OpenClaw-Medical-Skills single-cell-cellphonedb-communication-mapping

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

install

source · Clone the upstream repo

git clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/single-cellphone-db" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-single-cell-cellphonedb-communicatio && rm -rf "$T"

OpenClaw · Install into ~/.openclaw/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/single-cellphone-db" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-single-cell-cellphonedb-communicatio && rm -rf "$T"

manifest: skills/single-cellphone-db/SKILL.md

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

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.
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.

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.

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.
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.

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(...)

viz.netAnalysis_contribution(pathway)

to surface dominant pairs.

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.

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:

omicverse_guide/docs/Tutorials-single/data/cpdb/

Quick copy/paste commands:
```
reference.md
```