R/someone_evoverse.R

In brucemoran/somenone: R functions required by the somenone NextFlow nf-core pipeline

# #' Parse per-sample VCF and CN inputs, creating a list for evoverse input
# #' evoverse is github.com/caravagna/mobster, cnaqc, viber, ctree etc.
# #' Format:
# #' chr, from, to, ref, alt, is_driver, driver_label, FILTER, DP, NV, VAF
# #' ref/alt are the bases, DP total depth and NV No. reads at Variant, VAF = NV/DP
# #' mut_rdata is master_consensus_shared from somatic_n-of-1 workflow
# #' For somatic_n-of-1 workflow, CN is based on FACETS inputs
# #' Format:
# #' chr, from, to, Major, minor (not sic)
# #' Purity also required as float
# #'
# #' @param mut_rdata is RData file with shared mutations in 2+ samples; colnames should be: "seqnames","start","end","width","strand","rowname","Consequence",          "IMPACT","SYMBOL","HGVSc","HGVSp","HGVSp1""CLIN_SIG","samples_n",          "sampleIDs",<sampleIDn.AD>,"sampleIDn.AD.1","sampleIDn.AF"
# #' @param cn_pattern is a pattern that matches FACETS input filenames
# #' @param pp_pattern is a pattern that matches polidy/purity table from FACETS
# #' @param which_genome is GRCh37 or GRCh38
# #' @return none, writes a tsv for input to Chimaera
# #' @export
#
# mut_cn_to_evoverse <- function(mut_rdata, cn_pattern, pp_pattern, which_genome, tag){
#
#   ##genome
#   if(which_genome %in% c("GRCh37", "hg19")){
#     which_genome <- "hg19"
#   } else {
#     which_genome <- "GRCh38"
#   }
#
#   ##make tibble of Mutation format
#   load(mut_rdata)
#   mut_df <- as.data.frame(gr_master_consensus_all[[2]])
#   mut_df$ref <- unlist(apply(mut_df, 1, function(f){
#     strsplit(f["rowname"], "[_/]")[[1]][2]
#   }))
#   mut_df$alt <- unlist(apply(mut_df, 1, function(f){
#     strsplit(f["rowname"], "[_/]")[[1]][3]
#   }))
#
#   ##add driver status for alll HIGH, MODERATE impacts
#   mut_df$is_driver <- FALSE
#   mut_df$is_driver[mut_df$IMPACT %in% c("HIGH", "MODERATE")] <- TRUE
#   mut_df$driver_label <- FALSE
#   mut_df$driver_label[mut_df$is_driver] <- paste0(mut_df$SYMBOL[mut_df$is_driver], ":", mut_df$HGVSp1[mut_df$is_driver])
#   colnames(mut_df)[c(1,2,3)] <- c("chr", "from", "to")
#
#   ##make df list of CN with Mutations
#   sampleIDs <- sort(unique(unlist(strsplit(mut_df$sampleIDs, "\\,"))))
#
#   ##parse out each sample and make correct format
#   mut_df_list <- parse_sample_mut_list(sampleIDs, mut_df)
#
#   ##parse out CN files and makke correct format
#   cn_df_list <- parse_sample_cn_list(cn_pattern)
#
#   purity_list <- parse_pur_list(pp_pattern)
#
#   lapply(sampleIDs, function(s){
#     readr::write_tsv(mut_df_list[[s]], file = paste0(s, ".mut.evoverse.tsv"))
#     readr::write_tsv(cn_df_list[[s]], file = paste0(s, ".cna.evoverse.tsv"))
#     data <- list(mut_df_list[[s]], cn_df_list[[s]], as.numeric(purity_list[s]), which_genome)
#     names(data) <- c("mut", "cna", "purity", "reference")
#     assigned_name <- paste0(s, "_evoverse_data")
#     assign(assigned_name, value = data)
#     save_file <- paste0(s, "_evoverse_data.RData")
#     save(list = assigned_name, file = save_file)
#   })
# }
#
# # run_multivar_mobster_viber_ctree <- function(evo_pattern){
# #   evov_files <- dir(pattern = evo_pattern)
# #   viber_list <- lapply(evov_files, function(f){
# #       load(f)
# #       ff <- gsub(".RData", "", f)
# #       print(paste0("Working on: ", ff))
# #       ds <- get(ff)
# #       dataset_muts <- ds[[1]]
# #       run_mobster(dataset_muts)
# #     })
# # }
# #' Parse mut_df and output as list per sample
# #'
# #' @param sampleIDs is vector of sample IDs to parse out depth data
# #' @param mut_df is data frame of data with samples <sampleID[1].AD, .AD.1, .AF
# #' @param vaf_filter is level of VAF to