scvi.external.CellAssign

class scvi.external.CellAssign(adata, cell_type_markers, **model_kwargs)

Reimplementation of CellAssign for reference-based annotation [Zhang et al., 2019].

Original implementation: irrationone/cellassign.

Parameters

• adata (AnnData) – single-cell AnnData object that has been registered via setup_anndata(). The object should be subset to contain the same genes as the cell type marker dataframe.

• cell_type_markers (DataFrame) – Binary marker gene DataFrame of genes by cell types. Gene names corresponding to adata.var_names should be in DataFrame index, and cell type labels should be the columns.

• **model_kwargs – Keyword args for CellAssignModule

Examples

>>> adata = scvi.data.read_h5ad(path_to_anndata)
>>> library_size = adata.X.sum(1)
>>> adata.obs["size_factor"] = library_size / np.mean(library_size)
>>> marker_gene_mat = pd.read_csv(path_to_marker_gene_csv)
>>> bdata = adata[:, adata.var.index.isin(marker_gene_mat.index)].copy()
>>> CellAssign.setup_anndata(bdata, size_factor_key="size_factor")
>>> model = CellAssign(bdata, marker_gene_mat)
>>> model.train()
>>> predictions = model.predict(bdata)

Notes

Size factors in the R implementation of CellAssign are computed using scran. An approximate approach computes the sum of UMI counts (library size) over all genes and divides by the mean library size.

See further usage examples in the following tutorial:

1. Annotation with CellAssign

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.
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 specific to this model instance.
get_from_registry(adata, registry_key)	Returns the object in AnnData associated with the key in the data registry.
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.
predict()	Predict soft cell type assignment probability for each cell.
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, size_factor_key[, ...])	Sets up the AnnData object for this model.
to_device(device)	Move model to device.
train([max_epochs, lr, accelerator, ...])	Trains 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

CellAssign.adata

Data attached to model instance.

CellAssign.adata_manager

Manager instance associated with self.adata.

CellAssign.device

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

CellAssign.history

Returns computed metrics during training.

CellAssign.is_trained

Whether the model has been trained.

CellAssign.summary_string

Summary string of the model.

CellAssign.test_indices

Observations that are in test set.

CellAssign.train_indices

Observations that are in train set.

CellAssign.validation_indices

Observations that are in validation set.

Methods

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

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

Return type

None

CellAssign.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.

CellAssign.get_anndata_manager(adata, required=False)

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]

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

Return type

ndarray

Returns

The requested data as a NumPy array.

classmethod CellAssign.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 (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.

• 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 (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_....

static CellAssign.load_registry(dir_path, prefix=None)

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

CellAssign.predict()

Predict soft cell type assignment probability for each cell.

Return type

DataFrame

classmethod CellAssign.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.

CellAssign.save(dir_path, prefix=None, overwrite=False, save_anndata=False, save_kwargs=None, **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 (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

• save_kwargs (Optional[dict] (default: None)) – Keyword arguments passed into save().

• anndata_write_kwargs – Kwargs for write()

classmethod CellAssign.setup_anndata(adata, size_factor_key, batch_key=None, categorical_covariate_keys=None, continuous_covariate_keys=None, layer=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.

• size_factor_key (str) – key in adata.obs with continuous valued size factors.

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

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

• categorical_covariate_keys (Optional[list[str]] (default: None)) – keys in adata.obs that correspond to categorical data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.

• continuous_covariate_keys (Optional[list[str]] (default: None)) – keys in adata.obs that correspond to continuous data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.

CellAssign.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

CellAssign.train(max_epochs=400, lr=0.003, accelerator='auto', devices='auto', train_size=0.9, validation_size=None, shuffle_set_split=True, batch_size=1024, datasplitter_kwargs=None, plan_kwargs=None, early_stopping=True, early_stopping_patience=15, early_stopping_min_delta=0.0, **kwargs)

Trains the model.

Parameters

• max_epochs (int (default: 400)) – Number of epochs to train for

• lr (float (default: 0.003)) – 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 (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.

• 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: 1024)) – Minibatch size to use during training.

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

• plan_kwargs (Optional[dict] (default: None)) – Keyword args for TrainingPlan.

• early_stopping (bool (default: True)) – Adds callback for early stopping on validation_loss

• early_stopping_patience (int (default: 15)) – Number of times early stopping metric can not improve over early_stopping_min_delta

• early_stopping_min_delta (float (default: 0.0)) – Threshold for counting an epoch torwards patience train() will overwrite values present in plan_kwargs, when appropriate.

• **kwargs – Other keyword args for Trainer.

CellAssign.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 (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

static CellAssign.view_setup_args(dir_path, prefix=None)

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