infercnvpy-skill

name	infercnvpy-skill
description	scRNA-seq CNV analysis with infercnvpy. Infer copy number variation (CNV) from scRNA-seq data. Plays nicely with Scanpy and AnnData.
metadata	{"author":"sciskillhub","version":"0.1.4"}

infercnvpy-skill

Use this skill for general scRNA-seq CNV analysis with infercnvpy, especially when:

• the data are already in AnnData
• cell annotations already exist in adata.obs
• normal reference cells must be inferred from annotation labels
• malignant candidates must be inferred from annotation labels
• the user wants an automatic tumor/normal or tumor-candidate labeling workflow

What this skill does

This skill provides:

• an annotation-guided workflow for choosing infercnv reference cells
• heuristics for manually selecting normal baseline cell types and suspect malignant lineages from adata.obs
• a per-cell scoring workflow:
  • cnv_score_cell
  • cnv_normal_distance
  • cnv_segment_score
• a two-stage automatic labeling strategy
• default chromosome heatmap outputs for all cells
• a reusable script for arbitrary .h5ad input

Input assumptions

• AnnData is already quality-controlled
• expression is normalized and log-transformed for infercnvpy
• a cell-annotation column exists in adata.obs

Core workflow

1. Load a user-provided .h5ad.
2. Confirm the infercnvpy prerequisites:
   • normalized/log-transformed expression
   • genomic coordinates in adata.var
   • annotation column in adata.obs
   • inspect the preflight summary for annotation-column presence, available labels, genomic-position completeness, X dtype, value range, nonzero-per-cell counts, and normalization/log1p hints in adata.uns or adata.layers
3. If chromosome, start, or end are missing from adata.var, add genomic positions first:
   • prefer infercnvpy.io.genomic_position_from_gtf
   • fall back to infercnvpy.io.genomic_position_from_biomart
4. Inspect adata.obs[annotation_col] labels and their counts.
5. Choose normal reference categories explicitly from the observed labels.
6. Choose suspect malignant lineages explicitly from the observed labels.
   • if no suspect malignant categories can be justified from the user context and adata.obs, stop and ask for confirmation
   • do not continue CNV labeling until the suspect malignant lineages are explicitly confirmed
7. Run cnv.tl.infercnv(...) with explicit --reference-cats and --suspect-cats.
8. Compute per-cell CNV features:
   • per-cell CNV score (cnv_score_cell)
   • distance from normal baseline
   • contiguous abnormal segment score
9. Stage 1:
   • mark cells outside the normal baseline range as tumor_like
10. Stage 2:

• restrict to suspect malignant lineages
• use cnv_segment_score to promote high-confidence tumor cells

11. Save labels and scores back into AnnData.
12. Write standard plots:

• per-cell CNV score and segment score distributions
• expression-space embeddings if available
• chromosome heatmaps for all cells grouped by automatic CNV status and by the input annotation column

Why annotation-guided inference matters

For most scRNA-seq datasets, automatic CNV analysis is only as good as the normal reference set. Existing cell annotations usually contain enough information to build a reasonable first-pass reference:

• immune cells are usually safe normal references
• stromal cells are often safe normal references
• epithelial-lineage cells are often where malignant candidates live in carcinomas

The same annotation column should therefore drive both:

• baseline selection
• suspect lineage selection

In this skill, that decision is made by the agent after inspecting the actual labels in adata.obs. The bundled script does not do keyword-based auto-matching.

Practical defaults

• Stage 1 per-cell CNV score threshold: mean + 2*sd on baseline normals
• Stage 1 normal-distance threshold: q99 on baseline normals
• Stage 2 segment threshold: mean + 2*sd on baseline normals

These are defaults, not universal truths. Use stricter thresholds when false positives are more costly than false negatives.

