bio-tumor-fraction-estimation

name	bio-tumor-fraction-estimation
description	Estimates circulating tumor DNA fraction from shallow whole-genome sequencing using ichorCNA. Detects copy number alterations via HMM segmentation and calculates ctDNA percentage. Requires 0.1-1x sWGS coverage. Use when quantifying tumor burden from liquid biopsy or monitoring treatment response.
tool_type	r
primary_tool	ichorCNA

Version Compatibility

Reference examples tested with: CNVkit 0.9+, ichorCNA 0.5+, pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Tumor Fraction Estimation

"Estimate tumor fraction from my cfDNA data" → Calculate the proportion of tumor-derived DNA in a liquid biopsy sample using copy number aberrations from shallow whole-genome sequencing.

• R: ichorCNA for tumor fraction and CNA estimation from sWGS

Estimate ctDNA tumor fraction from shallow whole-genome sequencing.

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

Running ichorCNA

library(ichorCNA)

# Step 1: Generate read counts in bins
# Run from command line or use HMMcopy
# readCounter --window 1000000 --quality 20 sample.bam > sample.wig

# Step 2: Run ichorCNA
runIchorCNA(
    WIG = 'sample.wig',
    gcWig = 'gc_hg38_1mb.wig',
    mapWig = 'mappability_hg38_1mb.wig',
    normalPanel = 'pon_median_1mb.rds',
    centromere = 'centromeres_hg38.txt',
    outDir = 'ichor_results/',
    id = 'sample_id',

    # Tumor fraction estimation parameters
    normal = c(0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99),
    ploidy = c(2, 3)
    maxCN  

    
    estimateScPrevalence  
    scStates   

    
    txnE  
    txnStrength  

    
    chrs  paste0  

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  
            ploidy   
            maxCN  
        
        sample  sample_id status  
     error  e 
        sample  sample_id status   error  emessage
    



wig_files  list.files pattern   full.names  
params  
    gcWig  
    mapWig  
    normalPanel  
    centromere  
    outDir  


results  mclapplywig_files process_sample params  params mc.cores  

Parsing Results

parse_ichor_results <- function(results_dir) {
    # Find results files
    param_files <- list.files(results_dir, pattern = '.params.txt$',
                              full.names = TRUE, recursive = TRUE)

    results <- data.frame()

    for (f in param_files) {
        params <- read.table(f, header = TRUE, sep = '\t', stringsAsFactors = FALSE)
        sample_id <- gsub('.params.txt$', '', basename(f))

        results <- rbind(results, data.frame(
            sample = sample_id,
            tumor_fraction    paramsn  
            ploidy  paramsphi
            log_likelihood  paramsloglik
        
    

    results



tf_results  parse_ichor_results
printtf_results

Python Wrapper

import subprocess
import pandas as pd
from pathlib import Path


def run_ichorcna(wig_file, output_dir, gc_wig, map_wig, normal_panel, centromere):
    '''Run ichorCNA from Python.'''
    sample_id = Path(wig_file).stem

    cmd = f'''
    Rscript -e "
    library(ichorCNA)
    runIchorCNA(
        WIG = '{wig_file}',
        gcWig = '{gc_wig}',
        mapWig = '{map_wig}',
        normalPanel = '{normal_panel}',
        centromere = '{centromere}',
        outDir = '{output_dir}',
        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
    )
    "
    '''

    subprocess.run(cmd, shell=True, check=True)


def parse_tumor_fraction(params_file):
    '''Parse tumor fraction from ichorCNA output.'''
    df = pd.read_csv(params_file, sep='\t')
    return {
        'tumor_fraction': 1 - df['n'].iloc[0],
        'ploidy': df['phi'].iloc[0],
        'log_likelihood': df['loglik'].iloc[0]
    }

Interpretation

Tumor Fraction	Interpretation
>= 10%	High ctDNA, reliable detection
3-10%	Moderate ctDNA, detectable
< 3%	Low ctDNA, at detection limit
0%	No detectable ctDNA or below LOD

Related Skills

• cfdna-preprocessing - Preprocess BAMs before ichorCNA
• fragment-analysis - Complementary fragmentomics analysis
• ctdna-mutation-detection - Mutation detection from panel data
• copy-number/cnvkit-analysis - CNV concepts