omicverse-single-cell-liana-communication

name

omicverse-single-cell-liana-communication

description

LIANA+ ligand-receptor inference on single-cell AnnData via `ov.single.run_liana`, plus the OmicVerse cell-cell communication (CCC) plotting stack with `ov.pl.ccc_heatmap` and `ov.single.to_comm_adata`. Use when computing ligand-receptor scores from a labeled AnnData (`bulk_labels` / `cell_type` / etc.), when post-processing LIANA results into a CommAnnData for pathway-aware visualisation, or when reproducing `t_ccc_liana`.

OmicVerse Single-Cell — LIANA+ Cell-Cell Communication

Goal

Take a preprocessed annotated single-cell AnnData and run LIANA+ ligand-receptor inference, producing per-(sender, receiver, ligand, receptor) score columns in adata.uns['liana_res']. Then post-process via ov.single.to_comm_adata(...) into a communication AnnData keyed on the sender→receiver direction, classified against the CellChat / CellChatDB pathway taxonomy. Visualise with ov.pl.ccc_heatmap(...) — eight plot_type modes covering dot / tile / aggregation heatmap / pathway-bubble / role-heatmap / role-network / focused-heatmap.

This skill is the LIANA-side complement to the existing single-cell-cellphonedb-communication skill. They produce comparable hit sets but LIANA aggregates across multiple base methods (CellPhoneDB, NATMI, Connectome, SingleCellSignalR, CellChat) into a rank_aggregate consensus — typically more robust than any single method.

Quick Workflow

1. Load a preprocessed annotated AnnData (obs[group_key] populated; for the PBMC8k demo, bulk_labels).
2. Optional: visualise the cohort embedding to confirm cluster labels look right (ov.pl.embedding(adata, basis='X_umap', color='bulk_labels', frameon='small')).
3. Run LIANA: ov.single.run_liana(adata, groupby='bulk_labels', method='rank_aggregate', resource_name='consensus', key_added='liana_res', inplace=True). Result lands at adata.uns['liana_res'].
4. Pathway-aware reshape: comm_adata = ov.single.to_comm_adata(adata, result_uns_key='liana_res', score_key='specificity_rank', pvalue_key='specificity_rank', classification_reference='cellchat', classification_fallback='family'). Returns a CommAnnData with one var per (ligand, receptor) and a var['classification'] column mapping each pair to a CellChat pathway.
5. Pathway dot plots: ov.pl.ccc_heatmap(adata, plot_type='dot', display_by='interaction', score_key='specificity_rank', ...). Multiple plot_type modes share a uniform interface — see Branch Selection below.
6. Aggregation heatmap: plot_type='heatmap', display_by='aggregation' shows sender→receiver totals.
7. Sender / receiver focus: pass sender_use=<cluster> or receiver_use=<cluster> to filter to a single direction.
8. Pathway-focused / role-network views: plot_type='focused_heatmap', 'pathway_bubble', 'role_heatmap', 'role_network' — pick by question (see Branch Selection).
9. Multi-condition comparison: stack LIANA results from multiple conditions, set condition column on the result frames, and re-run plotters with the multi-condition adata.

Interface Summary

ov.single.run_liana(
    adata, *,
    groupby: str,                           # required — the cell-type column
    method: str = 'rank_aggregate',          # or 'cellphonedb', 'natmi', 'connectome', 'sca'
    resource_name: str = 'consensus',        # ligand-receptor DB; 'consensus', 'cellphonedb', 'cellchat', etc.
    key_added: str = 'liana_res',
    inplace: bool = True,
    **kwargs                                 # forwarded to liana.mt.<method>
)
# adata.uns[key_added] -> pd.DataFrame with one row per (sender, receiver, ligand_complex, receptor_complex)
# columns include: 'specificity_rank', 'magnitude_rank', 'lr_means', 'cellphone_pvals', etc.

ov.single.to_comm_adata(
    adata: AnnData | None = None, *,
    data: pd.DataFrame | None = None,
    result_uns_key: str | None = None,
    score_key: str = 'specificity_rank',
    pvalue_key: str = 'specificity_rank',
    inverse_score: bool = True,              # smaller rank = better → invert for visualisation
    inverse_pvalue: bool = False,
    classification: str | Mapping[str, str] | None = None,
    classification_reference: str | pd.DataFrame | None = 'cellchat',
    classification_fallback: str | None = 'family',
    separator: str = '|',
) -> AnnData
# Returns a "communication AnnData":
#   .obs   = (sender, receiver) directed pairs
#   .var   = (ligand, receptor) interactions; var['classification'] is the pathway label
#   .X     = score matrix (pairs × interactions)
#   .layers = {'pvalue': ...}

ov.pl.ccc_heatmap(
    adata_or_comm, *,
    plot_type: str = 'dot',                  # see Branch Selection for full list
    display_by: str = 'interaction',         # 'interaction' or 'aggregation'
    score_key: str = 'specificity_rank',
    pvalue_key: str = 'specificity_rank',
    classification_reference='cellchat',
    classification_fallback='family',
    sender_use: str | list | None = None,
    receiver_use: str | list | None = None,
    signaling: str | list | None = None,     # focus on named pathways
    pattern: str = 'incoming',               # for role_heatmap / role_network: 'incoming' / 'outgoing'
    pvalue_threshold: float = 0.05,
    top_n: int = 10,
    cmap: str = ...,
    figsize: tuple = ...,
    show: bool = False,
    ...,
) -> (fig, ax)

