sc-cell-communication

sc-cell-communication

When to use

The user has an annotated scRNA AnnData (cell-type labels in obs["cell_type"] or another column passed via --cell-type-key) and wants ligand-receptor / sender-receiver interaction tables and figures. Five backends:

• builtin (default) — compact curated L-R set, heuristic score, no p-values.
• liana — Python LIANA rank aggregation (recommended general default).
• cellphonedb — official CellPhoneDB statistical workflow (human-only).
• cellchat_r — R-backed CellChat with pathway / centrality outputs.
• nichenet_r — R-backed NicheNet ligand prioritisation; needs explicit --receiver + --senders + --condition-* (human-only).

For TF → target gene regulatory networks use sc-grn. For cell-type labelling use sc-cell-annotation.

Use in an analysis step

Use load_skill from the notebook SDK; write returned objects with write_output. This runnable example is also in examples/example_step.py. The CLI remains available for standalone reports and galleries.

# Rank curated ligand-receptor mean products across PBMC clusters.
# Reads pbmc3k_processed and uses its log-normalized raw snapshot.
# Calls sc-cell-communication: communicate, sender_receiver_summary, interaction_heatmap_figure.
# The builtin method does not test statistical significance.

from skills._sdk.notebook import load_demo, load_skill, write_output

communication = load_skill('sc-cell-communication')
adata = load_demo('pbmc3k_processed').raw.to_adata()

table = communication.communicate(adata, cell_type_key='louvain')
write_output(table, 'tables/lr_interactions.csv')
write_output(communication.sender_receiver_summary(table), 'tables/sender_receiver.csv')
write_output(communication.interaction_heatmap_figure(table), 'figures/interaction_heatmap.png')

assert not table.empty
assert table.pvalue.isna().all()
assert (table.score > 0).all()
assert set(table.source).issubset(set(adata.obs['louvain'].astype(str)))

Inputs & Outputs