scvi.external.SCBASSET#

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

Reimplementation of ScBasset [Yuan and Kelley, 2022] for representation learning of scATAC-seq data.

This implementation is EXPERIMENTAL. 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()

Attributes table#

`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.
`history`	Returns computed metrics during training.
`is_trained`	Whether the model has been trained.
`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.
`get_anndata_manager`(adata[, required])	Retrieves the `AnnDataManager` for a given AnnData object specific to this model instance.
`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.
`load`(dir_path[, adata, use_gpu, prefix, ...])	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_anndata])	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, use_gpu, train_size, ...])	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#

adata

SCBASSET.adata[source]#: Data attached to model instance.

adata_manager

SCBASSET.adata_manager[source]#: Manager instance associated with self.adata.

device

SCBASSET.device[source]#: The current device that the module’s params are on.

history

SCBASSET.history[source]#: Returns computed metrics during training.

is_trained

SCBASSET.is_trained[source]#: Whether the model has been trained.

test_indices

SCBASSET.test_indices[source]#: Observations that are in test set.

train_indices

SCBASSET.train_indices[source]#: Observations that are in train set.

validation_indices

SCBASSET.validation_indices[source]#: Observations that are in validation set.

Methods#

convert_legacy_save

classmethod SCBASSET.convert_legacy_save(dir_path, output_dir_path, overwrite=False, prefix=None)[source]#

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 (Optional[str] (default: None)) – Prefix of saved file names.

Return type:

None

get_anndata_manager

SCBASSET.get_anndata_manager(adata, required=False)[source]#

Retrieves the AnnDataManager for a given AnnData object specific to this model instance.

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

Parameters:

adata (Union[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:

Optional[AnnDataManager]

get_cell_bias

SCBASSET.get_cell_bias()[source]#

Returns the cell-specific bias term.

Return type:: ndarray
Returns:: bias (n_cells,)

get_from_registry

SCBASSET.get_from_registry(adata, registry_key)[source]#

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 (Union[AnnData, MuData]) – AnnData to pull data from.

Return type:

ndarray

Returns:

The requested data as a NumPy array.

get_latent_representation

SCBASSET.get_latent_representation()[source]#

Returns the latent representation of the cells.

Return type:: ndarray
Returns:: latent representation (n_cells, n_latent)

load

classmethod SCBASSET.load(dir_path, adata=None, use_gpu=None, prefix=None, backup_url=None)[source]#

Instantiate a model from the saved output.

Parameters:

dir_path (str) – Path to saved outputs.
adata (Union[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.
use_gpu (Union[str, int, bool, None] (default: None)) – Load model on default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str), or use CPU (if False).
prefix (Optional[str] (default: None)) – Prefix of saved file names.
backup_url (Optional[str] (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) # use the name of the model class used to save
>>> model.get_....

load_registry

static SCBASSET.load_registry(dir_path, prefix=None)[source]#

Return the full registry saved with the model.

Parameters:

dir_path (str) – Path to saved outputs.
prefix (Optional[str] (default: None)) – Prefix of saved file names.

Return type:

dict

Returns:

The full registry saved with the model

register_manager

classmethod SCBASSET.register_manager(adata_manager)[source]#

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.

save

SCBASSET.save(dir_path, prefix=None, overwrite=False, save_anndata=False, **anndata_write_kwargs)[source]#

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 (Optional[str] (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
anndata_write_kwargs – Kwargs for write()

setup_anndata

classmethod SCBASSET.setup_anndata(adata, dna_code_key, layer=None, batch_key=None, **kwargs)[source]#

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 (Optional[str] (default: None)) – if not None, uses this as the key in adata.layers for raw count data.
batch_key (Optional[str] (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().

to_device

SCBASSET.to_device(device)[source]#

Move model to device.

Parameters:: device (Union[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

train

SCBASSET.train(max_epochs=1000, lr=0.01, use_gpu=None, train_size=0.9, validation_size=None, batch_size=128, early_stopping=True, early_stopping_monitor='auroc_train', early_stopping_mode='max', early_stopping_min_delta=1e-06, plan_kwargs=None, **trainer_kwargs)[source]#

Train the model.

Parameters:

max_epochs (int (default: 1000)) – Number of epochs to train for
lr (float (default: 0.01)) – Learning rate for optimization.
use_gpu (Union[str, int, bool, None] (default: None)) – Use default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str, e.g., ‘cuda:0’), or use CPU (if False).
train_size (float (default: 0.9)) – Size of training set in the range [0.0, 1.0].
validation_size (Optional[float] (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.
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.
plan_kwargs (Optional[dict] (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.

view_anndata_setup

SCBASSET.view_anndata_setup(adata=None, hide_state_registries=False)[source]#

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

Parameters:

adata (Union[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

view_setup_args

static SCBASSET.view_setup_args(dir_path, prefix=None)[source]#

Print args used to setup a saved model.

Parameters:

dir_path (str) – Path to saved outputs.
prefix (Optional[str] (default: None)) – Prefix of saved file names.

Return type:

None