scvi.model.SCANVI#

class scvi.model.SCANVI(adata, n_hidden=128, n_latent=10, n_layers=1, dropout_rate=0.1, dispersion='gene', gene_likelihood='zinb', **model_kwargs)[source]#

Single-cell annotation using variational inference [Xu21].

Inspired from M1 + M2 model, as described in (https://arxiv.org/pdf/1406.5298.pdf).

Parameters
adata : AnnData

AnnData object that has been registered via setup_anndata().

n_hidden : int (default: 128)

Number of nodes per hidden layer.

n_latent : int (default: 10)

Dimensionality of the latent space.

n_layers : int (default: 1)

Number of hidden layers used for encoder and decoder NNs.

dropout_rate : float (default: 0.1)

Dropout rate for neural networks.

dispersion : {‘gene’, ‘gene-batch’, ‘gene-label’, ‘gene-cell’}Literal[‘gene’, ‘gene-batch’, ‘gene-label’, ‘gene-cell’] (default: 'gene')

One of the following:

  • 'gene' - dispersion parameter of NB is constant per gene across cells

  • 'gene-batch' - dispersion can differ between different batches

  • 'gene-label' - dispersion can differ between different labels

  • 'gene-cell' - dispersion can differ for every gene in every cell

gene_likelihood : {‘zinb’, ‘nb’, ‘poisson’}Literal[‘zinb’, ‘nb’, ‘poisson’] (default: 'zinb')

One of:

  • 'nb' - Negative binomial distribution

  • 'zinb' - Zero-inflated negative binomial distribution

  • 'poisson' - Poisson distribution

**model_kwargs

Keyword args for SCANVAE

Examples

>>> adata = anndata.read_h5ad(path_to_anndata)
>>> scvi.model.SCANVI.setup_anndata(adata, batch_key="batch", labels_key="labels")
>>> vae = scvi.model.SCANVI(adata, "Unknown")
>>> vae.train()
>>> adata.obsm["X_scVI"] = vae.get_latent_representation()
>>> adata.obs["pred_label"] = vae.predict()

Notes

See further usage examples in the following tutorials:

  1. Atlas-level integration of lung data

  2. Reference mapping with scvi-tools

  3. Seed labeling with scANVI

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#

differential_expression([adata, groupby, ...])

A unified method for differential expression analysis.

from_scvi_model(scvi_model, unlabeled_category)

Initialize scanVI model with weights from pretrained SCVI model.

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_feature_correlation_matrix([adata, ...])

Generate gene-gene correlation matrix using scvi uncertainty and expression.

get_from_registry(adata, registry_key)

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

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

Returns the latent library size for each cell.

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

Return the latent representation for each cell.

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

Estimates for the parameters of the likelihood \(p(x \mid z)\)

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

Return the marginal LL for the data.

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

Returns the normalized (decoded) gene expression.

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

Return the reconstruction error for the data.

load(dir_path[, adata, use_gpu, prefix])

Instantiate a model from the saved output.

load_query_data(adata, reference_model[, ...])

Online update of a reference model with scArches algorithm [Lotfollahi21].

posterior_predictive_sample([adata, ...])

Generate observation samples from the posterior predictive distribution.

predict([adata, indices, soft, batch_size])

Return cell label 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, ...[, ...])

Sets up the AnnData object for this model.

to_device(device)

Move model to device.

train([max_epochs, n_samples_per_label, ...])

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#

SCANVI.adata#

Data attached to model instance.

Return type

AnnData

adata_manager#

SCANVI.adata_manager#

Manager instance associated with self.adata.

Return type

AnnDataManager

device#

SCANVI.device#

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

Return type

str

history#

SCANVI.history#

Returns computed metrics during training.

is_trained#

SCANVI.is_trained#

Whether the model has been trained.

Return type

bool

test_indices#

SCANVI.test_indices#

Observations that are in test set.

Return type

ndarray

train_indices#

SCANVI.train_indices#

Observations that are in train set.

Return type

ndarray

validation_indices#

SCANVI.validation_indices#

Observations that are in validation set.

