simba.tl.find_target_genes

simba.tl.find_target_genes(adata_all, adata_PM, list_tf_motif=None, list_tf_gene=None, adata_CP=None, metric='euclidean', anno_filter='entity_anno', filter_peak='peak', filter_gene='gene', n_genes=200, cutoff_gene=None, cutoff_peak=1000, use_precomputed=True)[source]

For a given TF, infer its target genes

Parameters:

  • adata_all (AnnData) – Anndata object storing SIMBA embedding of all entities.

  • adata_PM (AnnData) – Peaks-by-motifs anndata object.

  • list_tf_motif (list) – A list of TF motifs. They should match TF motifs in list_tf_gene.

  • list_tf_gene (list) – A list TF genes. They should match TF motifs in list_tf_motif.

  • adata_CP (AnnData, optional (default: None)) – When use_precomputed is True, it can be set None

  • metric (str, optional (default: “euclidean”)) – The distance metric to use. It can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, ‘correlation’, ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘jensenshannon’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘wminkowski’, ‘yule’.

  • anno_filter (str, optional (default: None)) – The annotation of filter to use. It should be one of adata.obs_keys()

  • filter_gene (str, optional (default: None)) – The filter for gene. It should be in adata.obs[anno_filter]

  • filter_peak (str, optional (default: None)) – The filter for peak. It should be in adata.obs[anno_filter]

  • n_genes (int, optional (default: 200)) – The number of neighbor genes to consider initially around TF gene or TF motif

  • cutoff_gene (float, optional (default: None)) – Cutoff of “average_rank”

  • cutoff_peak (int, optional (default: 1000)) – Cutoff for peaks-associated ranks, including “rank_peak_to_gene” and “rank_peak_to_TFmotif”.

  • use_precomputed (bool, optional (default: True)) – Distances calculated between genes, peaks, and motifs (stored in adata.uns[‘tf_targets’]) will be imported

Returns:

  • dict_tf_targets (dict) – Target genes for each TF.

  • updates adata with the following fields.

  • tf_targets (dict, (adata.uns[‘tf_targets’])) – Distances calculated between genes, peaks, and motifs