode)) { @@ -242,63 +226,137 @@ if (opt$tcga) { # Add in metadata strelka_mutect_maf_df <- strelka_mutect_maf_df %>% dplyr::inner_join(metadata %>% - dplyr::select( - Tumor_Sample_Barcode, - experimental_strategy, - short_histology - ), - by = "Tumor_Sample_Barcode") + dplyr::select( + Tumor_Sample_Barcode, + experimental_strategy, + short_histology, + target_bed, + target_bed_path + ), + by = "Tumor_Sample_Barcode" + ) %>% + # Remove samples if they are labeled "Panel" + dplyr::filter(experimental_strategy != "Panel") + +############################# Set Up BED Files ################################# +# Make a data.frame of the unique BED file paths and their names +bed_files_key_df <- strelka_mutect_maf_df %>% + dplyr::select(Tumor_Sample_Barcode, target_bed, target_bed_path) %>% + dplyr::distinct() + +# Get the file paths for the bed files +bed_file_paths <- bed_files_key_df %>% + dplyr::distinct(target_bed, target_bed_path) %>% + tibble::deframe() + +# Read in each unique BED file and turn into GenomicRanges object +bed_ranges_list <- lapply(bed_file_paths, function(bed_file) { + + # Read in BED file as data.frame + bed_df <- readr::read_tsv(bed_file, + col_names = c("chr", "start", "end") + ) -############################# Coding TMB file ################################## + # Make into a GenomicRanges object + bed_ranges <- GenomicRanges::GRanges( + seqnames = bed_df$chr, + ranges = IRanges::IRanges( + start = bed_df$start, + end = bed_df$end + ) + ) + return(bed_ranges) +}) + +#################### Set up Coding Region version of BED ranges ################ +# Read in the coding regions BED file +coding_regions_df <- readr::read_tsv(opt$coding_regions, + col_names = c("chr", "start", "end") +) +# Make into a GenomicRanges object +coding_ranges <- GenomicRanges::GRanges( + seqnames = coding_regions_df$chr, + ranges = IRanges::IRanges( + start = coding_regions_df$start, + end = coding_regions_df$end + ) +) + +# For each BED range, find the coding regions intersection +coding_bed_ranges_list <- lapply(bed_ranges_list, function(bed_range, + coding_grange = coding_ranges) { + # Find the intersection + coding_intersect_ranges <- GenomicRanges::intersect(bed_range, coding_grange) + + # Return the reduce version of these ranges + return(GenomicRanges::reduce(coding_intersect_ranges)) +}) + +########################### All mutations TMB file ############################# # If the file exists or the overwrite option is not being used, run TMB calculations -if (file.exists(tmb_coding_file) && !opt$overwrite) { +if (file.exists(tmb_all_file) && !opt$overwrite) { # Stop if this file exists and overwrite is set to FALSE warning(cat( - "The 'coding only' Tumor Mutation Burden file already exists: \n", - tmb_coding_file, "\n", + "The 'all mutations' Tumor Mutation Burden file already exists: \n", + tmb_all_file, "\n", "Use --overwrite if you want to overwrite it." )) } else { # Print out warning if this file is going to be overwritten if (file.exists(tmb_coding_file)) { - warning("Overwriting existing 'coding only' TMB file.") + warning("Overwriting existing 'all mutations' TMB file.") } - # Print out progress message - message(paste("Calculating 'coding only' TMB...")) - - # Calculate coding only TMBs and write to file - tmb_coding_df <- calculate_tmb(strelka_mutect_maf_df, - bed_wgs = opt$coding_bed_wgs, - bed_wxs = opt$coding_bed_wxs + # Run TMB calculation on each tumor sample and its respective BED range + tmb_all_df <- purrr::map2_df( + bed_files_key_df$Tumor_Sample_Barcode, + bed_files_key_df$target_bed, + ~ calculate_tmb( + tumor_sample_barcode = .x, + maf_df = strelka_mutect_maf_df, + bed_ranges = bed_ranges_list[[.y]] + ) ) - readr::write_tsv(tmb_coding_df, tmb_coding_file) + + # Write to TSV file + readr::write_tsv(tmb_all_df, tmb_all_file) # Print out completion message - message(paste("TMB 'coding only' calculations saved to:", tmb_coding_file)) + message(paste("TMB 'all' calculations saved to:", tmb_all_file)) } - -########################### All mutations TMB file ############################# +############################# Coding TMB file ################################## # If the file exists or the overwrite option is not being used, run TMB calculations -if (file.exists(tmb_all_file) && !opt$overwrite) { +if (file.exists(tmb_coding_file) && !opt$overwrite) { # Stop if this file exists and overwrite is set to FALSE warning(cat( - "The 'all mutations' Tumor Mutation Burden file already exists: \n", - tmb_all_file, "\n", + "The 'coding only' Tumor Mutation Burden file already exists: \n", + tmb_coding_file, "\n", "Use --overwrite if you want to overwrite it." )) } else { # Print out warning if this file is going to be overwritten if (file.exists(tmb_coding_file)) { - warning("Overwriting existing 'all mutations' TMB file.") + warning("Overwriting existing 'coding only' TMB file.") } - # Calculate TMBs and write to TMB file - tmb_all_df <- calculate_tmb(strelka_mutect_maf_df, - bed_wgs = opt$all_bed_wgs, - bed_wxs = opt$all_bed_wxs + + # Print out progress message + message(paste("Calculating 'coding only' TMB...")) + + # Run coding TMB calculation on each tumor sample and its + # respective coding BED range + tmb_coding_df <- purrr::map2_df( + bed_files_key_df$Tumor_Sample_Barcode, + bed_files_key_df$target_bed, + ~ calculate_tmb( + tumor_sample_barcode = .x, + maf_df = strelka_mutect_maf_df, + bed_ranges = coding_bed_ranges_list[[.y]] + ) ) - readr::write_tsv(tmb_all_df, tmb_all_file) + + # Write to TSV file + readr::write_tsv(tmb_coding_df, tmb_coding_file) # Print out completion message - message(paste("TMB 'all' calculations saved to:", tmb_all_file)) + message(paste("TMB 'coding only' calculations saved to:", tmb_coding_file)) } diff --git a/analyses/snv-callers/util/split_mnv.R b/analyses/snv-callers/util/split_mnv.R index 26638ea824..f8249466ea 100644 --- a/analyses/snv-callers/util/split_mnv.R +++ b/analyses/snv-callers/util/split_mnv.R @@ -62,7 +62,7 @@ split_mnv <- function(mnv_tbl) { mnv_pos = dplyr::row_number(), Start_Position = Start_Position + mnv_pos - 1, End_Position = Start_Position - ) %>% + ) %>% dplyr::ungroup() return(mnv_df) } diff --git a/analyses/snv-callers/util/tmb_functions.R b/analyses/snv-callers/util/tmb_functions.R index 34c89715a3..771a38fda0 100644 --- a/analyses/snv-callers/util/tmb_functions.R +++ b/analyses/snv-callers/util/tmb_functions.R @@ -32,7 +32,9 @@ maf_to_granges <- function(maf_df) { ) } -snv_ranges_filter <- function(maf_df, keep_ranges = NULL, bp_window = 0) { +snv_ranges_filter <- function(maf_df, + keep_ranges = NULL, + bp_window = 0) { # Given a MAF formatted data.frame and a BED regions data.frame; filter out # any variants of the MAF df that are not within the BED regions. # @@ -52,7 +54,7 @@ snv_ranges_filter <- function(maf_df, keep_ranges = NULL, bp_window = 0) { # Turn the MAF sample mutations into a GRanges object maf_granges <- maf_to_granges(maf_df) - + # If ranges is given as a data.frame, convert if (is.data.frame(keep_ranges)) { # Turn the bed regions df into a GRanges object @@ -72,92 +74,75 @@ snv_ranges_filter <- function(maf_df, keep_ranges = NULL, bp_window = 0) { keep_ranges, maxgap = bp_window ) - + # Calculate of ratio of variants in this BED using the @from slot which - # indicates the indices of the ranges in `wxs_maf_ranges` that have overlaps - # with `wxs_bed_ranges` + # indicates the indices of the ranges in `maf_ranges` that have overlaps + # with `keep_ranges` ratio <- length(overlap@from) / nrow(maf_df) - + # What fraction of mutations are in these bed regions? cat( "Ratio of variants in this BED:", ratio, "\n", "Ratio of variants being filtered out:", 1 - ratio, "\n" ) - + # Only keep those in the BED regions that overlap the `wxs_bed_granges` filt_maf_df <- maf_df[unique(overlap@from), ] - + # Return this matrix with the WXS mutations filtered but WGS the same return(filt_maf_df) } -calculate_tmb <- function(maf_df, bed_wgs, bed_wxs) { - # Calculate Tumor Mutational Burden for each sample given their WGS or WXS - # sizes in bp. Based on the BED files provided, tthis function filters out SNV - # outside those ranges and uses the total size in bp of those BED ranges for th - # TMB denominator. +calculate_tmb <- function(tumor_sample_barcode = NULL, + maf_df, + bed_ranges) { + # Calculate Tumor Mutational Burden a given sample in `Tumor_Sample_Barcode` + # given a target region BED frame. This function uses `snv_ranges_filter` to + # filter out SNVs outside those target BED ranges and uses the total size in + # bp of those BED ranges for the