scvi.external.SOLO#

class scvi.external.SOLO(adata, **classifier_kwargs)[source]#

Doublet detection in scRNA-seq [Bernstein19].

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 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.
`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.
`create_doublets`(adata_manager, doublet_ratio)	Simulate doublets.
`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 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, use_gpu, prefix, ...])	Instantiate a model from the saved output.
`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_anndata])	Save the state of the model.
`setup_anndata`(adata[, labels_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, ...])	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#

adata#

SOLO.adata#

Data attached to model instance.

Return type:: AnnData

adata_manager#

SOLO.adata_manager#

Manager instance associated with self.adata.

Return type:: AnnDataManager

device#

SOLO.device#

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

Return type:: str

history#

SOLO.history#: Returns computed metrics during training.

is_trained#

SOLO.is_trained#

Whether the model has been trained.

Return type:: bool

test_indices#

SOLO.test_indices#

Observations that are in test set.

Return type:: ndarray

train_indices#

SOLO.train_indices#

Observations that are in train set.

Return type:: ndarray

validation_indices#

SOLO.validation_indices#

Observations that are in validation set.

Return type:: ndarray

Methods#

convert_legacy_save#

classmethod SOLO.convert_legacy_save(dir_path, output_dir_path, overwrite=False, prefix=None)#

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 | NoneOptional[str] (default: None): Prefix of saved file names.

Return type:

None

create_doublets#

classmethod SOLO.create_doublets(adata_manager, doublet_ratio, indices=None, seed=1)[source]#

Simulate doublets.

Parameters:

adata: AnnData object setup with setup_anndata.
doublet_ratio : int: Ratio of generated doublets to produce relative to number of cells in adata or length of indices, if not None.
indices : Sequence[int] | NoneOptional[Sequence[int]] (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

from_scvi_model#

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

Instantiate a SOLO model from an scvi model.

Parameters:

scvi_model : SCVI: Pre-trained model of SCVI. The adata object used to initialize this model should have only been setup with count data, and optionally a batch_key; i.e., no extra covariates or labels, etc.
adata : AnnData | NoneOptional[AnnData] (default: None): Optional anndata to use that is compatible with scvi_model.
restrict_to_batch : str | NoneOptional[str] (default: None): Batch category in batch_key used to setup adata for scvi_model to restrict Solo model to. This allows to train a Solo model on one batch of a scvi_model that was trained on multiple batches.
doublet_ratio : int (default: 2): Ratio of generated doublets to produce relative to number of cells in adata or length of indices, if not None.
**classifier_kwargs: Keyword args for Classifier

Returns:

SOLO model

get_anndata_manager#

SOLO.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 : AnnData: 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 | NoneOptional[AnnDataManager]

get_from_registry#

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 data registry.
adata : AnnData: AnnData to pull data from.

Return type:

ndarray

Returns:

The requested data as a NumPy array.

load#

classmethod SOLO.load(dir_path, adata=None, use_gpu=None, prefix=None, backup_url=None)#

Instantiate a model from the saved output.

Parameters:

dir_path : str: Path to saved outputs.
adata : AnnData | NoneOptional[AnnData] (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 : str | int | bool | NoneUnion[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 : str | NoneOptional[str] (default: None): Prefix of saved file names.
backup_url : str | NoneOptional[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_....

predict#

SOLO.predict(soft=True, include_simulated_doublets=False)[source]#

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 type:

DataFrame

Returns:

DataFrame with prediction, index corresponding to cell barcode.

register_manager#

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.

save#

SOLO.save(dir_path, prefix=None, overwrite=False, save_anndata=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 | NoneOptional[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 SOLO.setup_anndata(adata, labels_key=None, layer=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:

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

to_device#

SOLO.to_device(device)#

Move model to device.

Parameters:

device : str | intUnion[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#

SOLO.train(max_epochs=400, lr=0.001, use_gpu=None, train_size=0.9, validation_size=None, batch_size=128, plan_kwargs=None, early_stopping=True, early_stopping_patience=30, early_stopping_min_delta=0.0, **kwargs)[source]#

Trains the model.

Parameters:

max_epochs : int (default: 400): Number of epochs to train for
lr : float (default: 0.001): Learning rate for optimization.
use_gpu : str | int | bool | NoneUnion[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 : float | NoneOptional[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.
plan_kwargs : dict | NoneOptional[dict] (default: None): Keyword args for ClassifierTrainingPlan. Keyword arguments passed to
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 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.

view_anndata_setup#

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 | NoneOptional[AnnData] (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 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 | NoneOptional[str] (default: None): Prefix of saved file names.

Return type:

None