scvi.external.SpatialStereoscope#

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

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

https://github.com/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 : {‘n_obs’, ‘minibatch’}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. /user_guide/notebooks/stereoscope_heart_LV_tutorial

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.

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 specific to this model instance.

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, 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[, layer])

Sets up the AnnData object for this model.

to_device(device)

Move model to device.

train([max_epochs, lr, use_gpu, batch_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#

SpatialStereoscope.adata#

Data attached to model instance.

Return type:

AnnData

adata_manager#

SpatialStereoscope.adata_manager#

Manager instance associated with self.adata.

Return type:

AnnDataManager

device#

SpatialStereoscope.device#

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

Return type:

str

history#

SpatialStereoscope.history#

Returns computed metrics during training.

is_trained#

SpatialStereoscope.is_trained#

Whether the model has been trained.

Return type:

bool

test_indices#

SpatialStereoscope.test_indices#

Observations that are in test set.

Return type:

ndarray

train_indices#

SpatialStereoscope.train_indices#

Observations that are in train set.

Return type:

ndarray

validation_indices#

SpatialStereoscope.validation_indices#

Observations that are in validation set.

Return type:

ndarray

Methods#

convert_legacy_save#

classmethod SpatialStereoscope.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

from_rna_model#

classmethod SpatialStereoscope.from_rna_model(st_adata, sc_model, prior_weight='n_obs', layer=None, **model_kwargs)[source]#

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 : {‘n_obs’, ‘minibatch’}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.

layer : str | NoneOptional[str] (default: None)

if not None, uses this as the key in adata.layers for raw count data.

**model_kwargs

Keyword args for SpatialDeconv

get_anndata_manager#

SpatialStereoscope.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#

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

AnnData to pull data from.

Return type:

ndarray

Returns:

The requested data as a NumPy array.

get_proportions#

SpatialStereoscope.get_proportions(keep_noise=False)[source]#

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

whether to account for the noise term as a standalone cell type in the proportion estimate.

Return type:

DataFrame

get_scale_for_ct#

SpatialStereoscope.get_scale_for_ct(y)[source]#

Calculate the cell type specific expression.

Parameters:
y : ndarray

numpy array containing the list of cell types

Return type:

ndarray

Returns:

gene_expression

load#

classmethod SpatialStereoscope.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_....

register_manager#

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.

save#

SpatialStereoscope.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 SpatialStereoscope.setup_anndata(adata, 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:
layer : str | NoneOptional[str] (default: None)

if not None, uses this as the key in adata.layers for raw count data.

to_device#

SpatialStereoscope.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#

SpatialStereoscope.train(max_epochs=400, lr=0.01, use_gpu=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).

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#

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 | 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 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 | NoneOptional[str] (default: None)

Prefix of saved file names.

Return type:

None