scvi.model.TOTALVI#

class scvi.model.TOTALVI(adata, n_latent=20, gene_dispersion='gene', protein_dispersion='protein', gene_likelihood='nb', latent_distribution='normal', empirical_protein_background_prior=None, override_missing_proteins=False, **model_kwargs)[source]#

total Variational Inference [Gayoso et al., 2021].

Parameters:
  • adata (AnnData) – AnnData object that has been registered via setup_anndata().

  • n_latent (int (default: 20)) – Dimensionality of the latent space.

  • gene_dispersion (Literal['gene', 'gene-batch', 'gene-label', 'gene-cell'] (default: 'gene')) –

    One of the following:

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

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

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

  • protein_dispersion (Literal['protein', 'protein-batch', 'protein-label'] (default: 'protein')) –

    One of the following:

    • 'protein' - protein_dispersion parameter is constant per protein across cells

    • 'protein-batch' - protein_dispersion can differ between different batches NOT TESTED

    • 'protein-label' - protein_dispersion can differ between different labels NOT TESTED

  • gene_likelihood (Literal['zinb', 'nb'] (default: 'nb')) –

    One of:

    • 'nb' - Negative binomial distribution

    • 'zinb' - Zero-inflated negative binomial distribution

  • latent_distribution (Literal['normal', 'ln'] (default: 'normal')) –

    One of:

    • 'normal' - Normal distribution

    • 'ln' - Logistic normal distribution (Normal(0, I) transformed by softmax)

  • empirical_protein_background_prior (bool | None (default: None)) – Set the initialization of protein background prior empirically. This option fits a GMM for each of 100 cells per batch and averages the distributions. Note that even with this option set to True, this only initializes a parameter that is learned during inference. If False, randomly initializes. The default (None), sets this to True if greater than 10 proteins are used.

  • override_missing_proteins (bool (default: False)) – If True, will not treat proteins with all 0 expression in a particular batch as missing.

  • **model_kwargs – Keyword args for TOTALVAE

Examples

>>> adata = anndata.read_h5ad(path_to_anndata)
>>> scvi.model.TOTALVI.setup_anndata(
        adata, batch_key="batch", protein_expression_obsm_key="protein_expression"
    )
>>> vae = scvi.model.TOTALVI(adata)
>>> vae.train()
>>> adata.obsm["X_totalVI"] = vae.get_latent_representation()

Notes

See further usage examples in the following tutorials:

  1. CITE-seq analysis with totalVI

  2. Integration of CITE-seq and scRNA-seq data

  3. Reference mapping with scvi-tools

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.

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

A unified method for differential expression analysis.

get_anndata_manager(adata[, required])

Retrieves the AnnDataManager for a given AnnData object.

get_elbo([adata, indices, batch_size, ...])

Compute the evidence lower bound (ELBO) on 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, ...])

Compute the latent representation of the data.

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

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

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

Compute the marginal log-likehood of the data.

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

Returns the normalized gene expression and protein expression.

get_protein_background_mean(adata, indices, ...)

Get protein background mean.

get_protein_foreground_probability([adata, ...])

Returns the foreground probability for proteins.

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

Compute the reconstruction error on the data.

load(dir_path[, adata, accelerator, device, ...])

Instantiate a model from the saved output.

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

Online update of a reference model with scArches algorithm [Lotfollahi et al., 2021].

load_registry(dir_path[, prefix])

Return the full registry saved with the model.

posterior_predictive_sample([adata, ...])

Generate observation samples from the posterior predictive distribution.

prepare_query_anndata(adata, reference_model)

Prepare data for query integration.

prepare_query_mudata(mdata, reference_model)

Prepare multimodal dataset for query integration.

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, protein_expression_obsm_key)

Sets up the AnnData object for this model.

setup_mudata(mdata[, rna_layer, ...])

Sets up the MuData object for this model.

to_device(device)

Move model to device.

train([max_epochs, lr, accelerator, ...])

Trains the model using amortized variational 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#

TOTALVI.adata[source]#

Data attached to model instance.

TOTALVI.adata_manager[source]#

Manager instance associated with self.adata.

TOTALVI.device[source]#

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

TOTALVI.history[source]#

Returns computed metrics during training.

