scvi.model.PEAKVI

scvi.model.PEAKVI#

class scvi.model.PEAKVI(adata, n_hidden=None, n_latent=None, n_layers_encoder=2, n_layers_decoder=2, dropout_rate=0.1, model_depth=True, region_factors=True, use_batch_norm='none', use_layer_norm='both', latent_distribution='normal', deeply_inject_covariates=False, encode_covariates=False, **model_kwargs)[source]#

Peak Variational Inference for chromatin accessilibity analysis [Ashuach et al., 2022].

Parameters:

adata (AnnData) – AnnData object that has been registered via setup_anndata().
n_hidden (int | None (default: None)) – Number of nodes per hidden layer. If None, defaults to square root of number of regions.
n_latent (int | None (default: None)) – Dimensionality of the latent space. If None, defaults to square root of n_hidden.
n_layers_encoder (int (default: 2)) – Number of hidden layers used for encoder NN.
n_layers_decoder (int (default: 2)) – Number of hidden layers used for decoder NN.
dropout_rate (float (default: 0.1)) – Dropout rate for neural networks
model_depth (bool (default: True)) – Model sequencing depth / library size (default: True)
region_factors (bool (default: True)) – Include region-specific factors in the model (default: True)
latent_distribution (Literal['normal', 'ln'] (default: 'normal')) –
One of
- 'normal' - Normal distribution (Default)
- 'ln' - Logistic normal distribution (Normal(0, I) transformed by softmax)
deeply_inject_covariates (bool (default: False)) – Whether to deeply inject covariates into all layers of the decoder. If False (default), covariates will only be included in the input layer.
**model_kwargs – Keyword args for PEAKVAE

Examples

>>> adata = anndata.read_h5ad(path_to_anndata)
>>> scvi.model.PEAKVI.setup_anndata(adata, batch_key="batch")
>>> vae = scvi.model.PEAKVI(adata)
>>> vae.train()

Notes

See further usage examples in the following tutorials:

PeakVI: Analyzing scATACseq data

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_accessibility`([adata, groupby, ...])	.
`get_accessibility_estimates`([adata, ...])	Impute the full accessibility matrix.
`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_from_registry`(adata, registry_key)	Returns the object in AnnData associated with the key in the data registry.
`get_latent_representation`([adata, indices, ...])	Compute the latent representation of the data.
`get_library_size_factors`([adata, indices, ...])	Return library size factors.
`get_marginal_ll`([adata, indices, ...])	Compute the marginal log-likehood of the data.
`get_reconstruction_error`([adata, indices, ...])	Compute the reconstruction error on the data.
`get_region_factors`()	Return region-specific factors.
`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.
`prepare_query_anndata`(adata, reference_model)	Prepare data 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[, batch_key, ...])	Sets up the `AnnData` 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#

PEAKVI.adata[source]#: Data attached to model instance.

PEAKVI.adata_manager[source]#: Manager instance associated with self.adata.

PEAKVI.device[source]#: The current device that the module’s params are on.

PEAKVI.history[source]#: Returns computed metrics during training.

PEAKVI.is_trained[source]#: Whether the model has been trained.

PEAKVI.summary_string[source]#: Summary string of the model.

PEAKVI.test_indices[source]#: Observations that are in test set.

PEAKVI.train_indices[source]#: Observations that are in train set.

PEAKVI.validation_indices[source]#: Observations that are in validation set.

Methods#

classmethod PEAKVI.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

PEAKVI.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.

PEAKVI.differential_accessibility(adata=None, groupby=None, group1=None, group2=None, idx1=None, idx2=None, mode='change', delta=0.05, batch_size=None, all_stats=True, batch_correction=False, batchid1=None, batchid2=None, fdr_target=0.05, silent=False, two_sided=True, **kwargs)[source]#

A unified method for differential accessibility 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: 0.05)) – 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.
two_sided (bool (default: True)) – Whether to perform a two-sided test, or a one-sided test.
**kwargs – Keyword args for scvi.model.base.DifferentialComputation.get_bayes_factors()

Return type:

DataFrame

Returns:

Differential accessibility DataFrame with the following columns: prob_da

the probability of the region being differentially accessible

is_da_fdr: whether the region passes a multiple hypothesis correction procedure with the target_fdr threshold
bayes_factor: Bayes Factor indicating the level of significance of the analysis
effect_size: the effect size, computed as (accessibility in population 2) - (accessibility in population 1)
emp_effect: the empirical effect, based on observed detection rates instead of the estimated accessibility scores from the PeakVI model
est_prob1: the estimated probability of accessibility in population 1
est_prob2: the estimated probability of accessibility in population 2
emp_prob1: the empirical (observed) probability of accessibility in population 1
emp_prob2: the empirical (observed) probability of accessibility in population 2

PEAKVI.get_accessibility_estimates(adata=None, indices=None, n_samples_overall=None, region_list=None, transform_batch=None, use_z_mean=True, threshold=None, normalize_cells=False, normalize_regions=False, batch_size=128, return_numpy=False)[source]#

Impute the full accessibility matrix.

