API

Preprocessing: pp

pp.MetaChatDB([species])	Extract metabolite-sensor pairs from MetaChatDB.
pp.generate_adata_met_compass(compass_output)	Generate processed metabolite matrix for COMPASS analysis using reaction-level penalty scores.
pp.generate_adata_met_scFEA(data_path)	Generate processed metabolite matrix for scFEA analysis.
pp.generate_adata_met_mebocost(data_path)	Generate processed metabolite matrix for scFEA analysis.
pp.global_intensity_scaling(adata_ref, ...)	Perform global intensity scaling of adata_target to match adata_ref, using either total ion current (TIC) or root-mean-square (RMS) normalization.
pp.load_barrier_segments([csv_path, ...])	Parse Napari shapes CSV and extract barrier line segments.
pp.LRC_unfiltered(adata[, LRC_name, ...])	Identify unfiltered candidate LRC (long-range channel) spots based on the quantile of a marker feature.
pp.LRC_cluster(adata[, LRC_name, ...])	Perform local density clustering on unfiltered LRC candidate points.
pp.LRC_filtered(adata[, LRC_name, ...])	Assign final LRC (long-range channel) clusters after local density clustering.
pp.compute_costDistance(adata[, LRC_type, ...])	Compute LRC-embedding cost distance based on visibility and local connectivity, supporting both 2D and 3D spatial coordinates.

Tools: tl

tl.metabolic_communication(adata[, ...])	Infer spatial metabolic cell communication (MCC) using the Flow Optimal Transport (FOT) framework.
tl.summary_communication(adata[, ...])	Summarize communication signals by metabolite sets, pathways, or custom lists.
tl.communication_flow(adata[, ...])	Construct spatial vector fields representing metabolic communication flow.
tl.communication_group(adata[, ...])	Summarize metabolic communication to group-level MCC and compute p-values via label permutation.
tl.communication_group_spatial(adata[, ...])	Function for summarizing metabolic MCC communication to group-level communication and computing p-values based on spatial distance distribution.
tl.summary_pathway(adata[, database_name, ...])	Summarize MCC (Metabolite–Sensor Communication) patterns between specific sender and receiver groups, and rank metabolic and sensor pathways.
tl.communication_responseGenes(adata, adata_raw)	Identify signal-dependent genes responding to MCC communication patterns.
tl.communication_responseGenes_cluster(...)	Function for cluster the communcation DE genes based on their fitted expression pattern.
tl.communication_responseGenes_keggEnrich([...])	Function for performing KEGG enrichment analysis on a given list of response genes.
tl.compute_direction_histogram_per_pair(...)	Compute per-pair directional histograms for MCC vector fields.

Plotting: pl

pl.plot_communication_flow(adata, database_name)	Visualize inferred metabolic communication vector fields on tissue images or annotated backgrounds.
pl.plot_group_communication_chord(adata[, ...])	Plot a chord diagram representing group-level metabolic cell communication (MCC).
pl.plot_group_communication_heatmap(adata[, ...])	Plot a heatmap diagram for group-level metabolic cell communication (MCC).
pl.plot_group_communication_compare_hierarchy_diagram(...)	Plot a hierarchy-style diagram comparing group-level MCC between two conditions.
pl.plot_MSpair_contribute_group(adata[, ...])	Plot a heatmap showing group-level contributions of metabolite–sensor pairs for a specific metabolite.
pl.plot_summary_pathway([ms_result, ...])	Plot a Sankey diagram summarizing metabolic cell communication between metabolite and sensor pathways.
pl.plot_metapathway_pair_contribution_bubbleplot(...)	Plot a bubble chart showing metabolite–sensor contributions for a selected metabolic pathway.
pl.plot_communication_responseGenes(df_deg, ...)	Plot the smoothed gene expression profiles of metabolic cell communication response genes.
pl.plot_communication_responseGenes_keggEnrich(...)	Plot a horizontal bar chart summarizing KEGG enrichment results of MCC response genes.
pl.plot_DEG_volcano(deg_result[, name_col, ...])	Plot a volcano plot from differential MCC results.
pl.plot_3d_feature(adata, feature[, ...])	Visualize a spatial feature (gene or obs annotation) in 3D using Plotly.
pl.plot_3d_LRC_with_two_slices(adata, mask_key)	Visualize 3D long-range channels (LRCs) with two representative z-slice views.
pl.plot_dis_thr(adata, dis_thr, spot_index)	Visualize spatial neighborhood within a specified distance threshold around a selected spot.
pl.plot_LRC_markers(adata, LRC_name, ...[, ...])	Visualize expression of LRC (Long-Range Channel) marker genes in spatial omics data.
pl.plot_spot_distance(adata, ...[, figsize, ...])	Visualize the spatial distance from a selected spot to all other spots.
pl.plot_graph_connectivity(G[, node_size, ...])	Plot a 2D visualization of a graph showing connectivity between nodes and edges.
pl.plot_direction_similarity(df_direction, ...)	Plot a block-ordered similarity heatmap for direction-based flow clusters.