TOTALVI.is_trained[source]#

Whether the model has been trained.

TOTALVI.summary_string[source]#

Summary string of the model.

TOTALVI.test_indices[source]#

Observations that are in test set.

TOTALVI.train_indices[source]#

Observations that are in train set.

TOTALVI.validation_indices[source]#

Observations that are in validation set.

Methods#

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

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

TOTALVI.deregister_manager(adata=None)[source]#

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.

TOTALVI.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, protein_prior_count=0.1, scale_protein=False, sample_protein_mixing=False, include_protein_background=False, **kwargs)[source]#

A unified method for differential expression analysis.

Implements “vanilla” DE [Lopez et al., 2018]. and “change” mode DE [Boyeau et al., 2019].

Parameters:
  • adata (AnnData | None (default: None)) – AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

  • groupby (str | None (default: None)) – The key of the observations grouping to consider.

  • group1 (Iterable[str] | None (default: None)) – Subset of groups, e.g. [‘g1’, ‘g2’, ‘g3’], to which comparison shall be restricted, or all groups in groupby (default).

  • group2 (str | None (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 | 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 | None (default: None)) – Custom identifier for group2 that has the same properties as idx1. By default, includes all cells not specified in idx1.

  • mode (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 | None (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] | None (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] | None (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.

  • protein_prior_count (float (default: 0.1)) – Prior count added to protein expression before LFC computation

  • scale_protein (bool (default: False)) – Force protein values to sum to one in every single cell (post-hoc normalization)

  • sample_protein_mixing (bool (default: False)) – Sample the protein mixture component, i.e., use the parameter to sample a Bernoulli that determines if expression is from foreground/background.

  • include_protein_background (bool (default: False)) – Include the protein background component as part of the protein expression

  • **kwargs – Keyword args for scvi.model.base.DifferentialComputation.get_bayes_factors()

Return type:

DataFrame

Returns:

Differential expression DataFrame.

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

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 (Union[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

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

Compute the evidence lower bound (ELBO) on the data.

The ELBO is the reconstruction error plus the Kullback-Leibler (KL) divergences between the variational distributions and the priors. It is different from the marginal log-likelihood; specifically, it is a lower bound on the marginal log-likelihood plus a term that is constant with respect to the variational distribution. It still gives good insights on the modeling of the data and is fast to compute.

Parameters:
  • adata (AnnData | None (default: None)) – AnnData object with var_names in the same order as the ones used to train the model. If None and dataloader is also None, it defaults to the object used to initialize the model.

  • indices (Sequence[int] | None (default: None)) – Indices of observations in adata to use. If None, defaults to all observations. Ignored if dataloader is not None.

  • batch_size (int | None (default: None)) – Minibatch size for the forward pass. If None, defaults to scvi.settings.batch_size. Ignored if dataloader is not None.

  • dataloader (Iterator[dict[str, Tensor | None]] (default: None)) – An iterator over minibatches of data on which to compute the metric. The minibatches should be formatted as a dictionary of Tensor with keys as expected by the model. If None, a dataloader is created from adata.

  • **kwargs – Additional keyword arguments to pass into the forward method of the module.

Return type:

float

Returns:

Evidence lower bound (ELBO) of the data.

Notes

This is not the negative ELBO, so higher is better.

TOTALVI.get_feature_correlation_matrix(adata=None, indices=None, n_samples=10, batch_size=64, rna_size_factor=1000, transform_batch=None, correlation_type='spearman', log_transform=False)[source]#

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

Parameters:
  • adata (default: None) – AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

  • indices (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]] | None (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 (Literal['spearman', 'pearson'] (default: 'spearman')) – One of “pearson”, “spearman”.

  • log_transform (bool (default: False)) – Whether to log transform denoised values prior to correlation calculation.

Return type:

DataFrame

Returns:

Gene-protein-gene-protein correlation matrix

TOTALVI.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 (Union[AnnData, MuData]) – AnnData to pull data from.

Return type:

ndarray

Returns:

The requested data as a NumPy array.

TOTALVI.get_latent_library_size(adata=None, indices=None, give_mean=True, batch_size=None)[source]#

Returns the latent library size for each cell.