Returns a matrix of accessibility probabilities for each cell and genomic region in the input (for return matrix A, A[i,j] is the probability that region j is accessible in cell i).

Parameters:

adata (AnnData | None (default: None)) – AnnData object that has been registered with scvi. If None, defaults to the AnnData object used to initialize the model.
indices (Sequence[int] (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 return in total
region_list (Sequence[str] | None (default: None)) – Return accessibility estimates for this subset of regions. if None, all regions are used. This can save memory when dealing with large datasets.
transform_batch (str | int | 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
use_z_mean (bool (default: True)) – If True (default), use the distribution mean. Otherwise, sample from the distribution.
threshold (float | None (default: None)) – If provided, values below the threshold are replaced with 0 and a sparse matrix is returned instead. This is recommended for very large matrices. Must be between 0 and 1.
normalize_cells (bool (default: False)) – Whether to reintroduce library size factors to scale the normalized probabilities. This makes the estimates closer to the input, but removes the library size correction. False by default.
normalize_regions (bool (default: False)) – Whether to reintroduce region factors to scale the normalized probabilities. This makes the estimates closer to the input, but removes the region-level bias correction. False by default.
batch_size (int (default: 128)) – Minibatch size for data loading into model
return_numpy (bool (default: False)) – If True and threshold=None, return ndarray. If True and threshold is given, return csr_matrix. If False, return DataFrame. DataFrame includes regions names as columns.

Return type:

DataFrame | ndarray | csr_matrix

PEAKVI.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

PEAKVI.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.

PEAKVI.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.

PEAKVI.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.

PEAKVI.get_library_size_factors(adata=None, indices=None, batch_size=128)[source]#

Return library size factors.

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] (default: None)) – Indices of cells in adata to use. If None, all cells are used.
batch_size (int (default: 128)) – Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

Return type:

dict[str, ndarray]

Returns:

Library size factor for expression and accessibility

PEAKVI.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.

PEAKVI.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.

PEAKVI.get_region_factors()[source]#: Return region-specific factors.

classmethod PEAKVI.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 PEAKVI.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 (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.
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 PEAKVI.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

static PEAKVI.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.

classmethod PEAKVI.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.

PEAKVI.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 PEAKVI.setup_anndata(adata, batch_key=None, labels_key=None, categorical_covariate_keys=None, continuous_covariate_keys=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 (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.
labels_key (str | None (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.
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.
layer (str | None (default: None)) – if not None, uses this as the key in adata.layers for raw count data.

PEAKVI.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

PEAKVI.train(max_epochs=500, lr=0.0001, accelerator='auto', devices='auto', train_size=0.9, validation_size=None, shuffle_set_split=True, batch_size=128, weight_decay=0.001, eps=1e-08, early_stopping=True, early_stopping_patience=50, save_best=True, check_val_every_n_epoch=None, n_steps_kl_warmup=None, n_epochs_kl_warmup=50, datasplitter_kwargs=None, plan_kwargs=None, **kwargs)[source]#

Trains the model using amortized variational inference.

Parameters:

max_epochs (int (default: 500)) – Number of passes through the dataset.
lr (float (default: 0.0001)) – 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: 128)) – Minibatch size to use during training.
weight_decay (float (default: 0.001)) – weight decay regularization term for optimization
eps (float (default: 1e-08)) – Optimizer eps
early_stopping (bool (default: True)) – Whether to perform early stopping with respect to the validation set.
early_stopping_patience (int (default: 50)) – How many epochs to wait for improvement before early stopping
save_best (bool (default: True)) – DEPRECATED Save the best model state with respect to the validation loss (default), or use the final state in the training procedure
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. If so, val is checked every epoch.
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: 50)) – Number of epochs to scale weight on KL divergences from 0 to 1. Overrides n_steps_kl_warmup when both are not None.
datasplitter_kwargs (dict | None (default: None)) – Additional keyword arguments passed into DataSplitter.
plan_kwargs (dict | None (default: None)) – Keyword args for TrainingPlan. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.
**kwargs – Other keyword args for Trainer.

Notes

save_best is deprecated in v1.2 and will be removed in v1.3. Please use enable_checkpointing instead.

PEAKVI.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 PEAKVI.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

scvi.model.PEAKVI

Contents

scvi.model.PEAKVI#

Attributes table#

Methods table#

Attributes#

Methods#