scdori.get_tf_expression

scdori.get_tf_expression#

scdori.get_tf_expression(tf_expression_mode, model, device, train_loader, rna_anndata, atac_anndata, num_cells, tf_indices, encoding_batch_onehot, config_file)#

Compute TF expression per topic.

If tf_expression_mode is “True”, this function computes the mean TF expression for the top-k cells in each topic. Otherwise, it uses a normalized topic-TF decoder matrix from the model.

Parameters:

  • tf_expression_mode (str) – Mode for TF expression. “True” calculates per-topic TF expression from top-k cells, “latent” uses the topic-TF decoder matrix.

  • model (torch.nn.Module) – The scDoRI model containing encoder and decoder modules.

  • device (torch.device) – The device (CPU or CUDA) used for PyTorch tensors.

  • train_loader (DataLoader) – DataLoader for training data.

  • rna_anndata (anndata.AnnData) – RNA single-cell data in AnnData format.

  • atac_anndata (anndata.AnnData) – ATAC single-cell data in AnnData format.

  • num_cells (np.ndarray) – number of cells constituting each input metacell, set to 1 for single cell data.

  • tf_indices (list of int) – Indices of TF features in the RNA data.

  • encoding_batch_onehot (np.ndarray) – One-hot encoding for batch information.

  • config_file (python file) – Configuration object with model training.

Returns:

torch.Tensor A (num_topics x num_tfs) tensor of TF expression values for each topic.

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