metachat.pl.plot_MSpair_contribute_group

metachat.pl.plot_MSpair_contribute_group(adata, database_name=None, group_name=None, metabolite_name=None, summary='sender', cmap='green', group_cmap=None, figsize=(4, 6), ax=None, plot_savepath=None)[source]

Plot a heatmap showing group-level contributions of metabolite–sensor pairs for a specific metabolite.

This function visualizes how each cell group contributes to the overall communication strength of all metabolite–sensor pairs associated with a given metabolite. It is particularly useful for identifying dominant sender or receiver groups for specific metabolites.

Parameters

adataanndata.AnnData

Annotated data matrix containing MetaChat results. Must include: - adata.uns['df_metasen_filtered'] : table linking metabolites (HMDB IDs) to sensors. - adata.obsm['MetaChat-<database_name>-sum-<summary>'] : communication summary matrix.

database_namestr

Name of the metabolite–sensor interaction database used for MCC computation.

group_namestr

Column name in adata.obs specifying the cell group or cluster identity.

metabolite_namestr

HMDB ID of the metabolite to visualize.

summary{“sender”, “receiver”}, default=”sender”

Whether to visualize sender- or receiver-side contributions.

cmap{“green”, “red”, “blue”}, default=”green”

Colormap for the heatmap. - “green”: metabolic enrichment style - “red”: activity intensity style - “blue”: signal pathway style

group_cmapdict, optional

Mapping from group names to colors. If None, derived automatically from adata.uns[group_name + '_colors'].

figsizetuple of float, default=(4, 6)

Figure size (width, height).

axmatplotlib.axes.Axes, optional

Existing Matplotlib axis to draw the heatmap on. If None, a new figure is created.

plot_savepathstr, optional

File path to save the figure (e.g., "results/MSpair_contribution_heatmap.pdf"). The format is inferred from the file extension. If None, the plot is displayed interactively without saving.

Returns

axmatplotlib.axes.Axes

Matplotlib axis containing the contribution heatmap.

Notes

  • The function requires prior execution of:

    1. mc.tl.metabolic_communication()

    2. mc.tl.summary_communication()

    3. mc.tl.communication_group() or mc.tl.communication_group_spatial()

  • Each column represents a metabolite–sensor pair, and each row represents a cell group.

  • Values are log-transformed group-level summed communication scores.