TMB denominator. # - # TMB = # variants / size of the genome or exome surveyed + # TMB = # variants / size of the genome or exome surveyed / Mb # # Args: + # tumor_sample_barcode: A string corresponding to a sample + # id in the `Tumor_Sample_Barcode` MAF file in maf_df. # maf_df: maf data.frame that has been turned into a data.frame, has had # the experimental_stategy column added from the metadata (can be # done with the `set_up_maf` function) and has WXS mutations filtered # using `wxs_bed_filter` function (If the situation calls for it). - # bed_wgs: BED file path with genome ranges to be used for filtering and - # genome size denominator for WGS samples - # bed_wxs: BED file path genome ranges to be used for filtering and genome - # size denominator for WXS samples + # bed_ranges: A GenomicRanges made from the BED file of the target regions to use for TMB + # calculations. # # Returns: - # A sample-wise data.frame with Tumor Mutation Burden statistics calculated - # using the given WGS and WXS sizes. - - # Read in the BED files we need - bed_wgs <- readr::read_tsv(bed_wgs, col_names = FALSE) - bed_wxs <- readr::read_tsv(bed_wxs, col_names = FALSE) - + # A calculated total region size, and TMB for the given Tumor_Sample_Barcode, + # returned as a single row data.frame. `experimental_strategy`, `short_histology` + # columns are carried along. + # Sum up genome sizes - wgs_size <- sum(bed_wgs[, 3] - bed_wgs[, 2]) - wxs_size <- sum(bed_wxs[, 3] - bed_wxs[, 2]) - - # Don't want integers per se - wgs_size <- as.numeric(wgs_size) - wxs_size <- as.numeric(wxs_size) - - # For WXS samples, filter out mutations that are outside of these coding regions. - filt_wxs_maf_df <- snv_ranges_filter(dplyr::filter( - maf_df, - experimental_strategy == "WXS" - ), - keep_ranges = bed_wxs - ) - - # For WXS samples, filter out mutations that are outside of these coding regions. - filt_wgs_maf_df <- snv_ranges_filter(dplyr::filter( - maf_df, - experimental_strategy == "WGS" - ), - keep_ranges = bed_wgs - ) - - # Bind the filtered WXS sample rows back to the WGS samples - filt_maf_df <- dplyr::bind_rows(filt_wxs_maf_df, filt_wgs_maf_df) - + bed_size <- as.numeric(sum(bed_ranges@ranges@width)) + + # Filter to only the sample's mutations + sample_maf_df <- maf_df %>% + dplyr::filter(Tumor_Sample_Barcode == tumor_sample_barcode) + + # Filter out mutations that are outside of these coding regions. + filt_maf_df <- snv_ranges_filter(sample_maf_df, keep_ranges = bed_ranges) + # Make a genome size variable - tmb <- maf_df %>% - dplyr::mutate(genome_size = dplyr::recode(experimental_strategy, - "WGS" = wgs_size, - "WXS" = wxs_size - )) %>% + tmb <- sample_maf_df %>% dplyr::group_by( - Tumor_Sample_Barcode, experimental_strategy, genome_size, + #TODO: Make this column passing stuff more flexible with some tidyeval maybe + Tumor_Sample_Barcode = tumor_sample_barcode, + experimental_strategy, short_histology ) %>% # Count number of mutations for that sample - dplyr::summarize(mutation_count = dplyr::n()) %>% - # Calculate TMB - dplyr::mutate(tmb = mutation_count / (genome_size / 1000000)) - + dplyr::summarize( + mutation_count = dplyr::n(), + region_size = bed_size, + tmb = mutation_count / (region_size / 1000000) + ) + + return(tmb) } diff --git a/analyses/tmb-compare-tcga/compare-tmb.Rmd b/analyses/tmb-compare-tcga/compare-tmb.Rmd index fb645680d0..74e861f42a 100644 --- a/analyses/tmb-compare-tcga/compare-tmb.Rmd +++ b/analyses/tmb-compare-tcga/compare-tmb.Rmd @@ -71,7 +71,7 @@ tmb_pbta <- data.table::fread(file.path( "pbta-snv-mutation-tmb-coding.tsv" )) %>% # This variable is weird when binding but we don't need it for the plot so we'll just remove it. - dplyr::select(-genome_size) %>% + dplyr::select(-region_size) %>% dplyr::filter(experimental_strategy != "Panel") ``` @@ -85,7 +85,7 @@ tmb_tcga <- data.table::fread(file.path( "consensus", "tcga-snv-mutation-tmb-coding.tsv" )) %>% - dplyr::select(-genome_size) + dplyr::select(-region_size) ``` ## Plot the TMB as a CDF plot diff --git a/analyses/tmb-compare-tcga/compare-tmb.nb.html b/analyses/tmb-compare-tcga/compare-tmb.nb.html index 468aaae30d..fbff2a698e 100644 --- a/analyses/tmb-compare-tcga/compare-tmb.nb.html +++ b/analyses/tmb-compare-tcga/compare-tmb.nb.html @@ -2965,18 +2965,24 @@

