terra.get_spatial_score

terra.get_spatial_score(dataset, model_folder_path, emb_layer=None, cell_gene_ensembl_id=[], neighborhood_gene_ensembl_id=[], batch_size=128, pin_memory=False, num_workers=12, compute_cosine_with_list=['cell', 'neighborhood'])

Compute and return cosine similarity matrix for specified gene IDs.

Parameters:

• dataset (Dataset) – Tokenized Hugging Face dataset.

• model_folder_path (str) – Path to the folder containing the model config, token dictionary, and normalization factors.

• emb_layer (Optional[int] (default: None)) – Layer for which to retrieve the embedding.

• cell_gene_ensembl_id (list (default: [])) – List with gene IDs for which cell gene embeddings will be retrieved.

• neighborhood_gene_ensembl_id (list (default: [])) – List with gene IDs for which neighborhood gene embeddings will be retrieved.

• batch_size (int (default: 128)) – Dataloader param.

• pin_memory (bool (default: False)) – Dataloader param.

• num_workers (int (default: 12)) – Number of workers used.

• compute_cosine_with_list (list[str] (default: ['cell', 'neighborhood'])) – Items with which to compute cosine similarity; may contain 'cell' and/or 'neighborhood'.

Returns:

• cos_sim_dict (dict) – Dictionary containing cosine similarity statistics as numpy arrays.