filter, default 5%
# #' @return list of correct format per sample
# #' @export
#
# parse_sample_mut_list <- function(sampleIDs, mut_df, vaf_filter = 0.05){
#
#   ##put muts in a list
#   mut_list <- lapply(sampleIDs, function(s){
#
#     ##create FILTER, DP, NV, VAF for sampleIDs[s]
#     mut_sampleID <- dplyr::select(.data = mut_df, dplyr::starts_with(!!s))
#     VAF <- dplyr::select(.data = mut_df, dplyr::starts_with(paste0(!!s, ".AF")))
#     ADS <- dplyr::select(.data = mut_df, dplyr::starts_with(paste0(!!s, ".AD")))
#     ADS[is.na(ADS)] <- 0
#     ADS$DP <- as.numeric(ADS[,1]) + as.numeric(ADS[,2])
#     mut_df$DP <- ADS$DP
#     mut_df$NV <- ADS[,2]
#     mut_df$VAF <- as.numeric(mut_df$NV) / as.numeric(mut_df$DP)
#     mut_df$VAF[is.na(mut_df$VAF)] <- 0
#     mut_df$FILTER <- mut_df$VAF > vaf_filter
#     wanted <- c("chr", "from", "to", "ref", "alt", "is_driver", "driver_label", "FILTER", "DP", "NV", "VAF")
#     muts <- dplyr::select(.data = mut_df, !!wanted)
#     return(muts)
#   })
#   names(mut_list) <- sampleIDs
#   return(mut_list)
# }
#
# #' Parse CN files and output as list
# #'
# #' @param cn_pattern is a pattern that matches CN input filenames
# #' @return list of correct format per sample CNs
# #'         chr, from, to, Major, minor (not sic)
# #' @export
#
# parse_sample_cn_list <- function(cn_pattern){
#
#   ##files are in current working dir
#   cn_files <- dir(pattern = cn_pattern)
#
#   ##get sampleIDs as first element of split on dot
#   cn_names <- unlist(lapply(cn_files, function(f){
#     strsplit(f, "\\.")[[1]][1]
#   }))
#
#   ##put cns in a list
#   cn_list <- lapply(cn_files, function(f){
#
#     ##read CN data, make GRanges
#     r1 <- readr::read_tsv(f)
#     sample_id <- strsplit(f, "\\.")[[1]][1]
#
#     print(paste0("Working on: ", sample_id))
#
#     wanted <- c("chrom", "start", "end", "tcn.em", "lcn.em")
#     cns <- r1[,wanted]
#     colnames(cns) <- c("chr", "from", "to", "Major", "minor")
#
#     return(cns)
#   })
#   names(cn_list) <- cn_names
#   return(cn_list)
# }
#
# #' Parse purity files and output as list
# #'
# #' @param pp_pattern is a pattern that matches polidy/purity table from FACETS
# #' @return list of correct format per sample
# #' @export
#
# parse_pur_list <- function(pp_patern){
#
#   ##files are in current working dir
#   cn_files <- dir(pattern = cn_pattern)
#
#   ##get sampleIDs as first element of split on dot
#   cn_names <- unlist(lapply(cn_files, function(f){
#     strsplit(f, "\\.")[[1]][1]
#   }))
#
#   ##files are in current working dir
#   pp_files <- dir(pattern = pp_pattern)
#
#   ##get sampleIDs as first element of split on dot
#   pp_names <- unlist(lapply(pp_files, function(f){
#     strsplit(f, "\\.")[[1]][1]
#   }))
#   names(pp_files) <- pp_names
#
#   pur_list <- lapply(pp_names, function(s){
#     purity <- readr::read_tsv(pp_files[s])[[2]]
#   })
#   names(pur_list) <- pp_names
#   return(pur_list)
# }
#
# # #' Run CNAqc
# #
# # run_cnaqc <- function(dataset){
# #
# #   cnaqc_obj <- CNAqc::init(
# #                 snvs = dataset[[1]],
# #                 cna = dataset[[2]],
# #                 purity = dataset[[3]],
# #                 ref = dataset[[4]])
# #
# #   # Set7_mapped_calls <- CNAqc::map_calls(
# #   #               CNA_calls = dataset,
# #   #               mutation_calls = Set7_mutations,
# #   #               purities = Set7_purity,
# #   #               samples = Set7_samples)
# # }
# #
# # run_mobster <- function(dataset_muts){
# #   dataset_muts_f <- dplyr::filter(.data = dataset_muts, FILTER == TRUE)
# #   m_fit <- mobster::mobster_fit(dataset_muts_f)
# #   cm_best <- Clusters(m_fit$best)
# #   b_fit <- mobster::mobster_fit(cm_best)
# #   return(b_fit$best$data)
# # }