netmap.downstream.regulon¶

Regulon selection and signed regulon construction from GRN attribution data.

select_top_edges() and select_top_edges_signed() select high-confidence edges per source TF per cluster, filtered by neighbourhood co-expression support. The signed variant uses cluster-wise Spearman correlation to split edges into positive and negative regulons.

Functions

`aggregate_edges`	Aggregate regulon attribution scores across edges.
`aggregate_edges_arbitrary`	Aggregate attribution scores for arbitrary named edge sets.
`compute_signatures_UCell_scores`	Compute UCell gene set enrichment scores for regulon signatures.
`get_overlapping_signatures`	Compute pairwise overlap status of target genes across clusters.
`get_sourcewise_jaccard_regulons`	Compute per-source-gene Jaccard similarity matrices across clusters.
`jaccard_similarity`	Compute the Jaccard similarity coefficient between two sets.
`load_edge_dict_from_dataframe`	Reconstruct a nested edge dictionary from a flat regulon DataFrame.
`make_cluster_regulon_dataframe`	Concatenate all regulon edge DataFrames into a flat DataFrame.
`make_cluster_regulon_dataframe_directional`
`make_global_target_similarity_plot`	Plot a heatmap of the mean pairwise Jaccard similarity across all source genes.
`process_cell_edges`	Organise per-cluster edge lists into a nested results dictionary.
`process_cell_edges_signed`	Build the positive/negative regulon result structure from per-cluster edge lists.
`select_top_edges`	Select the top-attribution edges per source TF per cluster.
`select_top_edges_signed`	Select top edges per source TF per cluster and split them into positive and negative regulons based on cluster-wise Spearman correlation.