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) – Size of the bottleneck layer
l2_reg_cell_embedding (float) – 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#
Data attached to model instance. |
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Manager instance associated with self.adata. |
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The current device that the module's params are on. |
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Returns computed metrics during training. |
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Whether the model has been trained. |
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Observations that are in test set. |
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Observations that are in train set. |
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Observations that are in validation set. |
Methods table#
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Converts a legacy saved model (<v0.15.0) to the updated save format. |
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Retrieves the |
Returns the cell-specific bias term. |
|
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Returns the object in AnnData associated with the key in the data registry. |
Returns the latent representation of the cells. |
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Instantiate a model from the saved output. |
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Return the full registry saved with the model. |
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Registers an |
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Save the state of the model. |
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Sets up the |
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Move model to device. |
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Train the model. |
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Print summary of the setup for the initial AnnData or a given AnnData object. |
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Print args used to setup a saved model. |
Attributes#
adata
adata_manager
device
history
is_trained
test_indices
train_indices
validation_indices
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.
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 anAnnDataManager
specific to this model instance.
get_cell_bias
- SCBASSET.get_cell_bias()[source]#
Returns the cell-specific bias term.
- Returns:
bias (n_cells,)
- Return type:
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.
get_latent_representation
- SCBASSET.get_latent_representation()[source]#
Returns the latent representation of the cells.
- Returns:
latent representation (n_cells, n_latent)
- Return type:
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 (Optional[Union[AnnData, MuData]]) – 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 (Optional[Union[str, int, bool]]) – 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).
backup_url (Optional[str]) – 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.
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 thesetup_anndata()
class method followed up by retrieval of theAnnDataManager
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 previousAnnDataManager
.- Parameters:
adata_manager (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]) – Prefix to prepend to saved file names.
overwrite (bool) – Overwrite existing data or not. If
False
and directory already exists atdir_path
, error will be raised.save_anndata (bool) – 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]) – if not
None
, uses this as the key inadata.layers
for raw count data.batch_key (Optional[str]) – key in
adata.var
for batch information. Categories will automatically be converted into integer categories and saved toadata.var['_scvi_batch']
. IfNone
, 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) – Number of epochs to train for
lr (float) – Learning rate for optimization.
use_gpu (Optional[Union[str, int, bool]]) – 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) – Size of training set in the range [0.0, 1.0].
validation_size (Optional[float]) – Size of the test set. If
None
, defaults to 1 -train_size
. Iftrain_size + validation_size < 1
, the remaining cells belong to a test set.batch_size (int) – Minibatch size to use during training.
early_stopping (bool) – Perform early stopping. Additional arguments can be passed in
**kwargs
. SeeTrainer
for further options.early_stopping_monitor (str) – 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']) – 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) – 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]) – Keyword args for
TrainingPlan
. Keyword arguments passed totrain()
will overwrite values present inplan_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.
view_setup_args