scvi.model.LinearSCVI#

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

Linearly-decoded VAE [Svensson et al., 2020].

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 NN.

  • dropout_rate (float (default: 0.1)) – Dropout rate for neural networks.

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

    One of:

    • 'nb' - Negative binomial distribution

    • 'zinb' - Zero-inflated negative binomial distribution

    • 'poisson' - Poisson distribution

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

    One of:

    • 'normal' - Normal distribution

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

  • **model_kwargs – Keyword args for LDVAE

Examples

>>> adata = anndata.read_h5ad(path_to_anndata)
>>> scvi.model.LinearSCVI.setup_anndata(adata, batch_key="batch")
>>> vae = scvi.model.LinearSCVI(adata)
>>> vae.train()
>>> adata.var["loadings"] = vae.get_loadings()

Notes

See further usage examples in the following tutorials:

  1. Linearly decoded VAE

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])

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_loadings()

Extract per-gene weights in the linear decoder.

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, accelerator, device, ...])

Instantiate a model from the saved output.

load_registry(dir_path[, prefix])

Return the full registry saved with the model.

posterior_predictive_sample([adata, ...])

Generate predictive samples from the posterior predictive distribution.

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[, batch_key, ...])

Sets up the AnnData object for this model.

to_device(device)

Move model to device.

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

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#

LinearSCVI.adata[source]#

Data attached to model instance.

LinearSCVI.adata_manager[source]#

Manager instance associated with self.adata.

LinearSCVI.device[source]#

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

LinearSCVI.history[source]#

Returns computed metrics during training.

LinearSCVI.is_trained[source]#

Whether the model has been trained.

LinearSCVI.summary_string[source]#

Summary string of the model.

LinearSCVI.test_indices[source]#

Observations that are in test set.

LinearSCVI.train_indices[source]#

Observations that are in train set.

LinearSCVI.validation_indices[source]#

Observations that are in validation set.

Methods#

classmethod LinearSCVI.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

LinearSCVI.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.

LinearSCVI.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, weights='uniform', filter_outlier_cells=False, importance_weighting_kwargs=None, **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 (list[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 (list[int] | list[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 (list[int] | list[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 (list[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 (list[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.

  • weights (Optional[Literal['uniform', 'importance']] (default: 'uniform')) – Weights to use for sampling. If None, defaults to “uniform”.

  • filter_outlier_cells (bool (default: False)) – Whether to filter outlier cells with filter_outlier_cells().

  • importance_weighting_kwargs (dict | None (default: None)) – Keyword arguments passed into _get_importance_weights().

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

Return type:

DataFrame

Returns:

Differential expression DataFrame.

LinearSCVI.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

LinearSCVI.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 (Optional[AnnData] (default: None)) – AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

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

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

Return type:

float

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

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

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 (list[int] | None (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 (list[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”.

Return type:

DataFrame

Returns:

Gene-gene correlation matrix

LinearSCVI.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.

LinearSCVI.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 scVI 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 (list[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

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

Return the latent representation for each cell.

This is typically denoted as \(z_n\).

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

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

  • return_dist (bool (default: False)) – Return (mean, variance) of distributions instead of just the mean. If True, ignores give_mean and mc_samples. In the case of the latter, mc_samples is used to compute the mean of a transformed distribution. If return_dist is true the untransformed mean and variance are returned.

Return type:

Union[ndarray, tuple[ndarray, ndarray]]

Returns:

Low-dimensional representation for each cell or a tuple containing its mean and variance.

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

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

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 (list[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]

LinearSCVI.get_loadings()[source]#

Extract per-gene weights in the linear decoder.

Shape is genes by n_latent.

Return type:

DataFrame

LinearSCVI.get_marginal_ll(adata=None, indices=None, n_mc_samples=1000, batch_size=None, return_mean=True, **kwargs)[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 (Optional[AnnData] (default: None)) – AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

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

  • return_mean (Optional[bool] (default: True)) – If False, return the marginal log likelihood for each observation. Otherwise, return the mmean arginal log likelihood.

Return type:

Union[Tensor, float]

LinearSCVI.get_normalized_expression(adata=None, indices=None, transform_batch=None, gene_list=None, library_size=1, n_samples=1, n_samples_overall=None, weights=None, batch_size=None, return_mean=True, return_numpy=None, **importance_weighting_kwargs)[source]#

Returns the normalized (decoded) gene expression.

