terra.get_spatial_score

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'.

Return type:

dict

Returns:

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

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