scvi.external.GIMVI#

class scvi.external.GIMVI(adata_seq, adata_spatial, generative_distributions=['zinb', 'nb'], model_library_size=[True, False], n_latent=10, **model_kwargs)[source]#

Joint VAE for imputing missing genes in spatial data [Lopez19].

Parameters:
adata_seq : AnnData

AnnData object that has been registered via setup_anndata() and contains RNA-seq data.

adata_spatial : AnnData

AnnData object that has been registered via setup_anndata() and contains spatial data.

n_hidden

Number of nodes per hidden layer.

generative_distributions : List (default: ['zinb', 'nb'])

List of generative distribution for adata_seq data and adata_spatial data.

model_library_size : List (default: [True, False])

List of bool of whether to model library size for adata_seq and adata_spatial.

n_latent : int (default: 10)

Dimensionality of the latent space.

**model_kwargs

Keyword args for JVAE

Examples

>>> adata_seq = anndata.read_h5ad(path_to_anndata_seq)
>>> adata_spatial = anndata.read_h5ad(path_to_anndata_spatial)
>>> scvi.external.GIMVI.setup_anndata(adata_seq)
>>> scvi.external.GIMVI.setup_anndata(adata_spatial)
>>> vae = scvi.model.GIMVI(adata_seq, adata_spatial)
>>> vae.train(n_epochs=400)

Notes

See further usage examples in the following tutorials:

  1. /user_guide/notebooks/gimvi_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 GIMVI model (<v0.15.0) to the updated save format.

get_anndata_manager(adata[, required])

Retrieves the AnnDataManager for a given AnnData object specific to this model instance.

get_elbo([adata, indices, batch_size])

Return the ELBO for the data.

get_from_registry(adata, registry_key)

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

get_imputed_values([adatas, deterministic, ...])

Return imputed values for all genes for each dataset.

get_latent_representation([adatas, ...])

Return the latent space embedding for each dataset.

get_marginal_ll([adata, indices, ...])

Return the marginal LL for the data.

get_reconstruction_error([adata, indices, ...])

Return the reconstruction error for the data.

load(dir_path[, adata_seq, adata_spatial, ...])

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_anndata])

Save the state of the model.

setup_anndata(adata[, batch_key, ...])

Sets up the AnnData object for this model.

to_device(device)

Move model to device.

train([max_epochs, use_gpu, kappa, ...])

Train 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#

GIMVI.adata[source]#

Data attached to model instance.

Return type:

AnnData | MuDataUnion[AnnData, MuData]

adata_manager#

GIMVI.adata_manager[source]#

Manager instance associated with self.adata.

Return type:

AnnDataManager

device#

GIMVI.device[source]#

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

Return type:

str

history#

GIMVI.history[source]#

Returns computed metrics during training.

is_trained#

GIMVI.is_trained[source]#

Whether the model has been trained.

Return type:

bool

test_indices#

GIMVI.test_indices[source]#

Observations that are in test set.

Return type:

ndarray

train_indices#

GIMVI.train_indices[source]#

Observations that are in train set.

Return type:

ndarray

validation_indices#

GIMVI.validation_indices[source]#

Observations that are in validation set.

Return type:

ndarray

Methods#

convert_legacy_save#

classmethod GIMVI.convert_legacy_save(dir_path, output_dir_path, overwrite=False, prefix=None)[source]#

Converts a legacy saved GIMVI 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

get_anndata_manager#

GIMVI.get_anndata_manager(adata, required=False)[source]#

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 | MuDataUnion[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 | NoneOptional[AnnDataManager]

get_elbo#

GIMVI.get_elbo(adata=None, indices=None, batch_size=None)[source]#

Return the ELBO for the data.

The ELBO is a lower bound on the log likelihood of the data used for optimization of VAEs. Note, this is not the negative ELBO, higher is better.

Parameters:
adata : AnnData | NoneOptional[AnnData] (default: None)

AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

indices : Sequence[int] | NoneOptional[Sequence[int]] (default: None)

Indices of cells in adata to use. If None, all cells are used.

batch_size : int | NoneOptional[int] (default: None)

Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

Return type:

float

get_from_registry#

GIMVI.get_from_registry(adata, registry_key)[source]#

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 | MuDataUnion[AnnData, MuData]

AnnData to pull data from.

Return type:

ndarray

Returns:

The requested data as a NumPy array.

get_imputed_values#

GIMVI.get_imputed_values(adatas=None, deterministic=True, normalized=True, decode_mode=None, batch_size=128)[source]#

Return imputed values for all genes for each dataset.

