Infer CNV scores and clone labels from spatial transcriptomics AnnData objects.
npx skills add https://github.com/chenyhvvvv/STAT-agent --skill cnv-inference
Please help me install this Agent Skill. Skill: cnv-inference Source: https://github.com/chenyhvvvv/STAT-agent/tree/main/stat_agent/skills/cnv-infercnvpy Command: npx skills add https://github.com/chenyhvvvv/STAT-agent --skill cnv-inference
CNV inference from gene expression data enables identification of tumor subclones and genomic alterations in cancer spatial transcriptomics.
Run the CNV inference workflow on your AnnData slice to generate CNV scores and clone labels.
High-intent search queries and answers about installing and using this skill.
To infer CNVs from spatial transcriptomics data, you can run a workflow that generates a CNV score matrix and assigns CNV-based clone labels using a prepared AnnData object with annotated cell types and genomic positions.
CNV detection in an AnnData object requires cell type annotations in adata.obs, genomic position annotations in adata.var, and a defined normal reference cell population to anchor the inference baseline.
Yes, infercnvpy can be applied to single-slice spatial transcriptomics datasets to calculate CNV scores and identify tumor subclones, provided the slice contains the required cell type and genomic position annotations.
Running CNV inference on gene expression data outputs a CNV score matrix stored in X_cnv, CNV-based clone labels in cnv_leiden, and an uns dictionary containing the parameter details used during the analysis.
A normal reference cell population is required for CNV inference to establish a baseline expression profile, allowing the algorithm to distinguish copy number variations in tumor cells from normal expression noise.
Identifying tumor subclones from gene expression data is best achieved by inferring CNV scores to generate a matrix, then clustering the results to assign CNV-based clone labels for downstream spatial analysis.
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