tangram.mapping_utils.map_cells_to_space

tangram.mapping_utils.map_cells_to_space(adata_sc, adata_sp, cv_train_genes=None, cluster_label=None, mode='cells', device='cpu', learning_rate=0.1, num_epochs=1000, scale=True, lambda_d=0, lambda_g1=1, lambda_g2=0, lambda_r=0, lambda_count=1, lambda_f_reg=1, target_count=None, random_state=None, verbose=True, density_prior='rna_count_based')

Map single cell data (adata_sc) on spatial data (adata_sp).

Parameters

  • adata_sc (AnnData) – single cell data

  • adata_sp (AnnData) – gene spatial data

  • cv_train_genes (list) – Optional. Training gene list. Default is None.

  • cluster_label (str) – Optional. Field in adata_sc.obs used for aggregating single cell data. Only valid for mode=clusters.

  • mode (str) – Optional. Tangram mapping mode. Currently supported: ‘cell’, ‘clusters’, ‘constrained’. Default is ‘cell’.

  • device (string or torch.device) – Optional. Default is ‘cpu’.

  • learning_rate (float) – Optional. Learning rate for the optimizer. Default is 0.1.

  • num_epochs (int) – Optional. Number of epochs. Default is 1000.

  • scale (bool) – Optional. Whether weight input single cell data by the number of cells in each cluster, only valid when cluster_label is not None. Default is True.

  • lambda_d (float) – Optional. Hyperparameter for the density term of the optimizer. Default is 0.

  • lambda_g1 (float) – Optional. Hyperparameter for the gene-voxel similarity term of the optimizer. Default is 1.

  • lambda_g2 (float) – Optional. Hyperparameter for the voxel-gene similarity term of the optimizer. Default is 0.

  • lambda_r (float) – Optional. Strength of entropy regularizer. An higher entropy promotes probabilities of each cell peaked over a narrow portion of space. lambda_r = 0 corresponds to no entropy regularizer. Default is 0.

  • lambda_count (float) – Optional. Regularizer for the count term. Default is 1. Only valid when mode == ‘constrained’

  • lambda_f_reg (float) – Optional. Regularizer for the filter, which promotes Boolean values (0s and 1s) in the filter. Only valid when mode == ‘constrained’. Default is 1.

  • target_count (int) – Optional. The number of cells to be filtered. Default is None.

  • random_state (int) – Optional. pass an int to reproduce training. Default is None.

  • verbose (bool) – Optional. If print training details. Default is True.

  • density_prior (str, ndarray or None) – Spatial density of spots, when is a string, value can be ‘rna_count_based’ or ‘uniform’, when is a ndarray, shape = (number_spots,). This array should satisfy the constraints sum() == 1. If None, the density term is ignored. Default value is ‘rna_count_based’.

Returns

a cell-by-spot AnnData containing the probability of mapping cell i on spot j. The uns field of the returned AnnData contains the training genes.