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 [Ashuach22]

Parameters:
adata : AnnData

AnnData object that has been registered via setup_anndata().

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

Number of nodes per hidden layer. If None, defaults to square root of number of regions.

n_latent : int | NoneOptional[int] (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 : {‘normal’, ‘ln’}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:

  1. 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.

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

A unified method for differential accessibility analysis.

get_accessibility_estimates([adata, ...])

Impute the full accessibility matrix.

get_anndata_manager(adata[, required])

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

get_elbo([adata, indices, batch_size])

Return the ELBO for the data.

get_from_registry(adata, registry_key)

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

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

Return the latent representation for each cell.

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

Return library size factors.

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

Return the marginal LL for the data.

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

Return the reconstruction error for the data.

get_region_factors()

Return region-specific factors.

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

Instantiate a model from the saved output.

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

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

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_anndata])

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, use_gpu, train_size, ...])

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#

adata#

PEAKVI.adata[source]#

Data attached to model instance.

Return type:

AnnData | MuDataUnion[AnnData, MuData]

adata_manager#

PEAKVI.adata_manager[source]#

Manager instance associated with self.adata.

Return type:

AnnDataManager

device#

PEAKVI.device[source]#

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

Return type:

str

history#

PEAKVI.history[source]#

Returns computed metrics during training.

is_trained#

PEAKVI.is_trained[source]#

Whether the model has been trained.

Return type:

bool

test_indices#

PEAKVI.test_indices[source]#

Observations that are in test set.

Return type:

ndarray

train_indices#

PEAKVI.train_indices[source]#

Observations that are in train set.

Return type:

ndarray

validation_indices#

PEAKVI.validation_indices[source]#

Observations that are in validation set.

Return type:

ndarray

Methods#

convert_legacy_save#

classmethod PEAKVI.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 : str | NoneOptional[str] (default: None)

Prefix of saved file names.

Return type:

None

differential_accessibility#

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

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

AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

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

The key of the observations grouping to consider.

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

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

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

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

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

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

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

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

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

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

delta : float (default: 0.05)

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 | NoneOptional[int] (default: None)

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

all_stats : bool (default: True)

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

batch_correction : bool (default: False)

Whether to correct for batch effects in DE inference.

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

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

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

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

fdr_target : float (default: 0.05)

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

silent : bool (default: False)

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

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

get_accessibility_estimates#

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

Indices of cells in adata to use. If None, all cells are used.

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

Number of samples to return in total

region_list : Sequence[str] | NoneOptional[Sequence[str]] (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 : int | str | NoneUnion[int, 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

use_z_mean : bool (default: True)

If True (default), use the distribution mean. Otherwise, sample from the distribution.

threshold : float | NoneOptional[float] (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_matrixUnion[DataFrame, ndarray, csr_matrix]

get_anndata_manager#

PEAKVI.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 : AnnData | MuDataUnion[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 | NoneOptional[AnnDataManager]

get_elbo#

PEAKVI.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 : AnnData | NoneOptional[AnnData] (default: None)

AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

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

Indices of cells in adata to use. If None, all cells are used.

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

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

Return type:

float

get_from_registry#

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 : AnnData | MuDataUnion[AnnData, MuData]

AnnData to pull data from.

Return type:

ndarray

Returns:

The requested data as a NumPy array.

get_latent_representation#

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

Return the latent representation for each cell.

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

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

AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

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

Indices of cells in adata to use. If None, all cells are used.

give_mean : bool (default: True)

Give mean of distribution or sample from it.

mc_samples : int (default: 5000)

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

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

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

Return type:

ndarray

Returns:

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

get_library_size_factors#

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

Return library size factors.

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

AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

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

Indices of cells in adata to use. If None, all cells are used.

batch_size : int (default: 128)

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

Return type:

{str: ndarray}Dict[str, ndarray]

Returns:

Library size factor for expression and accessibility

get_marginal_ll#

PEAKVI.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 : AnnData | NoneOptional[AnnData] (default: None)

AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

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

Indices of cells in adata to use. If None, all cells are used.

n_mc_samples : int (default: 1000)

Number of Monte Carlo samples to use for marginal LL estimation.