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

Parameters:
  • adata (AnnData | None (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] | None (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 | None (default: None)) – Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

Return type:

ndarray

TOTALVI.get_latent_representation(adata=None, indices=None, give_mean=True, mc_samples=5000, batch_size=None, return_dist=False, dataloader=None)[source]#

Compute the latent representation of the data.

This is typically denoted as \(z_n\).

Parameters:
  • adata (AnnData | None (default: None)) – AnnData object with var_names in the same order as the ones used to train the model. If None and dataloader is also None, it defaults to the object used to initialize the model.

  • indices (Sequence[int] | None (default: None)) – Indices of observations in adata to use. If None, defaults to all observations. Ignored if dataloader is not None

  • give_mean (bool (default: True)) – If True, returns the mean of the latent distribution. If False, returns an estimate of the mean using mc_samples Monte Carlo samples.

  • mc_samples (int (default: 5000)) – Number of Monte Carlo samples to use for the estimator for distributions with no closed-form mean (e.g., the logistic normal distribution). Not used if give_mean is True or if return_dist is True.

  • batch_size (int | None (default: None)) – Minibatch size for the forward pass. If None, defaults to scvi.settings.batch_size. Ignored if dataloader is not None

  • return_dist (bool (default: False)) – If True, returns the mean and variance of the latent distribution. Otherwise, returns the mean of the latent distribution.

  • dataloader (Iterator[dict[str, Tensor | None]] (default: None)) – An iterator over minibatches of data on which to compute the metric. The minibatches should be formatted as a dictionary of Tensor with keys as expected by the model. If None, a dataloader is created from adata.

Return type:

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]] | tuple[ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]], ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]]

Returns:

An array of shape (n_obs, n_latent) if return_dist is False. Otherwise, returns a tuple of arrays (n_obs, n_latent) with the mean and variance of the latent distribution.

TOTALVI.get_likelihood_parameters(adata=None, indices=None, n_samples=1, give_mean=False, batch_size=None)[source]#

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

Parameters:
  • adata (AnnData | None (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] | None (default: None)) – Indices of cells in adata to use. If None, all cells are used.

  • n_samples (int | None (default: 1)) – Number of posterior samples to use for estimation.

  • give_mean (bool | None (default: False)) – Return expected value of parameters or a samples

  • batch_size (int | None (default: None)) – Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

Return type:

dict[str, ndarray]

TOTALVI.get_marginal_ll(adata=None, indices=None, n_mc_samples=1000, batch_size=None, return_mean=True, dataloader=None, **kwargs)[source]#

Compute the marginal log-likehood of the data.

The computation here is a biased estimator of the marginal log-likelihood of the data.

Parameters:
  • adata (AnnData | None (default: None)) – AnnData object with var_names in the same order as the ones used to train the model. If None and dataloader is also None, it defaults to the object used to initialize the model.

  • indices (Sequence[int] | None (default: None)) – Indices of observations in adata to use. If None, defaults to all observations. Ignored if dataloader is not None.

  • n_mc_samples (int (default: 1000)) – Number of Monte Carlo samples to use for the estimator. Passed into the module’s marginal_ll method.

  • batch_size (int | None (default: None)) – Minibatch size for the forward pass. If None, defaults to scvi.settings.batch_size. Ignored if dataloader is not None.

  • return_mean (bool (default: True)) – Whether to return the mean of the marginal log-likelihood or the marginal-log likelihood for each observation.

  • dataloader (Iterator[dict[str, Tensor | None]] (default: None)) – An iterator over minibatches of data on which to compute the metric. The minibatches should be formatted as a dictionary of Tensor with keys as expected by the model. If None, a dataloader is created from adata.

  • **kwargs – Additional keyword arguments to pass into the module’s marginal_ll method.

Return type:

float | Tensor

Returns:

If True, returns the mean marginal log-likelihood. Otherwise returns a tensor of shape (n_obs,) with the marginal log-likelihood for each observation.

Notes

This is not the negative log-likelihood, so higher is better.