Packages and functions

Declare names of input and output directories.

- -

rr tmb_cdf_plot <- function(tmb_df, plot_title, colour) { tmb_df %>% as.data.frame() %>% dplyr::mutate(short_histology = tools::toTitleCase(short_histology)) %>% # Only plot histologies groups with more than min_samples number of samples dplyr::group_by(short_histology, add = TRUE) %>% # Only keep groups with this amount of samples dplyr::filter(dplyr::n() > 5) %>% # Calculate histology group mean dplyr::mutate( hist_median = median(tmb), hist_rank = rank(tmb, ties.method = ) / dplyr::n(), sample_size = paste0(= , dplyr::n()) ) %>% dplyr::ungroup() %>% dplyr::mutate(short_histology = reorder(short_histology, hist_median)) %>% # Now we will plot these as cummulative distribution plots ggplot2::ggplot(ggplot2::aes( x = hist_rank, y = tmb )) + ggplot2::geom_point(color = colour) + # Add summary line for mean ggplot2::geom_segment( x = 0, xend = 1, color = , ggplot2::aes(y = hist_median, yend = hist_median) ) + # Separate by histology ggplot2::facet_wrap(~ short_histology + sample_size, nrow = 1, strip.position = ) + ggplot2::theme_classic() + ggplot2::xlab(\) + ggplot2::ylab(mutations per Mb) + # Transform to log10 make non-log y-axis labels ggplot2::scale_y_continuous(trans = 1p, breaks = c(0, 1, 3, 10, 30, 100, 10000, 30000), limits = c(0, 30000)) + ggplot2::scale_x_continuous(limits = c(-0.2, 1.2), breaks = c()) + # Making it pretty ggplot2::theme(legend.position = ) + ggplot2::theme( axis.text.x = ggplot2::element_blank(), axis.ticks.x = ggplot2::element_blank(), strip.placement = , strip.text = ggplot2::element_text(size = 10, angle = 90, hjust = 1), strip.background = ggplot2::element_rect(fill = NA, color = NA) ) + ggplot2::ggtitle(plot_title) }

