scvi.external.RNAStereoscope#

class scvi.external.RNAStereoscope(sc_adata, **model_kwargs)[source]#

Reimplementation of Stereoscope [Andersson20] for deconvolution of spatial transcriptomics from single-cell transcriptomics.

https://github.com/almaan/stereoscope.

Parameters

sc_adata : AnnData: single-cell AnnData object that has been registered via setup_anndata().
**model_kwargs: Keyword args for RNADeconv

Examples

>>> sc_adata = anndata.read_h5ad(path_to_sc_anndata)
>>> scvi.external.RNAStereoscope.setup_anndata(sc_adata, labels_key="labels")
>>> stereo = scvi.external.stereoscope.RNAStereoscope(sc_adata)
>>> stereo.train()

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#

`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.
`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 using MAP inference.
`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#

RNAStereoscope.adata#

Data attached to model instance.

Return type: AnnData

adata_manager#

RNAStereoscope.adata_manager#

Manager instance associated with self.adata.

Return type: AnnDataManager

device#

RNAStereoscope.device#

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

Return type: str

history#

RNAStereoscope.history#: Returns computed metrics during training.

is_trained#

RNAStereoscope.is_trained#

Whether the model has been trained.

Return type: bool

test_indices#

RNAStereoscope.test_indices#

Observations that are in test set.

Return type: ndarray

train_indices#

RNAStereoscope.train_indices#

Observations that are in train set.

Return type: ndarray

validation_indices#

RNAStereoscope.validation_indices#

Observations that are in validation set.

Return type: ndarray

Methods#

get_anndata_manager#

RNAStereoscope.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#

RNAStereoscope.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 RNAStereoscope.load(dir_path, adata=None, use_gpu=None, prefix=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.

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_....

register_manager#

classmethod RNAStereoscope.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#

RNAStereoscope.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 RNAStereoscope.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#

RNAStereoscope.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#

RNAStereoscope.train(max_epochs=400, lr=0.01, use_gpu=None, train_size=1, validation_size=None, batch_size=128, plan_kwargs=None, **kwargs)[source]#

Trains the model using MAP inference.

Parameters

max_epochs : int (default: 400): Number of epochs to train for
lr : float (default: 0.01): 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: 1): 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 TrainingPlan. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.
**kwargs: Other keyword args for Trainer.

view_anndata_setup#

RNAStereoscope.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 RNAStereoscope.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