TOTALVI.get_normalized_expression(adata=None, indices=None, n_samples_overall=None, transform_batch=None, gene_list=None, protein_list=None, library_size=1, n_samples=1, sample_protein_mixing=False, scale_protein=False, include_protein_background=False, batch_size=None, return_mean=True, return_numpy=None)[source]#

Returns the normalized gene expression and protein expression.

This is denoted as \(\rho_n\) in the totalVI paper for genes, and TODO for proteins, \((1-\pi_{nt})\alpha_{nt}\beta_{nt}\).

Parameters:
  • adata (default: None) – AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

  • indices (default: None) – Indices of cells in adata to use. If None, all cells are used.

  • n_samples_overall (int | None (default: None)) – Number of samples to use in total

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

    Batch to condition on. If transform_batch is:

    • None, then real observed batch is used

    • int, then batch transform_batch is used

    • List[int], then average over batches in list

  • gene_list (Sequence[str] | None (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.

  • protein_list (Sequence[str] | None (default: None)) – Return protein expression for a subset of genes. This can save memory when working with large datasets and few genes are of interest.

  • library_size (Union[float, Literal['latent'], None] (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.

  • n_samples (int (default: 1)) – Get sample scale from multiple samples.

  • sample_protein_mixing (bool (default: False)) – Sample mixing bernoulli, setting background to zero

  • scale_protein (bool (default: False)) – Make protein expression sum to 1

  • include_protein_background (bool (default: False)) – Include background component for protein expression

  • batch_size (int | None (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 | None (default: None)) – Return a np.ndarray instead of a pd.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:

tuple[ndarray | DataFrame, ndarray | DataFrame]

Returns:

  • gene_normalized_expression - normalized expression for RNA

  • protein_normalized_expression - normalized expression for proteins

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

TOTALVI.get_protein_background_mean(adata, indices, batch_size)[source]#

Get protein background mean.

TOTALVI.get_protein_foreground_probability(adata=None, indices=None, transform_batch=None, protein_list=None, n_samples=1, batch_size=None, return_mean=True, return_numpy=None)[source]#

Returns the foreground probability for proteins.

This is denoted as \((1 - \pi_{nt})\) in the totalVI paper.

Parameters:
  • adata (AnnData | None (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] | None (default: None)) – Indices of cells in adata to use. If None, all cells are used.

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

    Batch to condition on. If transform_batch is:

    • None, then real observed batch is used

    • int, then batch transform_batch is used

    • List[int], then average over batches in list

  • protein_list (Sequence[str] | None (default: None)) – Return protein expression for a subset of genes. This can save memory when working with large datasets and few genes are of interest.

  • n_samples (int (default: 1)) – Number of posterior samples to use for estimation.

  • batch_size (int | None (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 | None (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.

Returns:

  • foreground_probability - probability foreground for each protein

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.

TOTALVI.get_reconstruction_error(adata=None, indices=None, batch_size=None, dataloader=None, **kwargs)[source]#

Compute the reconstruction error on the data.

The reconstruction error is the negative log likelihood of the data given the latent variables. It is different from the marginal log-likelihood, but still gives good insights on the modeling of the data and is fast to compute. This is typically written as \(p(x \mid z)\), the likelihood term given one posterior sample.

Parameters:
  • adata (AnnData | None (default: None)) – AnnData object with var_names in the same order as the ones used to train the model. If None and dataloader is also None, it defaults to the object used to initialize the model.

  • indices (Sequence[int] | None (default: None)) – Indices of observations in adata to use. If None, defaults to all observations. Ignored if dataloader is not None

  • batch_size (int | None (default: None)) – Minibatch size for the forward pass. If None, defaults to scvi.settings.batch_size. Ignored if dataloader is not None

  • dataloader (Iterator[dict[str, Tensor | None]] (default: None)) – An iterator over minibatches of data on which to compute the metric. The minibatches should be formatted as a dictionary of Tensor with keys as expected by the model. If None, a dataloader is created from adata.

  • **kwargs – Additional keyword arguments to pass into the forward method of the module.

Return type:

dict[str, float]

Returns:

Reconstruction error for the data.

Notes

This is not the negative reconstruction error, so higher is better.

classmethod TOTALVI.load(dir_path, adata=None, accelerator='auto', device='auto', prefix=None, backup_url=None)[source]#

Instantiate a model from the saved output.

