spatial-communication

Research

Load when computing ligand-receptor cell-cell communication on a preprocessed spatial AnnData with `obs[cell_type_key]` (default `leiden`) via LIANA (default), CellPhoneDB, FastCCC, or CellChat (R). Skip when running scRNA-only L-R inference (use `sc-cell-communication`) or when no cell-type labels exist (run `spatial-annotate` or `spatial-domains` first).

QUICK START

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

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spatial-communication

When to use

The user has a preprocessed spatial AnnData with cell-type labels (obs[cell_type_key], default leiden) and wants ligand-receptor cell-cell communication scored. Four backends:

liana (default) — LIANA consensus across multiple L-R methods. Tunables --liana-expr-prop, --liana-min-cells, --liana-n-perms.
cellphonedb — Permutation test with mean expression statistic. Tunables --cellphonedb-iterations, --cellphonedb-threshold.
fastccc — Fast permutation-free percentile-based score. Tunables --fastccc-min-percentile.
cellchat_r — CellChat (R) via rpy2 interop. Tunables --cellchat-min-cells, --cellchat-prob-type.

Species: --species human (default) or mouse. For non-spatial L-R use sc-cell-communication; for pathway scoring use spatial-enrichment.

Inputs & Outputs

Input	Format	Required
Preprocessed spatial AnnData	`.h5ad` with `obsm["spatial"]`, `obs[cell_type_key]` (default `leiden`)	yes (unless `--demo`)

Output	Path	Notes
Annotated AnnData	`processed.h5ad`	`uns["ccc_results"]` (canonical L-R DataFrame); per-method copy at `uns["liana_results"]` / `uns["cellphonedb_results"]` / `uns["fastccc_results"]` / `uns["cellchat_results"]`; `uns["communication_summary"]` (pathway-level); `uns["communication_signaling_roles"]` (sender/receiver scores); `uns["spatial_communication"]` (run metadata)
L-R interactions	`tables/lr_interactions.csv`	full L-R list
Top interactions	`tables/top_interactions.csv`	top-N filtered
Pathway summary	`tables/communication_summary.csv`	aggregate per pathway
Signaling roles	`tables/signaling_roles.csv`	sender/receiver per cell type
Source-target	`tables/source_target_summary.csv`	per source-target pair counts
Run summary	`tables/communication_run_summary.csv`	params used
Report	`report.md` + `result.json`	always

Flow

Load AnnData, validate obs[cell_type_key] exists with ≥ 2 categories (_lib/communication.py:764-765).
Sync obsm["spatial"] ↔ obsm["X_spatial"] (spatial_communication.py:79-81); cast cell-type column to Categorical.
Dispatch to chosen backend (LIANA / CellPhoneDB / FastCCC / CellChat-R).
Write canonical L-R results to uns["ccc_results"] + per-method uns[METHOD_RESULT_KEYS[method]] (_lib/communication.py:735-739).
Compute pathway-level summary, signaling roles, source-target summary.
Save tables + processed.h5ad + report.

Gotchas

obs[cell_type_key] is REQUIRED — no auto-fallback. _lib/communication.py:764-765 raises ValueError when the column is missing. Run spatial-annotate or spatial-domains first.
Default cell-type column is leiden, not cell_type. spatial_communication.py:1065 defaults --cell-type-key to "leiden". If your AnnData uses cell_type, pass --cell-type-key cell_type explicitly.
CellChat backend needs an R install with CellChat. --method cellchat_r invokes R via rpy2. Install CellChat in your R environment first; missing R / rpy2 / CellChat surfaces as a runtime error inside the dispatch step (not at parser.error), so the failure happens after argument parsing succeeds.
FastCCC --fastccc-min-percentile must be in [0, 1]. spatial_communication.py:985 rejects values outside that range with parser.error.
Output uns keys are unconditionally written, even with 0 interactions. _lib/communication.py:735-739 writes empty uns["ccc_results"] / uns["communication_summary"] if no L-R pairs pass thresholds — distinguish "no signal" from "method failed" by inspecting tables/communication_run_summary.csv.
Per-method copy uses METHOD_RESULT_KEYS mapping. _lib/communication.py:68-73 maps liana → uns["liana_results"], cellphonedb → uns["cellphonedb_results"], fastccc → uns["fastccc_results"], cellchat_r → uns["cellchat_results"]. Downstream readers should prefer uns["ccc_results"] for portability.

Key CLI

# Demo
python omicsclaw.py run spatial-communication --demo --output /tmp/comm_demo

# LIANA consensus (default)
python omicsclaw.py run spatial-communication \
  --input preprocessed.h5ad --output results/ \
  --method liana --species human --cell-type-key cell_type \
  --liana-expr-prop 0.1 --liana-min-cells 5 --liana-n-perms 1000

# CellPhoneDB permutation test
python omicsclaw.py run spatial-communication \
  --input preprocessed.h5ad --output results/ \
  --method cellphonedb --cellphonedb-iterations 1000 --cellphonedb-threshold 0.1

# CellChat (R via rpy2)
python omicsclaw.py run spatial-communication \
  --input preprocessed.h5ad --output results/ \
  --method cellchat_r --species mouse \
  --cellchat-min-cells 10 --cellchat-prob-type triMean

See also

references/parameters.md — every CLI flag, per-method tunables
references/methodology.md — when each backend wins
references/output_contract.md — uns["ccc_results"] schema + per-method copies
Adjacent skills: spatial-annotate (upstream — provides obs[cell_type_key]), spatial-domains (upstream alternative — Leiden domains), sc-cell-communication (parallel — non-spatial L-R), spatial-condition (parallel — DE between conditions), spatial-enrichment (parallel — pathway scoring)