Return type

ndarray

Methods#

differential_expression#

SCANVI.differential_expression(adata=None, groupby=None, group1=None, group2=None, idx1=None, idx2=None, mode='change', delta=0.25, batch_size=None, all_stats=True, batch_correction=False, batchid1=None, batchid2=None, fdr_target=0.05, silent=False, **kwargs)#

A unified method for differential expression analysis.

Implements “vanilla” DE [Lopez18] and “change” mode DE [Boyeau19].

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.

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

The key of the observations grouping to consider.

group1 : Iterable[str] | NoneOptional[Iterable[str]] (default: None)

Subset of groups, e.g. [‘g1’, ‘g2’, ‘g3’], to which comparison shall be restricted, or all groups in groupby (default).

group2 : str | NoneOptional[str] (default: None)

If None, compare each group in group1 to the union of the rest of the groups in groupby. If a group identifier, compare with respect to this group.

idx1 : Sequence[int] | Sequence[bool] | str | NoneUnion[Sequence[int], Sequence[bool], str, None] (default: None)

idx1 and idx2 can be used as an alternative to the AnnData keys. Custom identifier for group1 that can be of three sorts: (1) a boolean mask, (2) indices, or (3) a string. If it is a string, then it will query indices that verifies conditions on adata.obs, as described in pandas.DataFrame.query() If idx1 is not None, this option overrides group1 and group2.

idx2 : Sequence[int] | Sequence[bool] | str | NoneUnion[Sequence[int], Sequence[bool], str, None] (default: None)

Custom identifier for group2 that has the same properties as idx1. By default, includes all cells not specified in idx1.

mode : {‘vanilla’, ‘change’}Literal[‘vanilla’, ‘change’] (default: 'change')

Method for differential expression. See user guide for full explanation.

delta : float (default: 0.25)

specific case of region inducing differential expression. In this case, we suppose that \(R \setminus [-\delta, \delta]\) does not induce differential expression (change model default case).

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

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

all_stats : bool (default: True)

Concatenate count statistics (e.g., mean expression group 1) to DE results.

batch_correction : bool (default: False)

Whether to correct for batch effects in DE inference.

batchid1 : Iterable[str] | NoneOptional[Iterable[str]] (default: None)

Subset of categories from batch_key registered in setup_anndata, e.g. [‘batch1’, ‘batch2’, ‘batch3’], for group1. Only used if batch_correction is True, and by default all categories are used.

batchid2 : Iterable[str] | NoneOptional[Iterable[str]] (default: None)

Same as batchid1 for group2. batchid2 must either have null intersection with batchid1, or be exactly equal to batchid1. When the two sets are exactly equal, cells are compared by decoding on the same batch. When sets have null intersection, cells from group1 and group2 are decoded on each group in group1 and group2, respectively.

fdr_target : float (default: 0.05)

Tag features as DE based on posterior expected false discovery rate.

silent : bool (default: False)

If True, disables the progress bar. Default: False.

**kwargs

Keyword args for scvi.model.base.DifferentialComputation.get_bayes_factors()

Return type

DataFrame

Returns

Differential expression DataFrame.

from_scvi_model#

classmethod SCANVI.from_scvi_model(scvi_model, unlabeled_category, labels_key=None, adata=None, **scanvi_kwargs)[source]#

Initialize scanVI model with weights from pretrained SCVI model.

Parameters
scvi_model : SCVI

Pretrained scvi model

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

key in adata.obs for label information. Label categories can not be different if labels_key was used to setup the SCVI model. If None, uses the labels_key used to setup the SCVI model. If that was None, and error is raised.

unlabeled_category : str

Value used for unlabeled cells in labels_key used to setup AnnData with scvi.

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

AnnData object that has been registered via setup_anndata().

scanvi_kwargs

kwargs for scANVI model

get_anndata_manager#

SCANVI.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_elbo#

SCANVI.get_elbo(adata=None, indices=None, batch_size=None)#

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_feature_correlation_matrix#

SCANVI.get_feature_correlation_matrix(adata=None, indices=None, n_samples=10, batch_size=64, rna_size_factor=1000, transform_batch=None, correlation_type='spearman')#

Generate gene-gene correlation matrix using scvi uncertainty and expression.