-
+ +
data_dir <- file.path("..", "..", "data")
+scratch_dir <- file.path("..", "..", "scratch")
+results_dir <- "results"
+plots_dir <- "plots"

Create output directories for this analysis.

- -

rr # TODO: update if the this tmb consensus file gets updated in a future data release tmb_pbta <- data.table::fread(file.path(.., -callers, , , -snv-mutation-tmb-coding.tsv)) %>% # This variable is weird when binding but we don’t need it for the plot so we’ll just remove it. dplyr::select(-genome_size) %>% dplyr::filter(experimental_strategy != )

-
+ +
if (!dir.exists(results_dir)) {
+  dir.create(results_dir)
+}
+if (!dir.exists(plots_dir)) {
+  dir.create(plots_dir)
+}
@@ -2995,7 +3001,7 @@

Set up consensus data

Read in the consensus TMB file.

- + 
# TODO: update if the this tmb consensus file gets updated in a future data release
 tmb_pbta <- data.table::fread(file.path(
   "..",
@@ -3005,23 +3011,19 @@ 
Set up consensus data

   "pbta-snv-mutation-tmb-coding.tsv"
 )) %>%
   # This variable is weird when binding but we don't need it for the plot so we'll just remove it.
-  dplyr::select(-genome_size) %>%
+  dplyr::select(-region_size) %>%
   dplyr::filter(experimental_strategy != "Panel")
+ +
File '../snv-callers/results/consensus/pbta-snv-mutation-tmb-coding.tsv' has size 0. Returning a NULL data.table.Error in is_character(x) : object 'region_size' not found
+ - -
# TODO: update if this tmb consensus file gets updated in a future data release
-tmb_tcga <- data.table::fread(file.path(
-  "..",
-  "snv-callers",
-  "results",
-  "consensus",
-  "tcga-snv-mutation-tmb-coding.tsv"
-)) %>%
-  dplyr::select(-genome_size)
+ +

rr tcga_plot <- cdf_plot( df = tmb_tcga, plot_title = (Adult), num_col = , group_col = _histology, color = #630882, n_group = 5, x_lim = c(-1.2, 1.2), y_lim = c(0, 30000), x_lab = \, y_lab = Mutations per Mb, breaks = c() ) + ggplot2::theme( axis.title.y = ggplot2::element_blank(), axis.text.y = ggplot2::element_blank(), axis.ticks.y = ggplot2::element_blank(), strip.text.x = ggplot2::element_text(size = 9) )

+
@@ -3031,84 +3033,27 @@

Plot the TMB as a CDF plot

Plot each dataset as its own CDF plot.

- -
pbta_plot <- cdf_plot(
-  df = tmb_pbta,
-  plot_title = "PBTA",
-  num_col = "tmb",
-  group_col = "short_histology",
-  color = "#3BC8A2",
-  n_group = 5,
-  x_lim = c(-1.2, 1.2),
-  y_lim = c(0, 30000),
-  x_lab = "",
-  y_lab = "Coding Mutations per Mb", 
-  breaks = c(0, 3, 10, 30, 100, 300, 1000, 3000, 10000, 30000)
-) +
-  ggplot2::theme(
-    strip.text.x = ggplot2::element_text(size = 12)
-  )
+ +

rr # Put the plots together tmb_plot <- cowplot::plot_grid(pbta_plot, tcga_plot, align = axis = , rel_widths = c(2.5, 1), label_size = 12 )

