bio-tumor-fraction-estimation

ichorCNA Overview

ichorCNA (GavinHaLab fork, v0.5.1+) detects copy number alterations and estimates tumor fraction from sWGS (0.1-1x coverage).

Sensitivity: 97-100% detection at >= 3% tumor fraction (2024 validation)

Input Requirements

Requirement	Specification
Data type	sWGS (NOT targeted panel)
Coverage	0.1-1x (0.5x recommended)
Input	BAM files
Output	Tumor fraction, ploidy, CNA segments

Batch Processing

Goal: Run ichorCNA tumor fraction estimation on a cohort of sWGS samples in parallel, collecting results and handling failures gracefully.

Approach: Apply the ichorCNA pipeline to each sample's WIG file using mclapply for parallelization, wrapping each call in tryCatch to report per-sample success or failure.

library(ichorCNA)
library(parallel)

process_sample <- function(wig_file, params) {
    sample_id <- basename(wig_file)
    sample_id <- gsub('.wig$', '', sample_id)

    tryCatch({
        runIchorCNA(
            WIG = wig_file,
            gcWig = params$gcWig,
            mapWig = params$mapWig,
            normalPanel = params$normalPanel,
            centromere = params$centromere,
            outDir = params$outDir,
            id = sample_id,
            normal = c(0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99),
            ploidy = c(2, 3),
            maxCN = 5
        )
        return(list(sample = sample_id, status = 'success'))
    }, error = function(e) {
        return(list(sample = sample_id, status = 'failed', error = e$message))
    })
}