tools-only Version Compatibility
Reference examples tested with: Bismark 0.24+, numpy 1.26+, pandas 2.2+, pysam 0.22+, scipy 1.12+, statsmodels 0.14+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - CLI:
<tool> --versionthen<tool> --helpto confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Methylation-Based Detection
"Detect cancer from cfDNA methylation patterns" → Analyze cell-free DNA methylation for multi-cancer early detection and tissue-of-origin classification using bisulfite or enzymatic conversion.
- CLI:
MethylDackel extractfor methylation calling from cfDNA bisulfite data
Analyze cfDNA methylation for cancer detection and tissue-of-origin analysis.
Methods Overview
| Method | Description | cfDNA Input |
|---|---|---|
| cfMeDIP-seq | Enrichment-based, good for low input | >= 5 ng |
| Bisulfite-seq | Single-base resolution | >= 10 ng |
| EM-seq | Enzymatic, less degradation | >= 10 ng |
MethylDackel Pipeline
MethylDackel is actively maintained and integrated with nf-core/methylseq.
# Extract methylation from bisulfite BAM
MethylDackel extract \
reference.fa \
sample_bismark.bam \
--CHG \
--CHH \
-o sample_methylation
# Output: sample_methylation_CpG.bedGraph, etc.
# Merge C and G strand calls
MethylDackel mergeContext \
reference.fa \
sample_methylation
Python Implementation
import subprocess
import pandas as pd
import numpy as np
def extract_methylation(bam_file, reference, output_prefix, min_depth=5):
'''
Extract methylation from bisulfite-seq BAM using MethylDackel.
'''
subprocess.run([
'MethylDackel', 'extract',
reference,
bam_file,
'-o', output_prefix,
'--minDepth', str(min_depth),
'--mergeContext'
], check=True)
# Parse output
bedgraph = f'{output_prefix}_CpG.bedGraph'
meth = pd.read_csv(bedgraph, sep='\t', header=None,
names=['chrom', 'start', 'end', 'meth_pct', 'meth', 'unmeth'])
return meth
def calculate_methylation_beta(meth_df):
'''Calculate beta values (0-1 scale).'''
meth_df['beta'] = meth_df['meth'] / (meth_df['meth'] + meth_df['unmeth'])
return meth_df
DMR Detection
def find_differentially_methylated_regions(cancer_samples, normal_samples, min_diff=0.2):
'''
Find differentially methylated regions between cancer and normal.
Args:
cancer_samples: List of methylation DataFrames
normal_samples: List of methylation DataFrames
min_diff: Minimum beta difference
'''
from scipy import stats
# Merge samples
cancer_betas = pd.concat([s['beta'] for s in cancer_samples], axis=1)
normal_betas = pd.concat([s['beta'] for s in normal_samples], axis=1)
results = []
for idx in cancer_betas.index:
c_vals = cancer_betas.loc[idx].dropna()
n_vals = normal_betas.loc[idx].dropna()
if len(c_vals) < 3 or len(n_vals) < 3:
continue
diff = c_vals.mean() - n_vals.mean()
stat, pval = stats.mannwhitneyu(c_vals, n_vals, alternative='two-sided')
if abs(diff) >= min_diff:
results.append({
'region': idx,
'cancer_mean': c_vals.mean(),
'normal_mean': n_vals.mean(),
'diff': diff,
'pvalue': pval
})
results_df = pd.DataFrame(results)
# FDR correction
from statsmodels.stats.multitest import multipletests
if len(results_df) > 0:
_, results_df['fdr'], _, _ = multipletests(results_df['pvalue'], method='fdr_bh')
return results_df.sort_values('fdr')
Tissue Deconvolution
Goal: Estimate the tissue-of-origin composition of cfDNA by decomposing its methylation profile against a reference atlas of tissue-specific methylomes.
Approach: Align sample beta values to reference atlas regions, then solve for non-negative tissue proportions using constrained least squares (NNLS) and normalize to sum to one.
def tissue_deconvolution(sample_meth, reference_atlas):
'''
Deconvolve tissue composition from cfDNA methylation.
Args:
sample_meth: Sample methylation DataFrame
reference_atlas: Reference methylomes per tissue type
'''
from scipy.optimize import nnls
# Align samples to reference regions
common_regions = sample_meth.index.intersection(reference_atlas.index)
sample_vec = sample_meth.loc[common_regions, 'beta'].values
ref_matrix = reference_atlas.loc[common_regions].values
# Non-negative least squares for proportions
proportions, residual = nnls(ref_matrix, sample_vec)
# Normalize to sum to 1
proportions = proportions / proportions.sum()
return dict(zip(reference_atlas.columns, proportions))
MCED Panel Analysis
def analyze_mced_regions(meth_df, mced_regions):
'''
Analyze multi-cancer early detection (MCED) regions.
Similar to Galleri-style analysis.
'''
results = {}
for cancer_type, regions in mced_regions.items():
region_betas = meth_df[meth_df['chrom'].isin(regions)]
results[cancer_type] = {
'mean_beta': region_betas['beta'].mean(),
'hypermethylated_frac': (region_betas['beta'] > 0.8).mean(),
'hypomethylated_frac': (region_betas['beta'] < 0.2).mean()
}
return results
cfMeDIP-seq Analysis
def analyze_cfmedip(bam_file, output_prefix, genome_bins):
'''
Analyze cfMeDIP-seq data for methylation enrichment.
'''
import pysam
bam = pysam.AlignmentFile(bam_file, 'rb')
bin_counts = {}
for chrom, start, end in genome_bins:
count = bam.count(chrom, start, end)
bin_counts[(chrom, start, end)] = count
bam.close()
# Normalize by total reads and bin size
total = sum(bin_counts.values())
for key in bin_counts:
bin_size = key[2] - key[1]
bin_counts[key] = (bin_counts[key] / total) * 1e6 / (bin_size / 1000) # RPM per kb
return bin_counts
Related Skills
- cfdna-preprocessing - Preprocess before methylation analysis
- fragment-analysis - Complement with fragmentomics
- methylation-analysis/bismark-alignment - General methylation processing