scvi.external.SOLO

class scvi.external.SOLO(adata, **classifier_kwargs)

Doublet detection in scRNA-seq [Bernstein et al., 2020].

Original implementation: calico/solo.

Most users will initialize the model using the class method from_scvi_model(), which takes as input a pre-trained SCVI object.

Parameters:

• adata (AnnData) – AnnData object that has been registered via setup_anndata(). Object should contain the latent representation of real cells and doublets as adata.X. Object should also be registered, using .X and labels_key=”_solo_doub_sim”.

• **classifier_kwargs – Keyword args for Classifier

Examples

In the case of scVI trained with multiple batches:

>>> adata = anndata.read_h5ad(path_to_anndata)
>>> scvi.model.SCVI.setup_anndata(adata, batch_key="batch")
>>> vae = scvi.model.SCVI(adata)
>>> vae.train()
>>> solo_batch_1 = scvi.external.SOLO.from_scvi_model(vae, restrict_to_batch="batch 1")
>>> solo_batch_1.train()
>>> solo_batch_1.predict()

Otherwise:

>>> adata = anndata.read_h5ad(path_to_anndata)
>>> scvi.model.SCVI.setup_anndata(adata)
>>> vae = scvi.model.SCVI(adata)
>>> vae.train()
>>> solo = scvi.external.SOLO.from_scvi_model(vae)
>>> solo.train()
>>> solo.predict()

Notes

Solo should be trained on one lane of data at a time. An SCVI instance that was trained with multiple batches can be used as input, but Solo should be created and run multiple times, each with a new restrict_to_batch in from_scvi_model().

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.
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.
registry	Data attached to model instance.
run_id	Returns the run id of the model.
run_name	Returns the run name of the model.
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.
create_doublets(adata_manager, doublet_ratio)	Simulate doublets.
data_registry(registry_key)	Returns the object in AnnData associated with the key in the data registry.
deregister_manager([adata])	Deregisters the AnnDataManager instance associated with adata.
differential_abundance(*args, **kwargs)
from_scvi_model(scvi_model[, adata, ...])	Instantiate a SOLO model from an scvi model.
get_anndata_manager(adata[, required])	Retrieves the AnnDataManager for a given AnnData object.
get_from_registry(adata, registry_key)	Returns the object in AnnData associated with the key in the data registry.
get_normalized_expression(*args, **kwargs)
get_setup_arg(setup_arg)	Returns the string provided to setup of a specific setup_arg.
get_state_registry(registry_key)	Returns the state registry for the AnnDataField registered with this instance.
get_var_names([legacy_mudata_format])	Variable names of input data.
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([soft, include_simulated_doublets, ...])	Return doublet predictions.
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[, labels_key, layer])	Sets up the AnnData object for this model.
to_device(device)	Move the model to the device.
train([max_epochs, lr, accelerator, ...])	Trains the model.
transfer_fields(adata, **kwargs)	Transfer fields from a model to an AnnData object.
update_setup_method_args(setup_method_args)	Update setup method args.
view_anndata_setup([adata, ...])	Print summary of the setup for the initial AnnData or a given AnnData object.
view_registry([hide_state_registries])	Prints summary of the registry.
view_setup_args(dir_path[, prefix])	Print args used to setup a saved model.
view_setup_method_args()	Prints setup kwargs used to produce a given registry.

Attributes

SOLO.adata

Data attached to model instance.

SOLO.adata_manager

Manager instance associated with self.adata.

SOLO.device

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

SOLO.get_normalized_function_name

What the get normalized functions name is

SOLO.history

Returns computed metrics during training.

SOLO.is_trained

Whether the model has been trained.

SOLO.registry

Data attached to model instance.

SOLO.run_id

Returns the run id of the model. Used in MLFlow

SOLO.run_name

Returns the run name of the model. Used in MLFlow

SOLO.summary_string

Summary string of the model.

SOLO.test_indices

Observations that are in test set.

SOLO.train_indices

Observations that are in train set.

SOLO.validation_indices

Observations that are in validation set.

Methods

classmethod SOLO.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 the directory where the 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, an error will be raised.

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

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

Return type:

None

classmethod SOLO.create_doublets(adata_manager, doublet_ratio, indices=None, seed=1)

Simulate doublets.

Parameters:

• adata – AnnData object setup with setup_anndata.

• doublet_ratio (int) – Ratio of generated doublets to produce relative to the number of cells in adata or length of indices, if not None.

• indices (Sequence[int] | None (default: None)) – Indices of cells in adata to use. If None, all cells are used.

• seed (int (default: 1)) – Seed for reproducibility

Return type:

AnnData

SOLO.data_registry(registry_key)

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

Parameters:

registry_key (str) – key of an object to get from self.data_registry

Return type:

ndarray | DataFrame

Returns:

The requested data.

SOLO.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.