Parameters:
adatas : List[AnnData] | NoneOptional[List[AnnData]] (default: None)

List of adata seq and adata spatial

deterministic : bool (default: True)

If true, use the mean of the encoder instead of a Gaussian sample for the latent vector.

normalized : bool (default: True)

Return imputed normalized values or not.

decode_mode : int | NoneOptional[int] (default: None)

If a decode_mode is given, use the encoder specific to each dataset as usual but use the decoder of the dataset of id decode_mode to impute values.

batch_size : int (default: 128)

Minibatch size for data loading into model.

Return type:

List[ndarray]

get_latent_representation#

GIMVI.get_latent_representation(adatas=None, deterministic=True, batch_size=128)[source]#

Return the latent space embedding for each dataset.

Parameters:
adatas : List[AnnData] | NoneOptional[List[AnnData]] (default: None)

List of adata seq and adata spatial.

deterministic : bool (default: True)

If true, use the mean of the encoder instead of a Gaussian sample.

batch_size : int (default: 128)

Minibatch size for data loading into model.

Return type:

List[ndarray]

get_marginal_ll#

GIMVI.get_marginal_ll(adata=None, indices=None, n_mc_samples=1000, batch_size=None)[source]#

Return the marginal LL for the data.

The computation here is a biased estimator of the marginal log likelihood of the data. Note, this is not the negative log likelihood, higher is better.

Parameters:
adata : AnnData | NoneOptional[AnnData] (default: None)

AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

indices : Sequence[int] | NoneOptional[Sequence[int]] (default: None)

Indices of cells in adata to use. If None, all cells are used.

n_mc_samples : int (default: 1000)

Number of Monte Carlo samples to use for marginal LL estimation.

batch_size : int | NoneOptional[int] (default: None)

Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

Return type:

float

get_reconstruction_error#

GIMVI.get_reconstruction_error(adata=None, indices=None, batch_size=None)[source]#

Return the reconstruction error for the data.

This is typically written as \(p(x \mid z)\), the likelihood term given one posterior sample. Note, this is not the negative likelihood, higher is better.

Parameters:
adata : AnnData | NoneOptional[AnnData] (default: None)

AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

indices : Sequence[int] | NoneOptional[Sequence[int]] (default: None)

Indices of cells in adata to use. If None, all cells are used.

batch_size : int | NoneOptional[int] (default: None)

Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

Return type:

float | {str: float}Union[float, Dict[str, float]]

load#

classmethod GIMVI.load(dir_path, adata_seq=None, adata_spatial=None, use_gpu=None, prefix=None, backup_url=None)[source]#

Instantiate a model from the saved output.

Parameters:
dir_path : str

Path to saved outputs.

adata_seq : 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. AnnData must be registered via setup_anndata().

adata_spatial : AnnData | NoneOptional[AnnData] (default: None)

AnnData organized in the same way as data used to train model. 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

>>> vae = GIMVI.load(adata_seq, adata_spatial, save_path)
>>> vae.get_latent_representation()

load_registry#

static GIMVI.load_registry(dir_path, prefix=None)[source]#

Return the full registry saved with the model.

Parameters:
dir_path : str

Path to saved outputs.

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

Prefix of saved file names.

Return type:

dict

Returns:

The full registry saved with the model

register_manager#

classmethod GIMVI.register_manager(adata_manager)[source]#

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#

GIMVI.save(dir_path, prefix=None, overwrite=False, save_anndata=False, **anndata_write_kwargs)[source]#

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 anndata write function

setup_anndata#

classmethod GIMVI.setup_anndata(adata, batch_key=None, 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:
batch_key : str | NoneOptional[str] (default: None)

key in adata.obs for batch information. Categories will automatically be converted into integer categories and saved to adata.obs[‘_scvi_batch’]. If None, assigns the same batch to all the data.

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#

GIMVI.to_device(device)[source]#

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#

GIMVI.train(max_epochs=200, use_gpu=None, kappa=5, train_size=0.9, validation_size=None, batch_size=128, plan_kwargs=None, **kwargs)[source]#

Train the model.

Parameters:
max_epochs : int (default: 200)

Number of passes through the dataset. If None, defaults to np.min([round((20000 / n_cells) * 400), 400])

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).

kappa : int (default: 5)

Scaling parameter for the discriminator loss.

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 model-specific Pytorch Lightning task. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.

**kwargs

Other keyword args for Trainer.

view_anndata_setup#

GIMVI.view_anndata_setup(adata=None, hide_state_registries=False)[source]#

Print summary of the setup for the initial AnnData or a given AnnData object.

Parameters:
adata : AnnData | MuData | NoneUnion[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

view_setup_args#

static GIMVI.view_setup_args(dir_path, prefix=None)[source]#

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