bio-tumor-fraction-estimation

FreedomIntelligence/OpenClaw-Medical-SkillsBiology Medicine and BioinformaticsAudit pendingSnapshot a53d3da5894c

Summary

This source did not publish a separate summary. Review SKILL.md before using the skill.

SKILL.md

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 = 5,

    # Subclonality
    estimateScPrevalence = TRUE,
    scStates = c(1, 3),

    # Segmentation
    txnE = 0.9999,
    txnStrength = 10000,

    # Chromosomes
    chrs = paste0('chr', c(1:22, 'X'))
)

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))
    })
}

# Run in parallel
wig_files <- list.files('wig/', pattern = '.wig$', full.names = TRUE)
params <- list(
    gcWig = 'gc_hg38_1mb.wig',
    mapWig = 'mappability_hg38_1mb.wig',
    normalPanel = 'pon_median_1mb.rds',
    centromere = 'centromeres_hg38.txt',
    outDir = 'ichor_results/'
)

results <- mclapply(wig_files, process_sample, params = params, mc.cores = 4)

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 = 1 - params$n[1],  # n is normal fraction
            ploidy = params$phi[1],
            log_likelihood = params$loglik[1]
        ))
    }

    return(results)
}

# Parse all results
tf_results <- parse_ichor_results('ichor_results/')
print(tf_results)

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]
    }

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

bio-tumor-fraction-estimation

Summary

SKILL.md

Version Compatibility

Tumor Fraction Estimation

ichorCNA Overview

Input Requirements

Running ichorCNA

Batch Processing

Parsing Results

Python Wrapper

Interpretation

Related Skills

Related skills