Boundary

Inside scope:

• LIANA fitting on a labeled AnnData with run_liana.
• Reshaping to CommAnnData with to_comm_adata (CellChat / CellPhoneDB / family classification).
• All eight ov.pl.ccc_heatmap modes.
• Sender / receiver / pathway focusing.
• Multi-condition comparison via stacked result frames with a condition column.

Outside scope — separate skill:

• CellPhoneDB-only inference + CellChatViz workflow — see single-cell-cellphonedb-communication (existing skill).
• Spatial cell-cell communication with COMMOT / FlowSig — see spatial-tutorials (existing skill).
• Building the upstream annotated AnnData — separate skill.
• Single-method LIANA (e.g. only NATMI) — supported by method= kwarg but rare in practice; the consensus rank_aggregate is the canonical choice.

Branch Selection

method for run_liana

• 'rank_aggregate' (default) — consensus across CellPhoneDB / NATMI / Connectome / SingleCellSignalR / CellChat. The right default.
• 'cellphonedb' — single-method permutation-based; matches the CellPhoneDB v3 paper.
• 'natmi' — specificity edge-weighting; less conservative than CellPhoneDB.
• 'connectome' — scaled mean expression; gives both magnitude and specificity ranks.
• 'sca' (SingleCellSignalR) — LRscore-based; conservative.

resource_name

• 'consensus' (default) — union of CellChat + CellPhoneDB + Ramilowski + others.
• 'cellphonedb' — strict CellPhoneDB v4 only.
• 'cellchat' — strict CellChat only.
• For organism-specific runs, append _human / _mouse to the resource name.

score_key and pvalue_key

• 'specificity_rank' — both score and pvalue (LIANA reuses the same column for the rank-aggregate output). For other methods, use the method-specific columns ('cellphone_pvals' for CellPhoneDB, 'lr_logfc' for NATMI, etc.).
• 'magnitude_rank' — alternate score that emphasises raw expression magnitude over specificity.
• inverse_score=True (default in to_comm_adata): converts rank-style scores (lower=better) to score-style (higher=better) for visualisation.

plot_type for ccc_heatmap

• 'dot' — sender × receiver bubble plot, dot size = score, colour = pvalue. Default exploratory view.
• 'tile' — heat tile: rows = interactions, cols = sender→receiver pairs.
• 'heatmap' (with display_by='aggregation') — sender × receiver aggregated total CCC strength.
• 'focused_heatmap' — restrict to a named pathway (signaling=['ECM/Adhesion']).
• 'pathway_bubble' — pathway × sender→receiver bubble; needs signaling=[...].
• 'role_heatmap' — sender / receiver role intensity per cluster (pattern='incoming' | 'outgoing').
• 'role_network' — directed network plot of dominant signalling roles.

display_by

• 'interaction' — per (ligand, receptor) detail.
• 'aggregation' — summed across interactions per (sender, receiver). Use with plot_type='heatmap'.

sender_use / receiver_use

• sender_use='CD34+' — show only signals originating from CD34+. Useful for "where does CD34+ talk to?"
• receiver_use='CD34+' — show only signals received by CD34+. Useful for "what regulates CD34+?"

classification_reference

• 'cellchat' (default) — CellChat pathway taxonomy.
• 'family' (fallback) — chemokine / cytokine / TGF-β / etc. families.
• Pass a custom pd.DataFrame keyed by ligand-receptor for arbitrary pathway labels.

Multi-condition comparison: build a stacked frame with a condition column, then call ov.pl.ccc_heatmap with that frame. Allows side-by-side baseline vs. stimulated visualisation.

Input Contract

• AnnData with obs[group_key] populated (categorical or strings); typical group_keys: 'bulk_labels', 'cell_type', 'leiden'.
• adata.X log-normalised expression. LIANA expects this scale; passing raw counts gives nonsensical specificity scores.
• pip install liana (the omicverse install does not ship LIANA by default).
• For to_comm_adata with classification_reference='cellchat': requires the CellChat ligand-receptor mapping shipped under omicverse.external.cellchat (always available in OmicVerse).