This is denoted as \(\rho_n\) in the scVI 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 (list[int] | None (default: None)) – Indices of cells in adata to use. If None, all cells are used.

  • transform_batch (list[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.

  • gene_list (list[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.

  • library_size (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 library size.

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

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

  • weights (Optional[Literal['uniform', 'importance']] (default: None)) – Weights to use for sampling. If None, defaults to “uniform”.

  • 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.

  • importance_weighting_kwargs – Keyword arguments passed into _get_importance_weights().

Return type:

ndarray | DataFrame

Returns:

If n_samples is provided and return_mean is False, this method returns a 3d tensor of shape (n_samples, n_cells, n_genes). If n_samples is provided and return_mean is True, it returns a 2d tensor of shape (n_cells, n_genes). In this case, return type is DataFrame unless return_numpy is True. Otherwise, the method expects n_samples_overall to be provided and returns a 2d tensor of shape (n_samples_overall, n_genes).

LinearSCVI.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 (Optional[AnnData] (default: None)) – AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

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

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

Return type:

float

classmethod LinearSCVI.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_....
static LinearSCVI.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

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

Generate predictive samples from the posterior predictive distribution.

The posterior predictive distribution is denoted as \(p(\hat{x} \mid x)\), where \(x\) is the input data and \(\hat{x}\) is the sampled data.

We sample from this distribution by first sampling n_samples times from the posterior distribution \(q(z \mid x)\) for a given observation, and then sampling from the likelihood \(p(\hat{x} \mid z)\) for each of these.

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

  • indices (list[int] | None (default: None)) – Indices of the observations in adata to use. If None, defaults to all the observations.

  • n_samples (int (default: 1)) – Number of Monte Carlo samples to draw from the posterior predictive distribution for each observation.

  • gene_list (list[str] | None (default: None)) – Names of the genes to which to subset. If None, defaults to all genes.

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

Return type:

GCXS

Returns:

Sparse multidimensional array of shape (n_obs, n_vars) if n_samples == 1, else (n_obs, n_vars, n_samples).

classmethod LinearSCVI.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.

LinearSCVI.save(dir_path, prefix=None, overwrite=False, save_anndata=False, save_kwargs=None, **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().

  • anndata_write_kwargs – Kwargs for write()

classmethod LinearSCVI.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:
  • adata (AnnData) – AnnData object. Rows represent cells, columns represent features.

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

LinearSCVI.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
LinearSCVI.train(max_epochs=None, accelerator='auto', devices='auto', train_size=0.9, validation_size=None, shuffle_set_split=True, load_sparse_tensor=False, batch_size=128, early_stopping=False, datasplitter_kwargs=None, plan_kwargs=None, data_module=None, **trainer_kwargs)[source]#

Train the model.

Parameters:
  • max_epochs (int | None (default: None)) – The maximum number of epochs to train the model. The actual number of epochs may be less if early stopping is enabled. If None, defaults to a heuristic based on get_max_epochs_heuristic(). Must be passed in if data_module is passed in, and it does not have an n_obs attribute.

  • 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]. Passed into DataSplitter. Not used if data_module is passed in.

  • 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. Passed into DataSplitter. Not used if data_module is passed in.

  • 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. Passed into DataSplitter. Not used if data_module is passed in.

  • load_sparse_tensor (bool (default: False)) – EXPERIMENTAL If True, loads data with sparse CSR or CSC layout as a Tensor with the same layout. Can lead to speedups in data transfers to GPUs, depending on the sparsity of the data. Passed into DataSplitter. Not used if data_module is passed in.

  • batch_size (int (default: 128)) – Minibatch size to use during training. Passed into DataSplitter. Not used if data_module is passed in.

  • early_stopping (bool (default: False)) – Perform early stopping. Additional arguments can be passed in through **kwargs. See Trainer for further options.

  • datasplitter_kwargs (dict | None (default: None)) – Additional keyword arguments passed into DataSplitter. Values in this argument can be overwritten by arguments directly passed into this method, when appropriate. Not used if data_module is passed in.

  • plan_kwargs (dict | None (default: None)) – Additional keyword arguments passed into TrainingPlan. Values in this argument can be overwritten by arguments directly passed into this method, when appropriate.

  • data_module (LightningDataModule | None (default: None)) – EXPERIMENTAL A LightningDataModule instance to use for training in place of the default DataSplitter. Can only be passed in if the model was not initialized with AnnData.

  • **kwargs – Additional keyword arguments passed into Trainer.

LinearSCVI.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 LinearSCVI.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