/genomics-cnv-calling

name: genomics-cnv-calling
description: 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).
trigger: CNV, copy number, amplification, deletion, CNVkit
tags:
- genomics
- cnv
- copy-number
- cbs
- segmentation
- cnvkit
- gatk-gcnv

genomics-cnv-calling

When to use

Load this skill for the file-based analysis named in the description. The function library and CLI share the same calculations; no external aligner, assembler, caller or annotation service is started.

Use from a step

from skills._sdk.notebook import load_skill, read_input, write_output
library = load_skill("genomics-cnv-calling")
data = read_input("input.csv", reader=library.read_bins)
result = library.analyze(data)
write_output(result, "tables/result.csv")
write_output(library.copy_ratio_figure(result), "figures/distribution.png")

Run `examples/example_step.py` through the step runner for a small, hand-worked synthetic fixture. It asserts known summary values. The reader materializes the input in memory; use bounded FASTQ reads or pre-filter large genomic files before loading them.

API

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`read_bins(path: str | Path) -> pd.DataFrame`

Read a bin CSV through read_input(path, reader=library.read_bins).

:param path: CSV containing chrom, start, end and log2_ratio. :returns: Bin table. :raises ValueError: The CSV cannot be parsed.

`analyze(data: pd.DataFrame, *, method: str='cbs', alpha: float=0.01) -> pd.DataFrame`

Return new CNV segments from bin-level log2 ratios; leave data unchanged.

:param data: Bins with chrom, start, end and finite log2_ratio. :param method: CLI default cbs, or none to classify individual bins. :param alpha: CLI default 0.01; smaller values require stronger splits. :returns: Segments with cn_state, estimated_cn and attrs['run_info']. :raises ValueError: Columns, coordinates, method or alpha are invalid.

`run_info(data: pd.DataFrame, *, keep: bool=True) -> dict`

Return analysis diagnostics and numeric summary.

:param data: Result returned by analyze. :param keep: Keep diagnostics by default; the CLI passes False. :returns: Independent copy of run diagnostics. :raises ValueError: analyze has not populated diagnostics.

`copy_ratio_figure(data: pd.DataFrame)`

Plot segment log2 ratios in table order without writing a file.

:param data: Segment table returned by analyze. :returns: Matplotlib Figure. :raises ValueError: log2_ratio is absent or the table is empty.

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Methods and parameters

`analyze` returns a new DataFrame and leaves the input unchanged. `run_info(result)` returns the summary and method diagnostics. The CLI passes `keep=False` so diagnostics do not enter output tables. All calculations are deterministic; synthetic CLI demos retain seed 42.

Gotchas

• `analyze(method="cbs")` uses the existing simplified deterministic split heuristic, not CNVkit or a calibrated CBS permutation test.
• `run_info()["summary"]["genome_fraction_altered"]` retains the legacy fraction of altered segments, not a base-pair-weighted genome fraction.
• `analyze(method="none")` classifies individual bins; gain/loss thresholds are 0.3/-0.3 and amplification/deep-deletion thresholds are 1/-1.

Inputs and outputs

Input files:

• File types: `.csv`

CLI output files:

• `tables/cnv_per_chromosome.csv`
• `tables/cnv_segments.csv`
• `report.md`
• `result.json`

The library writes no files. Steps use `write_output`; the CLI owns the listed artifacts. Public figure functions return matplotlib Figures and do not add new CLI outputs.

CLI

python skills/genomics/genomics-cnv-calling/genomics_cnv_calling.py --input input_file --output results/
python skills/genomics/genomics-cnv-calling/genomics_cnv_calling.py --demo --output /tmp/genomics_cnv_calling_demo

See also

• `references/parameters.md`
• `references/methodology.md`
• `references/output_contract.md`

Dependencies

`numpy`, `pandas`, `matplotlib`