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single-cell-cellphonedb-communication-mapping

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Run omicverse's CellPhoneDB v5 wrapper on annotated single-cell data to infer ligand-receptor networks and produce CellChat-style visualisations.

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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
    • Read the normalised counts with adata = ov.read('data/cpdb/normalised_log_counts.h5ad').
    • 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. 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