Recommended labels

The bundled workflow writes three automatic labels:

• normal
• tumor_candidate
• tumor_high_cnv

Interpretation:

• normal: not clearly outside the normal baseline
• tumor_candidate: abnormal but not yet high-confidence CNV tumor
• tumor_high_cnv: abnormal and supported by contiguous CNV segment structure in suspect malignant lineages

Default plot outputs

The bundled script writes these standard figures into --output-dir:

• 01_global_baseline_distribution.png
• 02_candidate_feature_scatter.png
• 03_segment_score_distribution.png
• 04_expression_umap_labels.png when X_umap exists
• 05_expression_umap_scores.png when X_umap exists
• 06_chromosome_heatmap_all_by_cnv_status.png
• 07_chromosome_heatmap_all_by_annotation.png

Files

• Main script:
  • scripts/run_infercnvpy.py
• Expression preflight script:
  • scripts/check_preflight.py
• References:
  • references/genomic_positions.md
  • references/annotation_heuristics.md
  • references/feature_definitions.md

When to read references

• Read references/genomic_positions.md when adata.var is missing genomic coordinates and positions must be added from GTF or Biomart.
• Read references/annotation_heuristics.md when deciding which labels should be normal references and which labels should be suspect malignant lineages.
• Read references/feature_definitions.md when tuning the automatic CNV features or justifying the method.

Running the bundled script

Example:

python \
./scripts/check_preflight.py \
--input-h5ad /path/to/input.h5ad \
--annotation-col cell_type \
--check-mode warn

PYTHONPATH=/data20T/dev/agenticbioinfo/sciskillhub/infercnvpy/src \
python \
./scripts/run_infercnvpy.py \
--input-h5ad /path/to/input.h5ad \
--annotation-col cell_type \
--reference-cats "T cell CD4,T cell CD8,B cell,NK cell,Fibroblast,Endothelial cell" \
--suspect-cats "Epithelial cell,Ciliated" \
--output-dir /path/to/output_dir

# Optional: skip heatmaps for large datasets
python \
./scripts/run_infercnvpy.py \
--input-h5ad /path/to/input.h5ad \
--annotation-col cell_type \
--reference-cats "T cell CD4,T cell CD8,B cell,NK cell,Fibroblast,Endothelial cell" \
--suspect-cats "Epithelial cell,Ciliated" \
--output-dir /path/to/output_dir \
--skip-heatmaps

Recommended usage pattern

First inspect adata.obs[annotation_col] directly, then choose categories explicitly and pass them to the script. The agent should not rely on keyword matching inside the script.

Use the separate preflight script first when checking a new dataset. It reports:

• whether annotation_col exists in adata.obs
• the available annotation labels and counts
• whether adata.var contains complete chromosome/start/end coordinates
• whether X looks more like normalized/log-transformed expression or suspicious raw/scaled input

The main infercnv script reuses the same preflight logic before CNV inference. If X looks like raw counts or scaled data, it will warn by default. Use --expression-check-mode fail when you want the run to stop on suspicious input.

Important reminder: If no suspect malignant labels can be justified from the annotation column, stop and ask for explicit confirmation instead of silently continuing.

The main script now also writes whole-dataset cnv.pl.chromosome_heatmap(...) outputs. These heatmaps use all cells in the supplied AnnData, grouped by:

• cnv_status_2stage
• annotation_col

Use --skip-heatmaps when the dataset is large enough that full-cell heatmaps would be too slow or too large to inspect comfortably.

Override or refine the initial category choice when:

• the dataset uses nonstandard annotation names
• epithelial subtypes are mixed with clearly normal epithelial cells
• the user already knows the exact normal reference set

If no suspect malignant categories can be justified from adata.obs, stop and ask the user to confirm them explicitly before continuing. In scripted runs, fail fast and require both --reference-cats and --suspect-cats.

Notes

• Prefer per-cell scoring over cluster-only scoring when cluster count is small.
• Do not treat the output as ground truth without checking whether the inferred normal references make biological sense.
• Segment-continuity remains the automatic quantitative readout for broad contiguous CNV structure, even though the default workflow now also saves whole-cell chromosome heatmaps for manual review.