Learn Embedding

  >>>embs = loaded_data.learn_embedding(
  ... dim=2, similarity_method='inner_product', embedding_method='MDS',
  ... n_strata=None, aggregation='median', return_distance=False
  ... )

This function will return the embeddings in the format of a numpy array with shape ( # of cells, # of dimensions).

• dim (int): the dimension for the embedding
• similarity_method (str): reproducibility measure, ‘InnerProduct’, ‘HiCRep’ or ‘Selfish’. Default: ‘InnerProduct’
• embedding_method (str): ‘MDS’, ‘tSNE’ or ‘UMAP’
• n_strata (int): only consider contacts within this genomic distance. Default: None. If it is None, it will use the all strata kept (the argument keep_n_strata from previous loading process). Thus n_strata and keep_n_strata (loading step) cannot be None at the same time.
• aggregation (str): method to aggregate different chromosomes, ‘mean’ or ‘median’. Default: ‘median’.
• return_distance (bool): if True, return (embeddings, distance_matrix); if False, only return embeddings. Default: False.
• Some additional argument for Selfish:
  • n_windows (int): split contact map into n windows, default: 10
  • sigma (float): sigma in the Gaussian-like kernel: default: 1.6