Upload tasks/somatic-variant-calling/scripts/compile_report.py with huggingface_hub

tasks/somatic-variant-calling/scripts/compile_report.py

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+#!/usr/bin/env python3
+"""Compile somatic variant calling report from pipeline outputs."""
+import csv, os, json, subprocess
+
+OUTDIR = "outputs"
+
+def count_vcf_variants(path):
+    """Count variants in a VCF file (excluding header lines)."""
+    if not os.path.exists(path):
+        return 0
+    count = 0
+    import gzip
+    opener = gzip.open if path.endswith('.gz') else open
+    try:
+        with opener(path, 'rt') as f:
+            for line in f:
+                if not line.startswith('#'):
+                    count += 1
+    except Exception:
+        return 0
+    return count
+
+def count_pass_variants(path):
+    """Count PASS variants in a VCF."""
+    if not os.path.exists(path):
+        return 0
+    count = 0
+    import gzip
+    opener = gzip.open if path.endswith('.gz') else open
+    try:
+        with opener(path, 'rt') as f:
+            for line in f:
+                if not line.startswith('#'):
+                    fields = line.split('\t')
+                    if len(fields) >= 7 and (fields[6] == 'PASS' or fields[6] == '.'):
+                        count += 1
+    except Exception:
+        return 0
+    return count
+
+def parse_mosdepth_summary(path):
+    """Parse mosdepth summary file."""
+    if not os.path.exists(path):
+        return {'mean': 0, 'min': 0, 'max': 0}
+    with open(path) as f:
+        reader = csv.DictReader(f, delimiter='\t')
+        for row in reader:
+            if row.get('chrom') == 'total' or row.get('chrom', '').startswith('total'):
+                return {
+                    'mean': float(row.get('mean', 0)),
+                    'min': float(row.get('min', 0)),
+                    'max': float(row.get('max', 0))
+                }
+    return {'mean': 0, 'min': 0, 'max': 0}
+
+def parse_fastp_json(path):
+    """Parse fastp JSON report."""
+    if not os.path.exists(path):
+        return {'before': 0, 'after': 0}
+    with open(path) as f:
+        j = json.load(f)
+    return {
+        'before': j['summary']['before_filtering']['total_reads'],
+        'after': j['summary']['after_filtering']['total_reads']
+    }
+
+def parse_markdup_metrics(path):
+    """Parse GATK MarkDuplicates metrics."""
+    if not os.path.exists(path):
+        return {'dup_pct': 0}
+    with open(path) as f:
+        in_metrics = False
+        headers = []
+        for line in f:
+            if line.startswith('## METRICS CLASS'):
+                in_metrics = True
+                continue
+            if in_metrics and not headers:
+                headers = line.strip().split('\t')
+                continue
+            if in_metrics and headers:
+                vals = line.strip().split('\t')
+                if len(vals) >= len(headers):
+                    d = dict(zip(headers, vals))
+                    return {'dup_pct': float(d.get('PERCENT_DUPLICATION', 0))}
+                break
+    return {'dup_pct': 0}
+
+# Gather results
+tumor_fastp = parse_fastp_json(f"{OUTDIR}/fastp/tumor_fastp.json")
+normal_fastp = parse_fastp_json(f"{OUTDIR}/fastp/normal_fastp.json")
+
+tumor_dup = parse_markdup_metrics(f"{OUTDIR}/markdup/tumor.metrics.txt")
+normal_dup = parse_markdup_metrics(f"{OUTDIR}/markdup/normal.metrics.txt")
+
+tumor_cov = parse_mosdepth_summary(f"{OUTDIR}/coverage/tumor.mosdepth.summary.txt")
+normal_cov = parse_mosdepth_summary(f"{OUTDIR}/coverage/normal.mosdepth.summary.txt")
+
+# Variant counts per caller
+caller1_raw = count_vcf_variants(f"{OUTDIR}/mutect2/somatic_raw.vcf.gz")
+caller1_pass = count_pass_variants(f"{OUTDIR}/mutect2/somatic_filtered.vcf.gz")
+caller2_raw = count_vcf_variants(f"{OUTDIR}/freebayes/joint_raw.vcf")
+caller3_raw = count_vcf_variants(f"{OUTDIR}/bcftools_call/pileup_raw.vcf.gz")
+
+# Annotated counts
+ann1 = count_vcf_variants(f"{OUTDIR}/annotate/mutect2_annotated.vcf.gz")
+ann2 = count_vcf_variants(f"{OUTDIR}/annotate/freebayes_annotated.vcf.gz")
+ann3 = count_vcf_variants(f"{OUTDIR}/annotate/bcftools_annotated.vcf.gz")
+
+# Contamination
+contamination = 0.0
+contam_file = f"{OUTDIR}/mutect2/contamination.table"
+if os.path.exists(contam_file):
+    with open(contam_file) as f:
+        for line in f:
+            if not line.startswith('sample'):
+                parts = line.strip().split('\t')
+                if len(parts) >= 2:
+                    contamination = float(parts[1])
+
+report = [
+    ('tumor_input_reads', tumor_fastp['before']),
+    ('tumor_qc_reads', tumor_fastp['after']),
+    ('normal_input_reads', normal_fastp['before']),
+    ('normal_qc_reads', normal_fastp['after']),
+    ('tumor_duplication_rate', round(tumor_dup['dup_pct'], 4)),
+    ('normal_duplication_rate', round(normal_dup['dup_pct'], 4)),
+    ('tumor_mean_coverage', round(tumor_cov['mean'], 2)),
+    ('normal_mean_coverage', round(normal_cov['mean'], 2)),
+    ('caller1_raw_variants', caller1_raw),
+    ('caller1_pass_variants', caller1_pass),
+    ('caller2_raw_variants', caller2_raw),
+    ('caller3_raw_variants', caller3_raw),
+    ('callers_used', 3),
+    ('annotated_variants_caller1', ann1),
+    ('annotated_variants_caller2', ann2),
+    ('annotated_variants_caller3', ann3),
+    ('estimated_contamination', round(contamination, 4)),
+]
+
+with open("results/report.csv", 'w') as f:
+    writer = csv.writer(f)
+    writer.writerow(['metric', 'value'])
+    writer.writerows(report)
+
+print("=== Final Report ===")
+for m, v in report:
+    print(f"  {m} = {v}")