spatial-enrichment

Research

Load when running pathway / gene-set enrichment per cluster on a preprocessed spatial AnnData via Enrichr (over-representation), GSEA (preranked), or ssGSEA (per-cell scores). Skip when ranking spatially variable genes (use `spatial-genes`) or when comparing pathways across conditions (use `spatial-condition` for DE first, then this skill on the ranked output).

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How to use this skill

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Source SKILL.md: https://github.com/TianGzlab/OmicsClaw/blob/HEAD/skills/spatial/spatial-enrichment/SKILL.md

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spatial-enrichment

When to use

The user has a preprocessed spatial AnnData with cluster labels in obs[groupby] (default leiden) and wants pathway / gene-set enrichment per cluster. Three backends:

enrichr (default) — over-representation against an Enrichr hosted gene-set library. Tunables --enrichr-padj-cutoff, --enrichr-log2fc-cutoff, --enrichr-max-genes.
gsea — preranked GSEA. Tunables --gsea-min-size, --gsea-max-size, --gsea-permutation-num, --gsea-weight, --gsea-threads, --gsea-seed.
ssgsea — single-sample GSEA per cell, scores written back to obs[...]. Tunables --ssgsea-min-size, --ssgsea-max-size, --ssgsea-weight.

--gene-set selects a hosted library (e.g. MSigDB_Hallmark_2020); --gene-set-file accepts a custom GMT. Species: human (default) or mouse. For non-spatial enrichment use sc-enrichment.

Inputs & Outputs

Input	Format	Required
Preprocessed spatial AnnData	`.h5ad` with `obsm["spatial"]`, `obs[groupby]` (auto-runs Leiden if missing)	yes (unless `--demo`)
Gene set	`--gene-set <library_name>` (Enrichr lib) or `--gene-set-file <path.gmt>`	one of the two for non-default runs

Output	Path	Notes
Annotated AnnData	`processed.h5ad`	Enrichr/GSEA/ssGSEA: `uns["enrichment_results"]` + `uns["{method}_results"]` (`uns["enrichr_results"]` / `uns["gsea_results"]` / `uns["ssgsea_results"]`, written at `_lib/enrichment.py:1113-1117`); ssGSEA additionally writes per-cell scores as `obs[<score_col>]` columns and a list `uns["enrichment_score_columns"]` (`_lib/enrichment.py:907-910`)
Full results	`tables/enrichment_results.csv`	every term, every group
Significant subset	`tables/enrichment_significant.csv`	filtered by `--fdr-threshold`
Ranked markers	`tables/ranked_markers.csv`	input to enrichment
Top terms	`tables/top_enriched_terms.csv`	top-N per group
Group metrics	`tables/enrichment_group_metrics.csv`	n_terms / n_significant per group
Run summary	`tables/enrichment_run_summary.csv`	params used
Report	`report.md` + `result.json`	always

Flow

Load AnnData; if obs[groupby] is missing, auto-cluster with Leiden (or parser.error if dataset too small).
For Enrichr / GSEA: rank markers per group via sc.tl.rank_genes_groups (--de-method wilcoxon/t-test), then submit to Enrichr or run preranked GSEA.
For ssGSEA: compute per-cell pathway scores; write columns to obs[...] + register them in uns["enrichment_score_columns"] (_lib/enrichment.py:907-910).
Persist canonical results to uns["enrichment_results"] plus a per-method copy at uns[f"{method}_results"] (_lib/enrichment.py:1113-1117).
Filter by --fdr-threshold + --n-top-terms; export per-group ranked tables.
Render barplot / dotplot / spatial / violin / score-distribution plots.
Save tables + processed.h5ad + report.

Gotchas

Default groupby is leiden. _lib/enrichment.py:32-39 and the CLI default --groupby leiden. If obs["leiden"] is missing, the script auto-runs Leiden — but only if the dataset is large enough; otherwise spatial_enrichment.py:1298 calls parser.error("Dataset is too small to auto-compute leiden clusters").
Enrichr requires internet access. Enrichr is a hosted API — runs fail in air-gapped environments. Use --method gsea or --method ssgsea with a local --gene-set-file for offline workflows.
ssGSEA score-column NAMES are not stable across runs. _lib/enrichment.py:907-910 constructs them from the geneset library + term name, then registers the list in uns["enrichment_score_columns"]. Always read that key — don't hard-code column names.
obs[groupby] is double-cast. The wrapper at spatial_enrichment.py:422-423 first casts to pd.Categorical(...) (so plotting / report ordering uses sorted categories). Later, _lib/enrichment.py:94-97 (_ensure_obs_string) re-casts to plain str for the marker-ranking step. The on-disk processed.h5ad reflects the final string cast — Categorical ordering on input is lost either way.
GSEA permutation tests are slow. --gsea-permutation-num (default 1000) drives runtime; for sketch runs drop to 100. Use --gsea-threads N for parallelism.
uns["{method}_results"] mirrors uns["enrichment_results"]. _lib/enrichment.py:1113-1117 writes the canonical key plus a per-method alias (uns["ssgsea_results"] / uns["gsea_results"]). Downstream readers should prefer the canonical key.

Key CLI

# Demo
python omicsclaw.py run spatial-enrichment --demo --output /tmp/enr_demo

# Enrichr over-representation (default)
python omicsclaw.py run spatial-enrichment \
  --input preprocessed.h5ad --output results/ \
  --method enrichr --groupby leiden --species human \
  --gene-set MSigDB_Hallmark_2020 --fdr-threshold 0.05 --n-top-terms 20

# GSEA preranked with custom GMT
python omicsclaw.py run spatial-enrichment \
  --input preprocessed.h5ad --output results/ \
  --method gsea --gene-set-file /path/to/library.gmt \
  --gsea-min-size 15 --gsea-max-size 500 --gsea-permutation-num 1000

# ssGSEA per-cell scoring
python omicsclaw.py run spatial-enrichment \
  --input preprocessed.h5ad --output results/ \
  --method ssgsea --gene-set MSigDB_Hallmark_2020 \
  --ssgsea-min-size 10 --ssgsea-max-size 500

See also

references/parameters.md — every CLI flag, per-method tunables
references/methodology.md — when each backend wins; gene-set choice
references/output_contract.md — uns["enrichment_results"] + ssGSEA obs[...] schema
Adjacent skills: spatial-preprocess (upstream), spatial-domains (upstream — provides obs[groupby]), spatial-de (parallel / upstream — provides rank_genes_groups ranking), sc-enrichment (parallel — non-spatial), spatial-communication (parallel — L-R signaling)