+
- -
tcga_plot <- cdf_plot(
-  df = tmb_tcga,
-  plot_title = "TCGA (Adult)",
-  num_col = "tmb",
-  group_col = "short_histology",
-  color = "#630882",
-  n_group = 5,
-  x_lim = c(-1.2, 1.2),
-  y_lim = c(0, 30000),
-  x_lab = "",
-  y_lab = "Coding Mutations per Mb",
-  breaks = c()
-) +
-  ggplot2::theme(
-    axis.title.y = ggplot2::element_blank(),
-    axis.text.y = ggplot2::element_blank(),
-    axis.ticks.y = ggplot2::element_blank(),
-    strip.text.x = ggplot2::element_text(size = 9)
-  )
+ +

rr # Save the plot to a png cowplot::save_plot(file.path(plots_dir, -cdf-pbta-tcga.png), plot = tmb_plot, base_width = 35, base_height = 20, unit =
+)

+

Combine both TMB data.frames into one plot

- -
# Put the plots together
-tmb_plot <- cowplot::plot_grid(pbta_plot, tcga_plot,
-  align = "v",
-  axis = "left",
-  rel_widths = c(2.5, 1),
-  label_size = 12
-)
- - - -

Save this final plot to a png file.

- - - -
# Save the plot to a png
-cowplot::save_plot(file.path(plots_dir, "tmb-cdf-pbta-tcga.png"),
-  plot = tmb_plot, base_width = 35, base_height = 20, unit = "cm"
-)
- - - -

Print from png so rendering is smoother TMB Plot

- -
-

Session Info

- - - -
sessionInfo()
+ +

rr sessionInfo()

+
R version 3.6.0 (2019-04-26)
@@ -3204,9 +3149,22 @@ 
Session Info

 [131] askpass_1.1
+ +

Save this final plot to a png file.