SOLO.differential_abundance(*args, **kwargs)

classmethod SOLO.from_scvi_model(scvi_model, adata=None, restrict_to_batch=None, doublet_ratio=2, **classifier_kwargs)

Instantiate a SOLO model from an scvi model.

Parameters:

• scvi_model (SCVI) – Pre-trained SCVI model. The AnnData object used to initialize this model should have only been setup with count data, and optionally a batch_key. Extra categorical and continuous covariates are currently unsupported.

• adata (AnnData | None (default: None)) – Optional AnnData to use that is compatible with scvi_model.

• restrict_to_batch (str | None (default: None)) – Batch category to restrict the SOLO model to if scvi_model was set up with a batch_key. This allows the model to be trained on the subset of cells belonging to restrict_to_batch when scvi_model was trained on multiple batches. If None, all cells are used.

• doublet_ratio (int (default: 2)) – Ratio of generated doublets to produce relative to the number of cells in adata or length of indices, if not None.

• **classifier_kwargs – Keyword args for Classifier

Returns:

SOLO model

SOLO.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 a manager instance for.

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

Return type:

AnnDataManager | None

SOLO.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 the data registry.

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

Return type:

ndarray

Returns:

The requested data as a NumPy array.

SOLO.get_normalized_expression(*args, **kwargs)

SOLO.get_setup_arg(setup_arg)

Returns the string provided to setup of a specific setup_arg.

Return type:

attrdict

SOLO.get_state_registry(registry_key)

Returns the state registry for the AnnDataField registered with this instance.

Return type:

attrdict

SOLO.get_var_names(legacy_mudata_format=False)

Variable names of input data.

Return type:

dict

classmethod SOLO.load(dir_path, adata=None, accelerator='auto', device='auto', prefix=None, backup_url=None, datamodule=None, allowed_classes_names_list=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. If False, will load the model without AnnData.

• 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.

• datamodule (LightningDataModule | None (default: None)) – EXPERIMENTAL A LightningDataModule instance to use for training in place of the default DataSplitter. Can only be passed in if the model was not initialized with AnnData.

• allowed_classes_names_list (list[str] | None (default: None)) – list of allowed classes names to be loaded (besides the original class name)

Returns:

Model with loaded state dictionaries.

Examples

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

static SOLO.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

SOLO.predict(soft=True, include_simulated_doublets=False, return_logits=False)

Return doublet predictions.

Parameters:

• soft (bool (default: True)) – Return probabilities instead of class label.

• include_simulated_doublets (bool (default: False)) – Return probabilities for simulated doublets as well.

• return_logits (bool (default: False)) – Whether to return logits instead of probabilities when soft is True.

Return type:

DataFrame

Returns:

DataFrame with prediction, index corresponding to cell barcode.

classmethod SOLO.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.

SOLO.save(dir_path, prefix=None, overwrite=False, save_anndata=False, save_kwargs=None, legacy_mudata_format=False, datamodule=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 (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, an 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.

• datamodule (LightningDataModule | None (default: None)) – EXPERIMENTAL A LightningDataModule instance to use for training in place of the default DataSplitter. Can only be passed in if the model was not initialized with AnnData.

• anndata_write_kwargs – Kwargs for write()

classmethod SOLO.setup_anndata(adata, labels_key=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:

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

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

SOLO.to_device(device)

Move the model to the device.

Parameters:

device (str | int | device) – Device to move model to. Options: ‘cpu’ for CPU, integer GPU index (e.g., 0), ‘cuda:X’ where X is the GPU index (e.g. ‘cuda:0’), or a torch.device object (including XLA devices for TPU). 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

SOLO.train(max_epochs=400, lr=0.001, accelerator='auto', devices='auto', train_size=None, validation_size=None, shuffle_set_split=True, batch_size=128, datasplitter_kwargs=None, plan_kwargs=None, early_stopping=True, early_stopping_patience=30, early_stopping_warmup_epochs=0, 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: 0.001)) – 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.

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

• plan_kwargs (dict | None (default: None)) – Keyword args for ClassifierTrainingPlan.

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

• early_stopping_patience (int (default: 30)) – Number of times early stopping metric cannot improve over early_stopping_min_delta

• early_stopping_warmup_epochs (int (default: 0)) – Wait for a certain number of warm-up epochs before the early stopping starts monitoring

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

• **kwargs – Other keyword args for Trainer.

SOLO.transfer_fields(adata, **kwargs)

Transfer fields from a model to an AnnData object.

Return type:

AnnData

SOLO.update_setup_method_args(setup_method_args)

Update setup method args.

Parameters:

setup_method_args (dict) – This is a bit of a misnomer, this is a dict representing kwargs of the setup method that will be used to update the existing values in the registry of this instance.

SOLO.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

SOLO.view_registry(hide_state_registries=False)

Prints summary of the registry.

Parameters:

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

Return type:

None

static SOLO.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

SOLO.view_setup_method_args()

Prints setup kwargs used to produce a given registry.

Parameters:

registry – Registry produced by an AnnDataManager.

Return type:

None