scvi.external.SCBASSET

class scvi.external.SCBASSET(adata, n_bottleneck_layer=32, l2_reg_cell_embedding=0.0, **model_kwargs)

EXPERIMENTAL Reimplementation of scBasset [Yuan and Kelley, 2022].

Performs representation learning of scATAC-seq data. Original implementation: calico/scBasset.

We are working to measure the performance of this model compared to the original.

Parameters:

• adata (AnnData) – single-cell AnnData object that has been registered via setup_anndata().

• n_bottleneck_layer (int (default: 32)) – Size of the bottleneck layer

• l2_reg_cell_embedding (float (default: 0.0)) – L2 regularization for the cell embedding layer. A value, e.g. 1e-8 can be used to improve integration performance.

• **model_kwargs – Keyword args for ScBassetModule

Examples

>>> adata = anndata.read_h5ad(path_to_sc_anndata)
>>> scvi.data.add_dna_sequence(adata)
>>> adata = adata.transpose()  # regions by cells
>>> scvi.external.SCBASSET.setup_anndata(adata, dna_code_key="dna_code")
>>> model = scvi.external.SCBASSET(adata)
>>> model.train()
>>> adata.varm["X_scbasset"] = model.get_latent_representation()

Notes

See further usage examples in the following tutorials:

1. ScBasset: Analyzing scATACseq data

2. scBasset: Batch correction of scATACseq data

Attributes table

DEFAULT_MOTIF_DIR
MOTIF_URLS
adata	Data attached to model instance.
adata_manager	Manager instance associated with self.adata.
device	The current device that the module's params are on.
get_normalized_function_name	What the get normalized functions name is
history	Returns computed metrics during training.
is_trained	Whether the model has been trained.
summary_string	Summary string of the model.
test_indices	Observations that are in test set.
train_indices	Observations that are in train set.
validation_indices	Observations that are in validation set.

Methods table

convert_legacy_save(dir_path, output_dir_path)	Converts a legacy saved model (<v0.15.0) to the updated save format.
deregister_manager([adata])	Deregisters the AnnDataManager instance associated with adata.
get_anndata_manager(adata[, required])	Retrieves the AnnDataManager for a given AnnData object.
get_cell_bias()	Returns the cell-specific bias term.
get_from_registry(adata, registry_key)	Returns the object in AnnData associated with the key in the data registry.
get_latent_representation()	Returns the latent representation of the cells.
get_normalized_expression()
get_tf_activity(tf[, genome, motif_dir, ...])	Infer transcription factor activity using a motif injection procedure.
load(dir_path[, adata, accelerator, device, ...])	Instantiate a model from the saved output.
load_registry(dir_path[, prefix])	Return the full registry saved with the model.
register_manager(adata_manager)	Registers an AnnDataManager instance with this model class.
save(dir_path[, prefix, overwrite, ...])	Save the state of the model.
setup_anndata(adata, dna_code_key[, layer, ...])	Sets up the AnnData object for this model.
to_device(device)	Move model to device.
train([max_epochs, lr, accelerator, ...])	Train the model.
view_anndata_setup([adata, ...])	Print summary of the setup for the initial AnnData or a given AnnData object.
view_setup_args(dir_path[, prefix])	Print args used to setup a saved model.

Attributes

SCBASSET.DEFAULT_MOTIF_DIR = './scbasset_motifs/'

SCBASSET.MOTIF_URLS = {'human': ('https://storage.googleapis.com/scbasset_tutorial_data/Homo_sapiens_motif_fasta.tar.gz', 'Homo_sapiens_motif_fasta')}

SCBASSET.adata

Data attached to model instance.

SCBASSET.adata_manager

Manager instance associated with self.adata.

SCBASSET.device

The current device that the module’s params are on.

SCBASSET.get_normalized_function_name

What the get normalized functions name is

SCBASSET.history

Returns computed metrics during training.

SCBASSET.is_trained

Whether the model has been trained.

SCBASSET.summary_string

Summary string of the model.

SCBASSET.test_indices

Observations that are in test set.

SCBASSET.train_indices

Observations that are in train set.

SCBASSET.validation_indices

Observations that are in validation set.

Methods

classmethod SCBASSET.convert_legacy_save(dir_path, output_dir_path, overwrite=False, prefix=None, **save_kwargs)

Converts a legacy saved model (<v0.15.0) to the updated save format.

Parameters:

• dir_path (str) – Path to directory where legacy model is saved.

• output_dir_path (str) – Path to save converted save files.

• overwrite (bool (default: False)) – Overwrite existing data or not. If False and directory already exists at output_dir_path, error will be raised.

• prefix (str | None (default: None)) – Prefix of saved file names.

• **save_kwargs – Keyword arguments passed into save().

Return type:

None

SCBASSET.deregister_manager(adata=None)

Deregisters the AnnDataManager instance associated with adata.

If adata is None, deregisters all AnnDataManager instances in both the class and instance-specific manager stores, except for the one associated with this model instance.

