single-cell-cellphonedb-communication-mapping

name	single-cell-cellphonedb-communication-mapping
title	Single-cell CellPhoneDB communication mapping
description	CellPhoneDB v5 ligand-receptor analysis, cell-cell communication networks, and CellChat-style visualization in OmicVerse.

Single-cell CellPhoneDB communication mapping

Overview

Apply this skill when a user wants to quantify ligand–receptor communication between annotated single-cell populations and display the networks with CellChatViz. It distils the workflow from t_cellphonedb.ipynb, which analyses EVT trophoblast data.

Instructions

1. Prepare the environment
   • Use an environment with omicverse>=0.2, scanpy, anndata, pandas, matplotlib, and cellphonedb resources. The tutorial assumes the pre-built CellPhoneDB v5 SQLite bundle downloaded as cellphonedb.zip in the working directory.
   • Activate omicverse plotting defaults via ov.plot_set() so that downstream figures follow the project palette.
2. Load and subset the annotated AnnData object
   • Load a local AnnData export prepared from the tutorial inputs; the public tutorial page documents the example CellPhoneDB dataset and expected preprocessing before running adata = ov.read(...).
   • Filter to the cell populations of interest using adata.obs['cell_labels'] (e.g., EVT, dNK, VCT). Ensure adata.obs['cell_labels'] is categorical and free of missing values so CellPhoneDB groups cells correctly.
   • Confirm values are log-normalised (adata.X.max() should be <10 and non-integer); raw counts inflate CellPhoneDB permutations.
3. Run CellPhoneDB via omicverse
   • Execute ov.single.run_cellphonedb_v5 with the curated AnnData and metadata column:

     cpdb_results, adata_cpdb = ov.single.run_cellphonedb_v5(
         adata,
         cpdb_file_path='./cellphonedb.zip',
         celltype_key='cell_labels',
         min_cell_fraction=0.005,
         min_genes=200,
         min_cells=3,
         iterations=1000,
         threshold=0.1,
         pvalue=0.05,
         threads=10,
         output_dir='./cpdb_results',
         cleanup_temp=True,
     )
     

   • Persist the outputs for reuse (ov.utils.save(cpdb_results, ...), adata_cpdb.write(...)). Saving avoids recomputing permutations.
4. Initialise CellChat-style visualisation
   • Create a colour dictionary that maps ordered cell_labels categories to adata.uns['cell_labels_colors'] from previous plots.
   • Instantiate the viewer: viz = ov.pl.CellChatViz(adata_cpdb, palette=color_dict). Inspect adata_cpdb to ensure communication slots (uns/obsm) were populated.
5. Summarise global communication
   • Derive aggregated counts/weights with viz.compute_aggregated_network(pvalue_threshold=0.05, use_means=True).
   • Plot overall interaction strength and counts using viz.netVisual_circle(...) with matching figure sizes and colormaps.
   • Generate outgoing/incoming per-celltype circles using viz.netVisual_individual_circle and viz.netVisual_individual_circle_incoming to highlight senders versus receivers.
6. Interrogate specific pathways
   • Compute pathway summaries: pathway_comm = viz.compute_pathway_communication(method='mean', min_lr_pairs=2, min_expression=0.1).
   • Identify significant signalling routes with viz.get_significant_pathways_v2(...), then plot selected pathways using viz.netVisual_aggregate(..., layout='circle'), viz.netVisual_chord_cell(...), or viz.netVisual_heatmap_marsilea(...).
   • For ligand–receptor focus, call viz.netVisual_chord_LR(...) or viz.netAnalysis_contribution(pathway) to surface dominant pairs.
7. System-level visualisations
   • Compose bubble summaries for multiple pathways with viz.netVisual_bubble_marsilea(...), optionally restricting sources_use/targets_use.
   • Display gene-level chords via viz.netVisual_chord_gene(...) to inspect signalling directionality.
   • Evaluate signalling roles using viz.netAnalysis_computeCentrality(), viz.netAnalysis_signalingRole_network_marsilea(...), viz.netAnalysis_signalingRole_scatter(...), and viz.netAnalysis_signalingRole_heatmap(...) for incoming/outgoing programmes.
8. Defensive validation

   # Before CellPhoneDB: validate cell type column
   assert celltype_key in adata.obs.columns, f"Column '{celltype_key}' not found in adata.obs"
   adata.obs[celltype_key] = adata.obs[celltype_key].astype('category').cat.remove_unused_categories()
   assert not adata.obs[celltype_key].isna().any(), f"NaN values in '{celltype_key}' — clean before running CellPhoneDB"
   min_per_group = adata.obs[celltype_key].value_counts().min()
   if min_per_group < 10:
       print(f"WARNING: smallest cell group has {min_per_group} cells — may cause permutation failures")
   

9. Troubleshooting tips
   • Metadata alignment: CellPhoneDB requires a categorical celltype_key. If the column contains spaces, mixed casing, or NaN, clean it (adata.obs['cell_labels'] = adata.obs['cell_labels'].astype('category').cat.remove_unused_categories()).
   • Database bundle: cpdb_file_path must point to a full CellPhoneDB v5 SQLite zip. If omicverse raises FileNotFoundError or missing receptor tables, re-download the bundle from the official release and ensure the zip is not corrupted.
   • Permutation failures: Low cell counts per group (<min_cells) cause early termination. Increase min_cell_fraction thresholds or merge sparse clusters before rerunning.
   • Palette mismatches: When colours render incorrectly, rebuild color_dict from adata.uns['cell_labels_colors'] after sorting categories to keep nodes and legends consistent.

Examples

• "Run CellPhoneDB on our trophoblast dataset and export both the cpdb results pickle and processed AnnData."
• "Highlight significant 'Signaling by Fibroblast growth factor' interactions with chord and bubble plots."
• "Generate outgoing versus incoming communication circles to compare dNK subsets."

References

• Tutorial notebook: t_cellphonedb.ipynb
• Example data: see the tutorial page for example CellPhoneDB inputs: t_cellphonedb
• Quick copy/paste commands: reference.md