Minimal Execution Patterns

import omicverse as ov
import scanpy as sc
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)

ov.plot_set(font_path='Arial')

# 1) Load a labeled AnnData
adata = sc.datasets.pbmc8k()                # or your own
adata.obs['bulk_labels'] = ...               # cluster labels

ov.pl.embedding(adata, basis='X_umap', color='bulk_labels', frameon='small')

# 2) Run LIANA
ov.single.run_liana(
    adata,
    groupby='bulk_labels',
    method='rank_aggregate',
    resource_name='consensus',
    key_added='liana_res',
    inplace=True,
)
print(adata.uns['liana_res'].head())

# 3) Reshape into a CommAnnData with CellChat pathway classification
comm_adata = ov.single.to_comm_adata(
    adata,
    result_uns_key='liana_res',
    score_key='specificity_rank',
    pvalue_key='specificity_rank',
    classification_reference='cellchat',
    classification_fallback='family',
)
print(comm_adata)
print(comm_adata.var['classification'].value_counts().head())

# 4) Dot plot — default exploratory view
fig, ax = ov.pl.ccc_heatmap(
    adata,
    plot_type='dot',
    display_by='interaction',
    score_key='specificity_rank',
    pvalue_key='specificity_rank',
    classification_reference='cellchat',
    classification_fallback='family',
    show=False,
)

# Sender / receiver focused dot plots
fig, ax = ov.pl.ccc_heatmap(
    comm_adata, plot_type='dot', display_by='interaction',
    score_key='specificity_rank', pvalue_key='specificity_rank',
    sender_use='CD34+', top_n=6, pvalue_threshold=0.05,
    show=False,
)
fig, ax = ov.pl.ccc_heatmap(
    adata, plot_type='dot', display_by='interaction',
    score_key='specificity_rank', pvalue_key='specificity_rank',
    receiver_use='CD34+', top_n=5, pvalue_threshold=0.05,
    show=False,
)

# Aggregation heatmap (sender × receiver totals)
fig, ax = ov.pl.ccc_heatmap(
    adata, plot_type='heatmap', display_by='aggregation',
    score_key='specificity_rank', pvalue_key='specificity_rank',
    classification_reference='cellchat', classification_fallback='family',
    cmap='YlGnBu', figsize=(4, 3), show=False,
)

# Focus on a specific pathway
focus = comm_adata.var['classification'].dropna().astype(str).unique().tolist()
focus = [v for v in focus if v not in {'Unclassified', 'nan'}]
focus_pathway = focus[0] if focus else 'Unclassified'

fig, ax = ov.pl.ccc_heatmap(
    adata, plot_type='focused_heatmap',
    signaling=[focus_pathway],
    min_interaction_threshold=,
    cmap=, figsize=(, ), show=,
)


fig, ax = ov.pl.ccc_heatmap(
    adata, plot_type=,
    signaling=[],
    top_n=, figsize=(, ), show=,
)


fig, ax = ov.pl.ccc_heatmap(
    adata, plot_type=,
    pattern=, cmap=,
    figsize=(, ), show=,
)


fig, ax = ov.pl.ccc_heatmap(
    adata, plot_type=,
    signaling=[],
    cmap=, figsize=(, ), show=,
)

Validation

• After run_liana: adata.uns['liana_res'] is a non-empty DataFrame; key columns include source (sender), target (receiver), ligand_complex, receptor_complex, specificity_rank, magnitude_rank. For PBMC8k, expect 100k+ rows (n_clusters² × n_LR_pairs).
• After to_comm_adata: comm_adata.shape is roughly (n_directional_pairs, n_LR_pairs). comm_adata.var['classification'] should have most entries non-null; null entries are unmapped LR pairs.
• For plot_type='dot': dot size and colour scale should be readable; if the figure is empty, the pvalue_threshold is too strict OR score_key was wrong.
• Sender/receiver focus: sender_use=<cluster> should reduce the number of bubbles substantially; if the figure is identical to the unfocused one, the cluster name was misspelled.
• plot_type='focused_heatmap' empty: the named pathway has no LR pairs above the threshold OR the pathway name doesn't exist (check comm_adata.var['classification'].unique()).
• For multi-condition plots: each condition's frame must have the same column schema (source, target, ligand_complex, receptor_complex, specificity_rank minimally).
• Compare LIANA rank_aggregate hits against CellPhoneDB-only hits on the same cohort: typically 60–80 % overlap at the top-100; large disagreement (<30 %) means one method has bad calibration on this cohort.

Resource Map

• See reference.md for compact copy-paste snippets.
• See references/source-grounding.md for verified run_liana / to_comm_adata / ccc_heatmap signatures.
• For the alternative CellPhoneDB-only workflow + CellChatViz, see single-cell-cellphonedb-communication.
• For spatial cell-cell communication (COMMOT / FlowSig), see spatial-tutorials.

Examples

• "Run LIANA rank_aggregate on a PBMC8k cohort grouped by bulk_labels and produce a sender × receiver dot plot."
• "Reshape the LIANA result into a CommAnnData classified against CellChat pathways and plot the role network for ECM/Adhesion."
• "Focus the dot plot on signals originating from CD34+ at pvalue_threshold=0.05."
• "Compare LIANA results between baseline and stimulated conditions on the same cohort."

References

• Tutorial notebook: t_ccc_liana.ipynb — PBMC8k LIANA + CellChat-style visualisation.
• LIANA+ paper: Dimitrov et al. 2022, Nature Communications — "Comparison of methods and resources for cell-cell communication inference".
• Live API verified — see references/source-grounding.md.