DeltaTopic.nn.modelhub.DeltaTopic

class DeltaTopic.nn.modelhub.DeltaTopic(adata_seq: anndata.AnnData, n_latent: int = 32, **model_kwargs)[source]

Dynamically-Encoded Latent Transcriptomic pattern Analysis by Topic modelling (DeltaTopic).

Parameters:

adata – AnnData object that has been registered via setup_anndata().
n_latent – Dimensionality of the latent space
**model_kwargs – Keyword args for DeltaTopic_module

Examples

>>> adata= anndata.read_h5ad(path_to_anndata_spliced)
>>> X_unspliced = sc.read(path_to_anndata_spliced)
>>> adata.obsm["unspliced_expression"] = (X_unspliced.X.copy()
>>> DeltaTopic.nn.util.setup_anndata(adata, layer="counts", unspliced_obsm_key = "unspliced_expression")
>>> model = DeltaTopic.nn.modelhub.DeltaTopic(adata)
>>> model.train(100)

__init__(adata_seq: anndata.AnnData, n_latent: int = 32, **model_kwargs)[source]

Methods

`__init__`(adata_seq[, n_latent])
`get_latent_representation`
`get_parameters`
`get_reconstruction_error`
`load`(dir_path[, adata_seq, use_gpu])	Instantiate a model from the saved output.
`save`(dir_path[, overwrite, save_anndata])	Save the state of the model.
`to_device`(device)	Move model to device.
`train`([max_epochs, lr, use_gpu, train_size, ...])	Trains the model using amortized variational inference.

Attributes

`device`	Device model is on.
`history`	Returns computed metrics during training.
`is_trained`
`test_indices`
`train_indices`
`validation_indices`