SCBASSET.get_anndata_manager(adata, required=False)

Retrieves the AnnDataManager for a given AnnData object.

Requires self.id has been set. Checks for an AnnDataManager specific to this model instance.

Parameters:

• adata (AnnData | MuData) – AnnData object to find manager instance for.

• required (bool (default: False)) – If True, errors on missing manager. Otherwise, returns None when manager is missing.

Return type:

AnnDataManager | None

SCBASSET.get_cell_bias()

Returns the cell-specific bias term.

Return type:

ndarray

Returns:

bias (n_cells,)

SCBASSET.get_from_registry(adata, registry_key)

Returns the object in AnnData associated with the key in the data registry.

AnnData object should be registered with the model prior to calling this function via the self._validate_anndata method.

Parameters:

• registry_key (str) – key of object to get from data registry.

• adata (AnnData | MuData) – AnnData to pull data from.

Return type:

ndarray

Returns:

The requested data as a NumPy array.

SCBASSET.get_latent_representation()

Returns the latent representation of the cells.

Return type:

ndarray

Returns:

latent representation (n_cells, n_latent)

SCBASSET.get_normalized_expression()

SCBASSET.get_tf_activity(tf, genome='human', motif_dir=None, lib_size_norm=True, batch_size=256)

Infer transcription factor activity using a motif injection procedure.

Parameters:

• tf (str) – transcription factor name. must be provided in the relevant motif repository.

• genome (str (default: 'human')) – species name for the motif injection procedure. Currently, only “human” is supported.

• motif_dir (str | None (default: None)) – path for the motif library. Will download if not already present.

• lib_size_norm (bool | None (default: True)) – normalize accessibility scores for library size by substracting the cell bias term from each accessibility score prior to comparing motif scores to background scores.

• batch_size (int (default: 256)) – minibatch size for TF activity inference.

Return type:

ndarray

Returns:

tf_score [cells,] TF activity scores.

Notes

scBasset infers TF activities by injecting known TF motifs into a shuffled dinucleotide sequence and computing the change in accessibility predicted between the injected motif and a randomized background sequence. See [Yuan and Kelley, 2022] for details. We modeled this function off the original implementation in scbasset.

calico/scBasset

classmethod SCBASSET.load(dir_path, adata=None, accelerator='auto', device='auto', prefix=None, backup_url=None)

Instantiate a model from the saved output.

Parameters:

• dir_path (str) – Path to saved outputs.

• adata (AnnData | MuData | None (default: None)) – AnnData organized in the same way as data used to train model. It is not necessary to run setup_anndata, as AnnData is validated against the saved scvi setup dictionary. If None, will check for and load anndata saved with the model.

• accelerator (str (default: 'auto')) – Supports passing different accelerator types (“cpu”, “gpu”, “tpu”, “ipu”, “hpu”, “mps, “auto”) as well as custom accelerator instances.

• device (int | str (default: 'auto')) – The device to use. Can be set to a non-negative index (int or str) or “auto” for automatic selection based on the chosen accelerator. If set to “auto” and accelerator is not determined to be “cpu”, then device will be set to the first available device.

• prefix (str | None (default: None)) – Prefix of saved file names.

• backup_url (str | None (default: None)) – URL to retrieve saved outputs from if not present on disk.

Returns:

Model with loaded state dictionaries.

Examples

>>> model = ModelClass.load(save_path, adata)
>>> model.get_....

static SCBASSET.load_registry(dir_path, prefix=None)

Return the full registry saved with the model.

Parameters:

• dir_path (str) – Path to saved outputs.

• prefix (str | None (default: None)) – Prefix of saved file names.

Return type:

dict

Returns:

The full registry saved with the model

classmethod SCBASSET.register_manager(adata_manager)

Registers an AnnDataManager instance with this model class.

Stores the AnnDataManager reference in a class-specific manager store. Intended for use in the setup_anndata() class method followed up by retrieval of the AnnDataManager via the _get_most_recent_anndata_manager() method in the model init method.

Notes

Subsequent calls to this method with an AnnDataManager instance referring to the same underlying AnnData object will overwrite the reference to previous AnnDataManager.

SCBASSET.save(dir_path, prefix=None, overwrite=False, save_anndata=False, save_kwargs=None, legacy_mudata_format=False, **anndata_write_kwargs)

Save the state of the model.

Neither the trainer optimizer state nor the trainer history are saved. Model files are not expected to be reproducibly saved and loaded across versions until we reach version 1.0.

Parameters:

• dir_path (str) – Path to a directory.

• prefix (str | None (default: None)) – Prefix to prepend to saved file names.

• overwrite (bool (default: False)) – Overwrite existing data or not. If False and directory already exists at dir_path, error will be raised.

• save_anndata (bool (default: False)) – If True, also saves the anndata

• save_kwargs (dict | None (default: None)) – Keyword arguments passed into save().

