scvi.external.SCAR#

class scvi.external.SCAR(adata, ambient_profile=None, n_hidden=150, n_latent=15, n_layers=2, dropout_rate=0.0, gene_likelihood='b', latent_distribution='normal', scale_activation='softplus_sp', sparsity=0.9, **model_kwargs)[source]#

Ambient RNA removal in scRNA-seq data [Sheng et al., 2022].

Original Github: https://github.com/Novartis/scar. The models are parameter matched in architecture, activations, dropout, sparsity, and batch normalization.

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

  • ambient_profile (Union[str, ndarray, DataFrame, tensor, None] (default: None)) – The probability of occurrence of each ambient transcript. If None, averaging cells to estimate the ambient profile, by default None.

  • n_hidden (int (default: 150)) – Number of nodes per hidden layer.

  • n_latent (int (default: 15)) – Dimensionality of the latent space.

  • n_layers (int (default: 2)) – Number of hidden layers used for encoder and decoder NNs.

  • dropout_rate (float (default: 0.0)) – Dropout rate for neural networks.

  • gene_likelihood (Literal[‘zinb’, ‘nb’, ‘b’, ‘poisson’] (default: 'b')) – One of: * 'b' - Binomial distribution * '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)

  • scale_activation (Literal[‘softmax’, ‘softplus’, ‘softplus_sp’] (default: 'softplus_sp')) – Activation layer to use for px_scale_decoder

  • sparsity (float (default: 0.9)) – The sparsity of expected native signals. It varies between datasets, e.g. if one prefilters genes – use only highly variable genes – the sparsity should be low; on the other hand, it should be set high in the case of unflitered genes.

  • **model_kwargs – Keyword args for SCAR

Examples

>>> adata = anndata.read_h5ad(path_to_anndata)
>>> raw_adata = anndata.read_h5ad(path_to_raw_anndata)
>>> scvi_external.SCAR.setup_anndata(adata, batch_key="batch")
>>> scvi_external.SCAR.get_ambient_profile(adata=adata, raw_adata=raw_adata, prob=0.995)
>>> vae = scvi_external.SCAR(adata)
>>> vae.train()
>>> adata.obsm["X_scAR"] = vae.get_latent_representation()
>>> adata.layers['denoised'] = vae.get_denoised_counts()

Attributes table#

adata

Data attached to model instance.

adata_manager

Manager instance associated with self.adata.

device

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

history

Returns computed metrics during training.

is_trained

Whether the model has been trained.

test_indices

Observations that are in test set.

train_indices

Observations that are in train set.

validation_indices

Observations that are in validation set.

Methods table#

convert_legacy_save(dir_path, output_dir_path)

Converts a legacy saved model (<v0.15.0) to the updated save format.

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

get_ambient_profile(adata, raw_adata[, ...])

Calculate ambient profile for relevant features.

get_anndata_manager(adata[, required])

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

get_denoised_counts([adata, n_samples, ...])

Generate observation samples from the posterior predictive distribution.

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_registry(dir_path[, prefix])

Return the full registry saved with the model.

posterior_predictive_sample([adata, ...])

Generate observation samples from the posterior predictive distribution.

register_manager(adata_manager)

Registers an AnnDataManager instance with this model class.

save(dir_path[, prefix, overwrite, save_anndata])

Save the state of the model.

setup_anndata(adata[, layer, size_factor_key])

Sets up the AnnData object for this model.

to_device(device)

Move model to device.

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

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

SCAR.adata[source]#

Data attached to model instance.

Return type:

Union[AnnData, MuData]

adata_manager

SCAR.adata_manager[source]#

Manager instance associated with self.adata.

Return type:

AnnDataManager

device

SCAR.device[source]#

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

Return type:

str

history

SCAR.history[source]#

Returns computed metrics during training.

is_trained

SCAR.is_trained[source]#

Whether the model has been trained.

Return type:

bool

test_indices

SCAR.test_indices[source]#

Observations that are in test set.

Return type:

ndarray

train_indices

SCAR.train_indices[source]#

Observations that are in train set.

Return type:

ndarray

validation_indices

SCAR.validation_indices[source]#

Observations that are in validation set.

Return type:

ndarray

Methods#

convert_legacy_save

classmethod SCAR.convert_legacy_save(dir_path, output_dir_path, overwrite=False, prefix=None)[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 (Optional[str] (default: None)) – Prefix of saved file names.

Return type:

None

differential_expression

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

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

  • group1 (Optional[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 (Optional[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 (Union[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 (Union[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 (Optional[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 (Optional[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 (Optional[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.

get_ambient_profile

static SCAR.get_ambient_profile(adata, raw_adata, prob=0.995, min_raw_counts=2, iterations=3, n_batch=1, sample=50000)[source]#

Calculate ambient profile for relevant features.

Identify the cell-free droplets through a multinomial distribution. See EmptyDrops [Lun et al., 2019] for details.

Parameters:
  • adata (AnnData) – A filtered adata object, loaded from filtered_feature_bc_matrix using scanpy.read, gene filtering is recommended to save memory.

  • raw_adata (AnnData) – A raw adata object, loaded from raw_feature_bc_matrix using read().

