genomics-cnv-calling

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Load when calling CNV segments via CBS-style segmentation on a bin-level log2-ratio CSV from exome / WGS coverage — emits per-segment 5-class CN state (`amplification` / `gain` / `neutral` / `loss` / `deep_deletion`), per-chromosome summary, genome-fraction-altered. Skip when working with single-cell / spatial CNV (use `spatial-cnv`).

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Source SKILL.md: https://github.com/TianGzlab/OmicsClaw/blob/HEAD/skills/genomics/genomics-cnv-calling/SKILL.md

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genomics-cnv-calling

When to use

The user has a bin-level log2-ratio CSV (typically from CNVkit cnr files, GATK gCNV denoised copy ratios, or Control-FREEC ratio output) and wants to segment into discrete CNV calls. Each segment is classified into one of five copy-number states based on mean log2 ratio: amplification (> +1.0), gain (> +0.3), neutral, loss (< -0.3), deep_deletion (< -1.0). --alpha controls segmentation significance (default 0.01).

This skill does NOT generate the bin-level log2-ratio CSV — it consumes the output of CNVkit / GATK gCNV / Control-FREEC. For spatial / single-cell CNV use spatial-cnv.

Inputs & Outputs

Input	Format	Required
Bin-level log2 ratios	`.csv` with columns `chrom`, `start`, `end`, `log2_ratio`	yes (unless `--demo`)
Segmentation significance	`--alpha <float>` (default 0.01)	no

Output	Path	Notes
CNV segments	`tables/cnv_segments.csv`	per-segment columns: `chrom`, `start`, `end`, `n_bins`, `log2_ratio`, `cn_state` (5-class), `estimated_cn`
Per-chromosome	`tables/cnv_per_chromosome.csv`	n_segments + altered fraction per chromosome
Report	`report.md` + `result.json`	summary includes `n_amplifications`, `n_deep_deletions`, `n_gains`, `n_losses`, `genome_fraction_altered`

Flow

Load bin CSV (--input <bins.csv>) or generate a demo bin file at output_dir/demo_cnv_bins.csv (genomics_cnv_calling.py:229).
Read columns via pd.read_csv (genomics_cnv_calling.py:250); group by df["chrom"] (:254) and segment per chromosome.
Classify each segment via np.select (genomics_cnv_calling.py:161-162) into one of amplification / gain / neutral / loss / deep_deletion based on mean log2.
Aggregate per-chromosome counts + genome-fraction-altered (:281-291).
Write tables/cnv_segments.csv (genomics_cnv_calling.py:373) + tables/cnv_per_chromosome.csv (:382) + report.md + result.json (:385).

Gotchas

Required CSV column is chrom, NOT chromosome. Code reads df["chrom"] at genomics_cnv_calling.py:254. CNVkit cnr files have a chromosome column — rename to chrom first (pd.read_csv(...).rename(columns={"chromosome": "chrom"})). Other required columns are start, end, log2_ratio.
cn_state has 5 classes, NOT 3. genomics_cnv_calling.py:161-162 produces amplification (log2 > 1.0), gain (> 0.3), neutral, loss (< -0.3), deep_deletion (< -1.0). The summary reports n_gains and n_losses as inclusive of amplification / deep_deletion (:281-282); inspect n_amplifications / n_deep_deletions for the high-magnitude subset.
No bin generator is invoked. This skill consumes a bin-level log2-ratio CSV — it does NOT run CNVkit / GATK gCNV / Control-FREEC. Run them upstream and feed the bin file here.
--input REQUIRED unless --demo. genomics_cnv_calling.py:363 raises ValueError("--input required when not using --demo"); non-existent paths raise FileNotFoundError at :366.
--alpha controls segmentation aggressiveness. Lower values (e.g. 0.001) yield fewer / larger segments; higher values (0.1) yield more / smaller. Default 0.01 is suitable for clean exome / WGS data; for noisy panels consider --alpha 0.001.
Classification thresholds are hard-coded. ±0.3 (gain/loss) and ±1.0 (amplification/deep_deletion) at genomics_cnv_calling.py:50-52 — no CLI flag to tune. For tumour-purity-corrected calling, scale the input log2 ratios upstream.

Key CLI

# Demo
python omicsclaw.py run genomics-cnv-calling --demo --output /tmp/cnv_demo

# Real CNVkit bins (rename `chromosome` → `chrom` first)
python omicsclaw.py run genomics-cnv-calling \
  --input sample_renamed.cnr.csv --output results/ --alpha 0.01

# Stricter segmentation for noisy panel data
python omicsclaw.py run genomics-cnv-calling \
  --input panel.cnr.csv --output results/ --alpha 0.001

See also

references/parameters.md — every CLI flag
references/methodology.md — segmentation algorithm, 5-class threshold rationale
references/output_contract.md — tables/cnv_segments.csv schema
Adjacent skills: genomics-alignment (upstream — BAM that generates depth bins), genomics-sv-detection (parallel — large structural variants), spatial-cnv (parallel — single-cell / spatial CNV via infercnvpy / Numbat), genomics-variant-calling (parallel — small variants on the same BAM)