pyXenium.pathway.pathway_topology_analysis#

pathway_topology_analysis(*, pathway_definitions, reference_df=None, expression_df=None, output_dir=None, adata=None, tbc_results=None, t_and_c_df=None, cluster_col='cluster', cell_id_col='cell_id', x_col='x', y_col='y', celltype_col='celltype', scoring_method='weighted_sum', view='intrinsic', structure_map=None, structure_map_df=None, anchor_mode='precomputed', pathway_modes=('gene_topology_aggregate', 'activity_point_cloud'), primary_pathway_mode='gene_topology_aggregate', pathway_aggregate='weighted_median', activity_threshold_schedule=(0.95, 0.90, 0.80, 0.70, 0.60, 0.50), min_activity_cells=50, entity_min_weight=0.0, k_neighbors=8, radius=None, topology_method='average', hotspot_quantile=0.9, export_figures=True, use_raw=False)#

Compute pathway-level topology in two coordinated views: gene-topology aggregate and activity point cloud.

Parameters:

  • pathway_definitions (Mapping[str, Any] | DataFrame)

  • reference_df (DataFrame | None)

  • expression_df (DataFrame | None)

  • output_dir (str | Path | None)

  • adata (Any)

  • tbc_results (str | Path | None)

  • t_and_c_df (DataFrame | None)

  • cluster_col (str)

  • cell_id_col (str)

  • x_col (str)

  • y_col (str)

  • celltype_col (str)

  • scoring_method (str)

  • view (str)

  • structure_map (DataFrame | None)

  • structure_map_df (DataFrame | None)

  • anchor_mode (str)

  • pathway_modes (Sequence[str])

  • primary_pathway_mode (str)

  • pathway_aggregate (str)

  • activity_threshold_schedule (Sequence[float])

  • min_activity_cells (int)

  • entity_min_weight (float)

  • k_neighbors (int)

  • radius (float | None)

  • topology_method (str)

  • hotspot_quantile (float)

  • export_figures (bool)

  • use_raw (bool)

Return type:

dict[str, Any]