Parameters:
  • dir_path (str) – Path to saved outputs.

  • adata (Union[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_....
classmethod TOTALVI.load_query_data(adata, reference_model, inplace_subset_query_vars=False, accelerator='auto', device='auto', unfrozen=False, freeze_dropout=False, freeze_expression=True, freeze_decoder_first_layer=True, freeze_batchnorm_encoder=True, freeze_batchnorm_decoder=False, freeze_classifier=True)[source]#

Online update of a reference model with scArches algorithm [Lotfollahi et al., 2021].

Parameters:
  • adata (Union[AnnData, MuData]) – 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 registry.

  • reference_model (Union[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.

  • accelerator (str (default: 'auto')) – Supports passing different accelerator types (“cpu”, “gpu”, “tpu”, “ipu”, “hpu”, “mps, “auto”) as well as custom accelerator instances.

  • device (Union[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.

  • 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.

static TOTALVI.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 | None (default: None)) – Prefix of saved file names.

Return type:

dict

Returns:

The full registry saved with the model

TOTALVI.posterior_predictive_sample(adata=None, indices=None, n_samples=1, batch_size=None, gene_list=None, protein_list=None)[source]#

Generate observation samples from the posterior predictive distribution.

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

Parameters:
  • adata (AnnData | None (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] | None (default: None)) – Indices of cells in adata to use. If None, all cells are used.

  • n_samples (int (default: 1)) – Number of required samples for each cell

  • batch_size (int | None (default: None)) – Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

  • gene_list (Sequence[str] | None (default: None)) – Names of genes of interest

  • protein_list (Sequence[str] | None (default: None)) – Names of proteins of interest

Return type:

ndarray

Returns:

x_new : ndarray tensor with shape (n_cells, n_genes, n_samples)

static TOTALVI.prepare_query_anndata(adata, reference_model, return_reference_var_names=False, inplace=True)[source]#

Prepare data for query integration.

This function will return a new AnnData object with padded zeros for missing features, as well as correctly sorted features.

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 registry.

  • reference_model (Union[str, BaseModelClass]) – Either an already instantiated model of the same class, or a path to saved outputs for reference model.

  • return_reference_var_names (bool (default: False)) – Only load and return reference var names if True.

  • inplace (bool (default: True)) – Whether to subset and rearrange query vars inplace or return new AnnData.

Return type:

Union[AnnData, Index, None]

Returns:

Query adata ready to use in load_query_data unless return_reference_var_names in which case a pd.Index of reference var names is returned.

static TOTALVI.prepare_query_mudata(mdata, reference_model, return_reference_var_names=False, inplace=True)[source]#

Prepare multimodal dataset for query integration.

This function will return a new MuData object such that the AnnData objects for individual modalities are given padded zeros for missing features, as well as correctly sorted features.

Parameters:
  • mdata (MuData) – MuData organized in the same way as data used to train model. It is not necessary to run setup_mudata, as MuData is validated against the registry.

  • reference_model (Union[str, BaseModelClass]) – Either an already instantiated model of the same class, or a path to saved outputs for reference model.

  • return_reference_var_names (bool (default: False)) – Only load and return reference var names if True.

  • inplace (bool (default: True)) – Whether to subset and rearrange query vars inplace or return new MuData.

Return type:

Union[MuData, dict[str, Index], None]

Returns:

Query mudata ready to use in load_query_data unless return_reference_var_names in which case a dictionary of pd.Index of reference var names is returned.

classmethod TOTALVI.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.

TOTALVI.save(dir_path, prefix=None, overwrite=False, save_anndata=False, save_kwargs=None, legacy_mudata_format=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 | 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 TOTALVI.setup_anndata(adata, protein_expression_obsm_key, protein_names_uns_key=None, batch_key=None, layer=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:
  • adata (AnnData) – AnnData object. Rows represent cells, columns represent features.

  • protein_expression_obsm_key (str) – key in adata.obsm for protein expression data.

  • protein_names_uns_key (str | None (default: None)) – key in adata.uns for protein names. If None, will use the column names of adata.obsm[protein_expression_obsm_key] if it is a DataFrame, else will assign sequential names to proteins.