  • prob (float (default: 0.995)) – The probability of each gene, considered as containing ambient RNA if greater than prob (joint prob euqals to the product of all genes for a droplet), by default 0.995.

  • min_raw_counts (int (default: 2)) – Total counts filter for raw_adata, filtering out low counts to save memory, by default 2.

  • iterations (int (default: 3)) – Total iterations, by default 3.

  • n_batch (int (default: 1)) – Total number of batches, set it to a bigger number when out of memory issue occurs, by default 1.

  • sample (int (default: 50000)) – Randomly sample droplets to test, if greater than total droplets, use all droplets, by default 50000.

Returns:

The relevant ambient profile is added in adata.varm

get_anndata_manager

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

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

Requires self.id has been set. Checks for an AnnDataManager specific to this model instance.

Parameters:
  • adata (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:

Optional[AnnDataManager]

get_denoised_counts

SCAR.get_denoised_counts(adata=None, n_samples=1, batch_size=None)[source]#

Generate observation samples from the posterior predictive distribution.

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

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.

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

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

Return type:

ndarray

Returns:

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

get_elbo

SCAR.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

get_feature_correlation_matrix

SCAR.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 (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_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 (Optional[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 (Literal[‘spearman’, ‘pearson’] (default: 'spearman')) – One of “pearson”, “spearman”.

Return type:

DataFrame

Returns:

Gene-gene correlation matrix

get_from_registry

SCAR.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.

get_latent_library_size

SCAR.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 (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)) – Return the mean or a sample from the posterior distribution.

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

Return type:

ndarray

get_latent_representation

SCAR.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 denoted as \(z_n\) in our manuscripts.

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 the distribution parameters of the latent variables rather than their sampled values. If True, ignores give_mean and mc_samples.

Return type:

Union[ndarray, Tuple[ndarray, ndarray]]

Returns:

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

get_likelihood_parameters

SCAR.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 (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_samples (Optional[int] (default: 1)) – Number of posterior samples to use for estimation.

  • give_mean (Optional[bool] (default: False)) – Return expected value of parameters or a samples

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

Return type:

Dict[str, ndarray]

get_marginal_ll

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

Return the marginal LL for the data.

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

Parameters:
  • adata (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 type:

float

get_normalized_expression

SCAR.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)[source]#

Returns the normalized (decoded) gene expression.

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

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.

  • transform_batch (Optional[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 (Optional[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 (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 (Optional[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 (Optional[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:

Union[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

SCAR.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

load

classmethod SCAR.load(dir_path, adata=None, use_gpu=None, prefix=None, backup_url=None)[source]#

Instantiate a model from the saved output.

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

  • adata (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.

  • use_gpu (Union[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 (Optional[str] (default: None)) – Prefix of saved file names.

  • backup_url (Optional[str] (default: None)) – URL to retrieve saved outputs from if not present on disk.

Returns:

Model with loaded state dictionaries.

Examples

>>> model = ModelClass.load(save_path, adata) # use the name of the model class used to save
>>> model.get_....

load_registry

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

Return the full registry saved with the model.

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

  • prefix (Optional[str] (default: None)) – Prefix of saved file names.

Return type:

dict

Returns:

The full registry saved with the model

posterior_predictive_sample

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

Generate observation samples from the posterior predictive distribution.

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

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_samples (int (default: 1)) – Number of samples for each cell.

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

  • batch_size (Optional[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)

register_manager

classmethod SCAR.register_manager(adata_manager)[source]#

Registers an AnnDataManager instance with this model class.

Stores the AnnDataManager reference in a class-specific manager store. Intended for use in the setup_anndata() class method followed up by retrieval of the AnnDataManager via the _get_most_recent_anndata_manager() method in the model init method.

Notes

Subsequent calls to this method with an AnnDataManager instance referring to the same underlying AnnData object will overwrite the reference to previous AnnDataManager.

save

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

Save the state of the model.

Neither the trainer optimizer state nor the trainer history are saved. Model files are not expected to be reproducibly saved and loaded across versions until we reach version 1.0.

Parameters:
  • dir_path (str) – Path to a directory.

  • prefix (Optional[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 SCAR.setup_anndata(adata, layer=None, size_factor_key=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 (Optional[str] (default: None)) – if not None, uses this as the key in adata.layers for raw count data.

  • batch_key – 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 – 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 (Optional[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.

to_device

SCAR.to_device(device)[source]#

Move model to device.

Parameters:

device (Union[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

SCAR.train(max_epochs=None, use_gpu=None, train_size=0.9, validation_size=None, batch_size=128, early_stopping=False, plan_kwargs=None, **trainer_kwargs)[source]#

Train the model.

Parameters:
  • max_epochs (Optional[int] (default: None)) – Number of passes through the dataset. If None, defaults to np.min([round((20000 / n_cells) * 400), 400])

  • use_gpu (Union[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).

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

  • validation_size (Optional[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.

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

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

  • **trainer_kwargs – Other keyword args for Trainer.

view_anndata_setup

SCAR.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

view_setup_args

static SCAR.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 (Optional[str] (default: None)) – Prefix of saved file names.

Return type:

None