SpatialQuery.spatial_query_multi.motif_enrichment_knn

spatial_query_multi.motif_enrichment_knn(ct, motifs=None, dataset=None, k=30, min_support=0.5, max_dist=20, return_cellID=False)[source]

Perform motif enrichment analysis using k-nearest neighbors (KNN) in multiple fields of view.

Parameters:

  • ct (str) – The cell type of the center cell.

  • motifs (str or List[str] or List[List[str]], optional) – Specified motifs to be tested. If motifs=None, find the frequent patterns as motifs within the neighborhood of center cell type in each fov.

  • dataset (str or List[str], optional) – Datasets for searching for frequent patterns and performing enrichment analysis. Use all datasets if dataset=None.

  • k (int, default=30) – Number of nearest neighbors to consider.

  • min_support (float, default=0.5) – Threshold of frequency to consider a pattern as a frequent pattern.

  • max_dist (float, default=20) – Maximum distance for neighbors.

  • return_cellID (bool, default=False) – Indicate whether return cell IDs for each frequent pattern within the neighborhood of center cell type and center cells. By defaults do not return cell ID.

Returns:

If return_cellID is False:
pd.DataFrame with motif enrichment results. Columns include:

  • center: center cell type name

  • motifs: list of cell types in the motif

  • n_center_motif: number of center cells with motif in neighborhood

  • n_center: total number of center cells

  • n_motif: total number of cells with motif in neighborhood

  • expectation: expected number under hypergeometric distribution

  • p-values: p-value from hypergeometric test

  • adj-pval: FDR-corrected p-values (when multiple motifs tested)

  • if_significant: whether the enrichment is significant

If return_cellID is True:
Tuple containing:

  • pd.DataFrame: enrichment results as described above

  • Dict: cell IDs of motifs in center cell’s neighborhood Format: {‘motif_str’: {‘dataset_name’: [cell_ids]}}

  • Dict: cell IDs of center cells with motif in neighborhood Format: {‘motif_str’: {‘dataset_name’: [cell_ids]}}

Return type:

Union[pd.DataFrame, Tuple[pd.DataFrame, Dict, Dict]]