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_samples : int (default: 10)

Number of posterior samples to use for estimation.

batch_size : int (default: 64)

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

rna_size_factor : int (default: 1000)

size factor for RNA prior to sampling gamma distribution.

transform_batch : Sequence[Union[int, float, str]] | NoneOptional[Sequence[Union[int, float, str]]] (default: None)

Batches to condition on. If transform_batch is:

  • None, then real observed batch is used.

  • int, then batch transform_batch is used.

  • list of int, then values are averaged over provided batches.

correlation_type : {‘spearman’, ‘pearson’}Literal[‘spearman’, ‘pearson’] (default: 'spearman')

One of “pearson”, “spearman”.

Return type

DataFrame

Returns

Gene-gene correlation matrix

get_from_registry#

SCANVI.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_latent_library_size#

SCANVI.get_latent_library_size(adata=None, indices=None, give_mean=True, batch_size=None)#

Returns the latent library size for each cell.

This is denoted as \(\ell_n\) in the scVI paper.

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.

give_mean : bool (default: True)

Return the mean or a sample from the posterior distribution.

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

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

Return type

ndarray

get_latent_representation#

SCANVI.get_latent_representation(adata=None, indices=None, give_mean=True, mc_samples=5000, batch_size=None)#

Return the latent representation for each cell.

This is denoted as \(z_n\) in our manuscripts.

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.

give_mean : bool (default: True)

Give mean of distribution or sample from it.

mc_samples : int (default: 5000)

For distributions with no closed-form mean (e.g., logistic normal), how many Monte Carlo samples to take for computing mean.

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

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

Return type

ndarray

Returns

-latent_representation (ndarray) Low-dimensional representation for each cell

get_likelihood_parameters#

SCANVI.get_likelihood_parameters(adata=None, indices=None, n_samples=1, give_mean=False, batch_size=None)#

Estimates for the parameters of the likelihood \(p(x \mid z)\)

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_samples : int | NoneOptional[int] (default: 1)

Number of posterior samples to use for estimation.

give_mean : bool | NoneOptional[bool] (default: False)

Return expected value of parameters or a samples

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

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

Return type

{str: ndarray}Dict[str, ndarray]

get_marginal_ll#

SCANVI.get_marginal_ll(adata=None, indices=None, n_mc_samples=1000, batch_size=None)#

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_normalized_expression#

SCANVI.get_normalized_expression(adata=None, indices=None, transform_batch=None, gene_list=None, library_size=1, n_samples=1, n_samples_overall=None, batch_size=None, return_mean=True, return_numpy=None)#

Returns the normalized (decoded) gene expression.

This is denoted as \(\rho_n\) in the scVI paper.

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.

transform_batch : Sequence[Union[int, float, str]] | NoneOptional[Sequence[Union[int, float, str]]] (default: None)

Batch to condition on. If transform_batch is:

  • None, then real observed batch is used.

  • int, then batch transform_batch is used.

gene_list : Sequence[str] | NoneOptional[Sequence[str]] (default: None)

Return frequencies of expression for a subset of genes. This can save memory when working with large datasets and few genes are of interest.

library_size : float | {‘latent’}Union[float, Literal[‘latent’]] (default: 1)

Scale the expression frequencies to a common library size. This allows gene expression levels to be interpreted on a common scale of relevant magnitude. If set to “latent”, use the latent libary size.

n_samples : int (default: 1)

Number of posterior samples to use for estimation.

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

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

return_mean : bool (default: True)

Whether to return the mean of the samples.

return_numpy : bool | NoneOptional[bool] (default: None)

Return a ndarray instead of a DataFrame. DataFrame includes gene names as columns. If either n_samples=1 or return_mean=True, defaults to False. Otherwise, it defaults to True.