  • batch_key (str | None (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.

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

  • size_factor_key (str | None (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] | None (default: None)) – keys in adata.obs that correspond to categorical data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.

  • continuous_covariate_keys (list[str] | None (default: None)) – keys in adata.obs that correspond to continuous data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.

Returns:

None. Adds the following fields:

.uns[‘_scvi’]

scvi setup dictionary

.obs[‘_scvi_labels’]

labels encoded as integers

.obs[‘_scvi_batch’]

batch encoded as integers

classmethod TOTALVI.setup_mudata(mdata, rna_layer=None, protein_layer=None, batch_key=None, size_factor_key=None, categorical_covariate_keys=None, continuous_covariate_keys=None, modalities=None, **kwargs)[source]#

Sets up the MuData 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:
  • mdata (MuData) – MuData object. Rows represent cells, columns represent features.

  • rna_layer (str | None (default: None)) – RNA layer key. If None, will use .X of specified modality key.

  • protein_layer (str | None (default: None)) – Protein layer key. If None, will use .X of specified modality key.

  • batch_key (str | None (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.

  • size_factor_key (str | None (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] | None (default: None)) – keys in adata.obs that correspond to categorical data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.

  • continuous_covariate_keys (list[str] | None (default: None)) – keys in adata.obs that correspond to continuous data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.

  • modalities (dict[str, str] | None (default: None)) – Dictionary mapping parameters to modalities.

Examples

>>> mdata = muon.read_10x_h5("pbmc_10k_protein_v3_filtered_feature_bc_matrix.h5")
>>> scvi.model.TOTALVI.setup_mudata(
        mdata, modalities={"rna_layer": "rna": "protein_layer": "prot"}
    )
>>> vae = scvi.model.TOTALVI(mdata)
TOTALVI.to_device(device)[source]#

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
TOTALVI.train(max_epochs=None, lr=0.004, accelerator='auto', devices='auto', train_size=0.9, validation_size=None, shuffle_set_split=True, batch_size=256, early_stopping=True, check_val_every_n_epoch=None, reduce_lr_on_plateau=True, n_steps_kl_warmup=None, n_epochs_kl_warmup=None, adversarial_classifier=None, datasplitter_kwargs=None, plan_kwargs=None, **kwargs)[source]#

Trains the model using amortized variational inference.

Parameters:
  • max_epochs (int | None (default: None)) – Number of passes through the dataset.

  • lr (float (default: 0.004)) – 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.

  • train_size (float (default: 0.9)) – Size of training set in the range [0.0, 1.0].

  • validation_size (float | None (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.

  • 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: 256)) – Minibatch size to use during training.

  • early_stopping (bool (default: True)) – Whether to perform early stopping with respect to the validation set.

  • check_val_every_n_epoch (int | None (default: None)) – Check val every n train epochs. By default, val is not checked, unless early_stopping is True or reduce_lr_on_plateau is True. If either of the latter conditions are met, val is checked every epoch.

  • reduce_lr_on_plateau (bool (default: True)) – Reduce learning rate on plateau of validation metric (default is ELBO).

  • n_steps_kl_warmup (int | None (default: None)) – Number of training steps (minibatches) to scale weight on KL divergences from 0 to 1. Only activated when n_epochs_kl_warmup is set to None. If None, defaults to floor(0.75 * adata.n_obs).

  • n_epochs_kl_warmup (int | None (default: None)) – Number of epochs to scale weight on KL divergences from 0 to 1. Overrides n_steps_kl_warmup when both are not None.

  • adversarial_classifier (bool | None (default: None)) – Whether to use adversarial classifier in the latent space. This helps mixing when there are missing proteins in any of the batches. Defaults to True is missing proteins are detected.

  • datasplitter_kwargs (dict | None (default: None)) – Additional keyword arguments passed into DataSplitter.

  • plan_kwargs (dict | None (default: None)) – Keyword args for AdversarialTrainingPlan. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.

  • **kwargs – Other keyword args for Trainer.

TOTALVI.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 (Union[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 TOTALVI.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 | None (default: None)) – Prefix of saved file names.

Return type:

None