spatial-cnv

name	spatial-cnv
description	Infer copy number variation programs from spatial transcriptomics data using inferCNVpy or Numbat, with method-aware matrix selection, reference controls, and spatially mappable CNV summaries.
version	0.3.0
author	OmicsClaw Team
license	MIT
tags	["spatial","CNV","copy number","infercnvpy","inferCNV","numbat","aneuploidy","cancer"]
metadata	{"omicsclaw":{"domain":"spatial","allowed_extra_flags":["--infercnv-chunksize","--infercnv-dynamic-threshold","--infercnv-exclude-chromosomes","--infercnv-include-sex-chromosomes","--infercnv-lfc-clip","--infercnv-n-jobs","--method","--numbat-genome","--numbat-max-entropy","--numbat-min-cells","--numbat-min-llr","--numbat-ncores","--reference-cat","--reference-key","--step","--window-size"],"param_hints":{"infercnvpy":"[Truncated]","numbat":"[Truncated]"},"legacy_aliases":["cnv"],"saves_h5ad":true,"requires_preprocessed":true,"requires":{"bins":"[Truncated]","env":"[Truncated]","config":"[Truncated]"},"emoji":"🧫","homepage":"https://github.com/TianGzlab/OmicsClaw","os":["macos","linux"],"install":["[Truncated]","[Truncated]"],"trigger_keywords":["copy number variation","CNV","inferCNV","infercnvpy","Numbat","aneuploidy","chromosomal aberration","tumor clone"]}}

🧫 Spatial CNV

You are Spatial CNV, the copy-number inference skill for OmicsClaw. Your role is to identify large-scale chromosomal gains and losses from spatial transcriptomics data while preserving the matrix assumptions and reference requirements of each CNV method.

Why This Exists

• Without it: users have to wire gene-position metadata, reference groups, expression matrices, and downstream spatial visualization manually.
• With it: one skill coordinates matrix-aware CNV inference, exports reproducibility commands, and writes spatially interpretable summary tables.
• Why OmicsClaw: it provides a method-specific wrapper layer around inferCNVpy and Numbat, so the user sees the right knobs first and the project can extend methods later without changing the interaction pattern.

Core Capabilities

1. inferCNVpy (default): expression-based CNV inference on log-normalized expression with official infercnvpy tuning flags.
2. Numbat: haplotype-aware CNV inference via R using raw counts, allele counts, and diploid reference expression profiles.
3. Reference-aware runs: supports reference_key / reference_cat baseline selection for both methods.
4. Standard Python gallery: emits a recipe-driven CNV gallery with overview, diagnostic, supporting, and uncertainty panels backed by the shared skills/spatial/_lib/viz layer.
5. Figure-ready exports: writes figure_data/ CSVs plus a gallery manifest so downstream tools can restyle the same analysis without recomputing CNV.
6. Compact result tables: writes cell-level CNV scores and method-specific CNV summaries for downstream inspection.

Input Formats

Format	Extension	Required Fields	Example
AnnData (preprocessed)	.h5ad	X, obsm["spatial"], var["chromosome"], var["start"], var["end"]	processed.h5ad
Demo	n/a	--demo flag	Runs spatial-preprocess demo first and injects synthetic gene positions / references