+ + + + +

Print from png so rendering is smoother TMB Plot

+
+
+

Session Info

+ + +
-
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+
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diff --git a/figures/README.md b/figures/README.md index 31eb543b23..c453d2eb7a 100644 --- a/figures/README.md +++ b/figures/README.md @@ -1,12 +1,12 @@ ## Running the figure generation script -Steps to refresh all publication-ready figures. +Steps to refresh all publication-ready figures. All steps assume your current directory is the top of this repository. #### 1. Obtain the current dataset. See [these instructions](https://github.com/AlexsLemonade/OpenPBTA-analysis#how-to-obtain-openpbta-data) to obtain the current data release. -We recommend [using the download script](https://github.com/AlexsLemonade/OpenPBTA-analysis#data-access-via-download-script) to obtain data because this will automatically create symlinks in `data/` to the latest files. +We recommend [using the download script](https://github.com/AlexsLemonade/OpenPBTA-analysis#data-access-via-download-script) to obtain data because this will automatically create symlinks in `data/` to the latest files. #### 2. Set up an up-to-date project Docker container. @@ -22,7 +22,7 @@ docker run \ ``` You may choose to use [`docker exec`](https://docs.docker.com/engine/reference/commandline/exec/) to interact with the container from there or if you'd prefer the RStudio interface, you can navigate to `localhost:8787` and enter username `rstudio` and the password you set with the `run` command above. -#### 3. Run the bash script that generates the figures (`scripts/run-figures.sh`). +#### 3. Run the bash script that generates the figures (`scripts/run-figures.sh`). This script runs **_all_** the intermediate steps needed to generate figures starting with the original data files. @@ -41,18 +41,18 @@ However, we list information about the resources, intermediate steps, and [PBTA | Figure | Individual script | Notes on requirements | Linked analysis modules | PBTA data files consumed | |--------|--------|------------------|-------------------------|-----------------------------| | Figure 1 | [scripts/fig1-sample-distribution.R`](./scripts/fig1-sample-distribution.R) | No high RAM requirements | [`sample-distribution-analysis`](https://github.com/AlexsLemonade/OpenPBTA-analysis/tree/master/analyses/sample-distribution-analysis) | `pbta-histologies.tsv` | -| Figure 2 | [scripts/fig2-mutational-landscape.R`](./scripts/fig2-mutational-landscape.R) | ~128MB of RAM are needed due to the run_caller_consensus_analysis-pbta.sh handling of large MAF files|[`snv-callers`](https://github.com/AlexsLemonade/OpenPBTA-analysis/tree/master/analyses/snv-callers)
[`mutational-signatures`](https://github.com/AlexsLemonade/OpenPBTA-analysis/tree/master/analyses/mutational-signatures) | `pbta-snv-lancet.vep.maf.gz`
`pbta-snv-mutect2.vep.maf.gz`
`pbta-snv-strelka2.vep.maf.gz`
`pbta-snv-vardict.vep.maf.gz`
`tcga-snv-lancet.vep.maf.gz`
`tcga-snv-mutect2.vep.maf.gz`
`tcga-snv-strelka2.vep.maf.gz` | +| Figure 2 | [scripts/fig2-mutational-landscape.R`](./scripts/fig2-mutational-landscape.R) | 256GB of RAM are needed due to the run_caller_consensus_analysis-pbta.sh handling of large MAF files|[`snv-callers`](https://github.com/AlexsLemonade/OpenPBTA-analysis/tree/master/analyses/snv-callers)
[`mutational-signatures`](https://github.com/AlexsLemonade/OpenPBTA-analysis/tree/master/analyses/mutational-signatures) | `pbta-snv-lancet.vep.maf.gz`
`pbta-snv-mutect2.vep.maf.gz`
`pbta-snv-strelka2.vep.maf.gz`
`pbta-snv-vardict.vep.maf.gz`
`tcga-snv-lancet.vep.maf.gz`
`tcga-snv-mutect2.vep.maf.gz`
`tcga-snv-strelka2.vep.maf.gz` | ## Color Palette Usage This project has a set of unified color palettes. There are 6 sets of hex color keys to be used for all final figures, stored as 6 TSV files in the `figures/palettes` folder. `hex_codes` contains the colors to be passed to your plotting code and `color_names` contains short descriptors of each color (e.g. `gradient_1`, or `divergent_neutral`). -Each palette contains an `na_color` that is the same color in all palettes. +Each palette contains an `na_color` that is the same color in all palettes. This color should be used for all `NA` values. `na_color` is always the last value in the palette. If `na_color` is not needed or is supplied separately to a plotting function, you can use a `dplyr::filter(hex_code != "na_color")` to remove `na_color`. -Biospecimens without a `short_histology` designation are coded as `none` and assigned the `na_color` in `palettes/histology_color_palette.tsv`. +Biospecimens without a `short_histology` designation are coded as `none` and assigned the `na_color` in `palettes/histology_color_palette.tsv`. | Palette File Name | HEX color key | Color Notes | Variable application | |--------------|--------------------|-----------|----------------------| @@ -130,8 +130,8 @@ gradient_col_palette <- gradient_col_palette %>% **Step 2)** Make a color function. -In this example, we are building a `colorRamp2` function based on a regular interval between the minimum and maximum of our variable `df$variable` by using `seq`. -However, depending on your data's distribution a regular interval based palette might not represent your data well on the plot. +In this example, we are building a `colorRamp2` function based on a regular interval between the minimum and maximum of our variable `df$variable` by using `seq`. +However, depending on your data's distribution a regular interval based palette might not represent your data well on the plot. You can provide any numeric vector to color code a palette using `circlize::colorRamp2` as long as that numeric vector is the same length as the palette itself. ``` @@ -155,14 +155,14 @@ df <- df %>% # Some plotting packages want a color function ComplexHeatmap::heatmap(df, - col = col_fun, + col = col_fun, na_col = na_color ) ``` ### Updating color palettes -The color palette TSV files are created by running `scripts/color_palettes.R`, which can be called by `Rscript scripts/color_palettes.R`. -Hex codes for the palettes are hard-coded in this script. +The color palette TSV files are created by running `scripts/color_palettes.R`, which can be called by `Rscript scripts/color_palettes.R`. +Hex codes for the palettes are hard-coded in this script. 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