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Here is doc for infercnv-mcp, but you can refer to infercnvpy docs at https://infercnvpy.readthedocs.io/

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Core Analysis Module

  • infercnv: Perform Copy Number Variation (CNV) analysis by averaging gene expression over genomic regions
  • cnv_score: Calculate CNV scores for individual cells to quantify copy number variation

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Dimensionality Reduction Module

  • pca: Perform Principal Component Analysis on CNV data with default parameters optimized for CNV analysis
  • tsne: Generate t-SNE visualization of CNV data using CNV-specific neighborhood information
  • umap: Create UMAP projection of CNV data utilizing CNV-specific neighborhood relationships

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Clustering Module

  • leiden: Perform Leiden clustering on CNV data using CNV-specific neighborhood information
  • louvain: Apply Louvain clustering algorithm to CNV data using CNV-specific neighborhood relationships

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Data Requirements

The input AnnData object must contain the following columns in adata.var:
  • chromosome: Chromosomal location of genes
  • start: Start position of genes
  • end: End position of genes
These columns can be added using the load_gene_position function before running CNV analysis.

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Output

The analysis generates two main outputs:
  1. CNV scores stored in the original AnnData object
  2. A separate CNV-specific AnnData object containing the CNV analysis results
All visualizations and clustering results are stored with CNV-specific keys (e.g., cnv_pca, cnv_umap, cnv_leiden) to distinguish them from other analyses.