• legacy_mudata_format (bool (default: False)) – If True, saves the model var_names in the legacy format if the model was trained with a MuData object. The legacy format is a flat array with variable names across all modalities concatenated, while the new format is a dictionary with keys corresponding to the modality names and values corresponding to the variable names for each modality.

• anndata_write_kwargs – Kwargs for write()

classmethod SCBASSET.setup_anndata(adata, dna_code_key, layer=None, batch_key=None, **kwargs)

Sets up the AnnData object for this model.

A mapping will be created between data fields used by this model to their respective locations in adata. None of the data in adata are modified. Only adds fields to adata.

Parameters:

• adata (AnnData) – AnnData object. Rows represent cells, columns represent features.

• dna_code_key (str) – Key in adata.obsm with dna sequences encoded as integer code.

• layer (str | None (default: None)) – if not None, uses this as the key in adata.layers for raw count data.

• batch_key (str | None (default: None)) – key in adata.var for batch information. Categories will automatically be converted into integer categories and saved to adata.var[‘_scvi_batch’]. If None, assigns the same batch to all the data.

Notes

The adata object should be in the regions by cells format. This is due to scBasset considering regions as observations and cells as variables. This can be simply achieved by transposing the data, bdata = adata.transpose().

SCBASSET.to_device(device)

Move model to device.

Parameters:

device (str | int) – Device to move model to. Options: ‘cpu’ for CPU, integer GPU index (eg. 0), or ‘cuda:X’ where X is the GPU index (eg. ‘cuda:0’). See torch.device for more info.

Examples

>>> adata = scvi.data.synthetic_iid()
>>> model = scvi.model.SCVI(adata)
>>> model.to_device("cpu")  # moves model to CPU
>>> model.to_device("cuda:0")  # moves model to GPU 0
>>> model.to_device(0)  # also moves model to GPU 0

SCBASSET.train(max_epochs=1000, lr=0.01, accelerator='auto', devices='auto', train_size=None, validation_size=None, shuffle_set_split=True, batch_size=128, early_stopping=True, early_stopping_monitor='auroc_train', early_stopping_mode='max', early_stopping_min_delta=1e-06, datasplitter_kwargs=None, plan_kwargs=None, **trainer_kwargs)

Train the model.

Parameters:

• max_epochs (int (default: 1000)) – Number of epochs to train for

• lr (float (default: 0.01)) – Learning rate for optimization.

• accelerator (str (default: 'auto')) – Supports passing different accelerator types (“cpu”, “gpu”, “tpu”, “ipu”, “hpu”, “mps, “auto”) as well as custom accelerator instances.

• devices (int | list[int] | str (default: 'auto')) – The devices to use. Can be set to a non-negative index (int or str), a sequence of device indices (list or comma-separated str), the value -1 to indicate all available devices, or “auto” for automatic selection based on the chosen accelerator. If set to “auto” and accelerator is not determined to be “cpu”, then devices will be set to the first available device.

• train_size (float | None (default: None)) – Size of training set in the range [0.0, 1.0].

• validation_size (float | None (default: None)) – Size of the test set. If None, defaults to 1 - train_size. If train_size + validation_size < 1, the remaining cells belong to a test set.

• shuffle_set_split (bool (default: True)) – Whether to shuffle indices before splitting. If False, the val, train, and test set are split in the sequential order of the data according to validation_size and train_size percentages.

• batch_size (int (default: 128)) – Minibatch size to use during training.

• early_stopping (bool (default: True)) – Perform early stopping. Additional arguments can be passed in **kwargs. See Trainer for further options.

• early_stopping_monitor (str (default: 'auroc_train')) – Metric logged during validation set epoch. The available metrics will depend on the training plan class used. We list the most common options here in the typing.

• early_stopping_mode (Literal['min', 'max'] (default: 'max')) – In ‘min’ mode, training will stop when the quantity monitored has stopped decreasing and in ‘max’ mode it will stop when the quantity monitored has stopped increasing.

• early_stopping_min_delta (float (default: 1e-06)) – Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than min_delta, will count as no improvement.

• datasplitter_kwargs (dict | None (default: None)) – Additional keyword arguments passed into DataSplitter.

• plan_kwargs (dict | None (default: None)) – Keyword args for TrainingPlan. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.

• **trainer_kwargs – Other keyword args for Trainer.

SCBASSET.view_anndata_setup(adata=None, hide_state_registries=False)

Print summary of the setup for the initial AnnData or a given AnnData object.

Parameters:

• adata (AnnData | MuData | None (default: None)) – AnnData object setup with setup_anndata or transfer_fields().

• hide_state_registries (bool (default: False)) – If True, prints a shortened summary without details of each state registry.

Return type:

None

static SCBASSET.view_setup_args(dir_path, prefix=None)

Print args used to setup a saved model.

Parameters:

• dir_path (str) – Path to saved outputs.

• prefix (str | None (default: None)) – Prefix of saved file names.

Return type:

None