Return type

ndarray | DataFrameUnion[ndarray, DataFrame]

Returns

If n_samples > 1 and return_mean is False, then the shape is (samples, cells, genes). Otherwise, shape is (cells, genes). In this case, return type is DataFrame unless return_numpy is True.

get_reconstruction_error#

SCANVI.get_reconstruction_error(adata=None, indices=None, batch_size=None)#

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 SCANVI.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_....

load_query_data#

classmethod SCANVI.load_query_data(adata, reference_model, inplace_subset_query_vars=False, use_gpu=None, unfrozen=False, freeze_dropout=False, freeze_expression=True, freeze_decoder_first_layer=True, freeze_batchnorm_encoder=True, freeze_batchnorm_decoder=False, freeze_classifier=True)#

Online update of a reference model with scArches algorithm [Lotfollahi21].

Parameters
adata : AnnData

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.

reference_model : str | BaseModelClassUnion[str, BaseModelClass]

Either an already instantiated model of the same class, or a path to saved outputs for reference model.

inplace_subset_query_vars : bool (default: False)

Whether to subset and rearrange query vars inplace based on vars used to train reference 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).

unfrozen : bool (default: False)

Override all other freeze options for a fully unfrozen model

freeze_dropout : bool (default: False)

Whether to freeze dropout during training

freeze_expression : bool (default: True)

Freeze neurons corersponding to expression in first layer

freeze_decoder_first_layer : bool (default: True)

Freeze neurons corersponding to first layer in decoder

freeze_batchnorm_encoder : bool (default: True)

Whether to freeze batchnorm weight and bias during training for encoder

freeze_batchnorm_decoder : bool (default: False)

Whether to freeze batchnorm weight and bias during training for decoder

freeze_classifier : bool (default: True)

Whether to freeze classifier completely. Only applies to SCANVI.

posterior_predictive_sample#

SCANVI.posterior_predictive_sample(adata=None, indices=None, n_samples=1, gene_list=None, batch_size=None)#

Generate observation samples from the posterior predictive distribution.

The posterior predictive distribution is written as \(p(\hat{x} \mid x)\).

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_samples : int (default: 1)

Number of samples for each cell.

gene_list : Sequence[str] | NoneOptional[Sequence[str]] (default: None)

Names of genes of interest.

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

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

Return type

ndarray

Returns

x_new : torch.Tensor tensor with shape (n_cells, n_genes, n_samples)

predict#

SCANVI.predict(adata=None, indices=None, soft=False, batch_size=None)[source]#

Return cell label predictions.

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

AnnData object that has been registered via setup_anndata().

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

Return probabilities for each class label.

soft : bool (default: False)

If True, returns per class probabilities

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

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

Return type

ndarray | DataFrameUnion[ndarray, DataFrame]

register_manager#

classmethod SCANVI.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#

SCANVI.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 SCANVI.setup_anndata(adata, labels_key, unlabeled_category, layer=None, batch_key=None, size_factor_key=None, categorical_covariate_keys=None, continuous_covariate_keys=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.

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

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.

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

key in adata.obs for size factor information. Instead of using library size as a size factor, the provided size factor column will be used as offset in the mean of the likelihood. Assumed to be on linear scale.

categorical_covariate_keys : List[str] | NoneOptional[List[str]] (default: None)

keys in adata.obs that correspond to categorical data.

continuous_covariate_keys : List[str] | NoneOptional[List[str]] (default: None)

keys in adata.obs that correspond to continuous data.

to_device#

SCANVI.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#

SCANVI.train(max_epochs=None, n_samples_per_label=None, check_val_every_n_epoch=None, train_size=0.9, validation_size=None, batch_size=128, use_gpu=None, plan_kwargs=None, **trainer_kwargs)[source]#

Train the model.

Parameters
max_epochs : int | NoneOptional[int] (default: None)

Number of passes through the dataset for semisupervised training.

n_samples_per_label : float | NoneOptional[float] (default: None)

Number of subsamples for each label class to sample per epoch. By default, there is no label subsampling.

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

Frequency with which metrics are computed on the data for validation set for both the unsupervised and semisupervised trainers. If you’d like a different frequency for the semisupervised trainer, set check_val_every_n_epoch in semisupervised_train_kwargs.

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.

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).

plan_kwargs : dict | NoneOptional[dict] (default: None)

Keyword args for SemiSupervisedTrainingPlan. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.

**trainer_kwargs

Other keyword args for Trainer.

view_anndata_setup#

SCANVI.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 SCANVI.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