scvi.external.SpatialStereoscope

class scvi.external.SpatialStereoscope(st_adata, sc_params, cell_type_mapping, prior_weight='n_obs', **model_kwargs)

Reimplementation of Stereoscope [Andersson et al., 2020].

Deconvolution of spatial transcriptomics from single-cell transcriptomics. Original implementation: almaan/stereoscope.

Parameters:

• st_adata (AnnData) – spatial transcriptomics AnnData object that has been registered via setup_anndata().

• sc_params (tuple[ndarray]) – parameters of the model learned from the single-cell RNA seq data for deconvolution.

• cell_type_mapping (ndarray) – numpy array mapping for the cell types used in the deconvolution

• prior_weight (Literal['n_obs', 'minibatch'] (default: 'n_obs')) – how to reweight the minibatches for stochastic optimization. “n_obs” is the valid procedure, “minibatch” is the procedure implemented in Stereoscope.

• **model_kwargs – Keyword args for SpatialDeconv

Examples

>>> sc_adata = anndata.read_h5ad(path_to_sc_anndata)
>>> scvi.external.RNAStereoscope.setup_anndata(sc_adata, labels_key="labels")
>>> sc_model = scvi.external.stereoscope.RNAStereoscope(sc_adata)
>>> sc_model.train()
>>> st_adata = anndata.read_h5ad(path_to_st_anndata)
>>> scvi.external.SpatialStereoscope.setup_anndata(st_adata)
>>> stereo = scvi.external.stereoscope.SpatialStereoscope.from_rna_model(st_adata, sc_model)
>>> stereo.train()
>>> st_adata.obsm["deconv"] = stereo.get_proportions()

Notes

See further usage examples in the following tutorials:

1. Stereoscope applied to left ventricule data

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.
from_rna_model(st_adata, sc_model[, ...])	Alternate constructor for exploiting a pre-trained model on RNA-seq data.
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_proportions([keep_noise])	Returns the estimated cell type proportion for the spatial data.
get_scale_for_ct(y)	Calculate the cell type specific expression.
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.
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[, layer])	Sets up the AnnData object for this model.
to_device(device)	Move model to device.
train([max_epochs, lr, accelerator, ...])	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

SpatialStereoscope.adata

Data attached to model instance.

SpatialStereoscope.adata_manager

Manager instance associated with self.adata.

SpatialStereoscope.device

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

SpatialStereoscope.history

Returns computed metrics during training.

SpatialStereoscope.is_trained

Whether the model has been trained.

SpatialStereoscope.summary_string

Summary string of the model.

SpatialStereoscope.test_indices

Observations that are in test set.

SpatialStereoscope.train_indices

Observations that are in train set.

SpatialStereoscope.validation_indices

Observations that are in validation set.

Methods

classmethod SpatialStereoscope.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 (str | None (default: None)) – Prefix of saved file names.

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

Return type:

None

SpatialStereoscope.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.

classmethod SpatialStereoscope.from_rna_model(st_adata, sc_model, prior_weight='n_obs', **model_kwargs)

Alternate constructor for exploiting a pre-trained model on RNA-seq data.

Parameters:

• st_adata (AnnData) – registed anndata object

• sc_model (RNAStereoscope) – trained RNADeconv model

• prior_weight (Literal['n_obs', 'minibatch'] (default: 'n_obs')) – how to reweight the minibatches for stochastic optimization. “n_obs” is the valid procedure, “minibatch” is the procedure implemented in Stereoscope.

• **model_kwargs – Keyword args for SpatialDeconv

SpatialStereoscope.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 manager instance for.

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

Return type:

AnnDataManager | None

SpatialStereoscope.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 | MuData) – AnnData to pull data from.

Return type:

ndarray

Returns:

The requested data as a NumPy array.

SpatialStereoscope.get_proportions(keep_noise=False)

Returns the estimated cell type proportion for the spatial data.

Shape is n_cells x n_labels OR n_cells x (n_labels + 1) if keep_noise

Parameters:

keep_noise (default: False) – whether to account for the noise term as a standalone cell type in the proportion estimate.

Return type:

DataFrame

SpatialStereoscope.get_scale_for_ct(y)

Calculate the cell type specific expression.

Parameters:

y (ndarray) – numpy array containing the list of cell types

Return type:

ndarray

Returns:

gene_expression

classmethod SpatialStereoscope.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 (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 (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.

Returns:

Model with loaded state dictionaries.

Examples

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

static SpatialStereoscope.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

classmethod SpatialStereoscope.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.

SpatialStereoscope.save(dir_path, prefix=None, overwrite=False, save_anndata=False, save_kwargs=None, legacy_mudata_format=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 | 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, 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.

• anndata_write_kwargs – Kwargs for write()

classmethod SpatialStereoscope.setup_anndata(adata, 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:

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

SpatialStereoscope.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

SpatialStereoscope.train(max_epochs=400, lr=0.01, accelerator='auto', devices='auto', shuffle_set_split=True, batch_size=128, datasplitter_kwargs=None, plan_kwargs=None, **kwargs)

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.

• 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.

• 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 TrainingPlan. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.

• **kwargs – Other keyword args for Trainer.

SpatialStereoscope.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

static SpatialStereoscope.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