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

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

Return type:

float

get_reconstruction_error#

PEAKVI.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 : AnnData | NoneOptional[AnnData] (default: None)

AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

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

Indices of cells in adata to use. If None, all cells are used.

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

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

Return type:

float | {str: float}Union[float, Dict[str, float]]

get_region_factors#

PEAKVI.get_region_factors()[source]#

Return region-specific factors.

load#

classmethod PEAKVI.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 : AnnData | MuData | NoneUnion[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 : str | int | bool | NoneUnion[str, int, bool, None] (default: None)

Load model on default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str), or use CPU (if False).

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

Prefix of saved file names.

backup_url : str | NoneOptional[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_query_data#

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

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

Parameters:
adata : AnnData

AnnData organized in the same way as data used to train model. It is not necessary to run setup_anndata, as AnnData is validated against the registry.

reference_model : str | BaseModelClassUnion[str, BaseModelClass]

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

inplace_subset_query_vars : bool (default: False)

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

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

Load model on default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str), or use CPU (if False).

unfrozen : bool (default: False)

Override all other freeze options for a fully unfrozen model

freeze_dropout : bool (default: False)

Whether to freeze dropout during training

freeze_expression : bool (default: True)

Freeze neurons corersponding to expression in first layer

freeze_decoder_first_layer : bool (default: True)

Freeze neurons corersponding to first layer in decoder

freeze_batchnorm_encoder : bool (default: True)

Whether to freeze batchnorm weight and bias during training for encoder

freeze_batchnorm_decoder : bool (default: False)

Whether to freeze batchnorm weight and bias during training for decoder

freeze_classifier : bool (default: True)

Whether to freeze classifier completely. Only applies to SCANVI.

load_registry#

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 | NoneOptional[str] (default: None)

Prefix of saved file names.

Return type:

dict

Returns:

The full registry saved with the model

prepare_query_anndata#

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 : str | BaseModelClassUnion[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:

AnnData | Index | NoneUnion[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.

register_manager#

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.

save#

PEAKVI.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 : str | NoneOptional[str] (default: None)

Prefix to prepend to saved file names.

overwrite : bool (default: False)

Overwrite existing data or not. If False and directory already exists at dir_path, error will be raised.

save_anndata : bool (default: False)

If True, also saves the anndata

anndata_write_kwargs

Kwargs for write()

setup_anndata#

classmethod 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:
batch_key : str | NoneOptional[str] (default: None)

key in adata.obs for batch information. Categories will automatically be converted into integer categories and saved to adata.obs[‘_scvi_batch’]. If None, assigns the same batch to all the data.

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

if not None, uses this as the key in adata.layers for raw count data.

categorical_covariate_keys : List[str] | NoneOptional[List[str]] (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] | NoneOptional[List[str]] (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.

to_device#

PEAKVI.to_device(device)[source]#

Move model to device.

Parameters:
device : str | intUnion[str, int]

Device to move model to. Options: ‘cpu’ for CPU, integer GPU index (eg. 0), or ‘cuda:X’ where X is the GPU index (eg. ‘cuda:0’). See torch.device for more info.

Examples

>>> adata = scvi.data.synthetic_iid()
>>> model = scvi.model.SCVI(adata)
>>> model.to_device('cpu')      # moves model to CPU
>>> model.to_device('cuda:0')   # moves model to GPU 0
>>> model.to_device(0)          # also moves model to GPU 0

train#

PEAKVI.train(max_epochs=500, lr=0.0001, use_gpu=None, train_size=0.9, validation_size=None, 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, 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.

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

Use default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str, e.g., ‘cuda:0’), or use CPU (if False).

train_size : float (default: 0.9)

Size of training set in the range [0.0, 1.0].

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

Size of the test set. If None, defaults to 1 - train_size. If train_size + validation_size < 1, the remaining cells belong to a test set.

batch_size : int (default: 128)

Minibatch size to use during training.

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)

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 | NoneOptional[int] (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 | NoneOptional[int] (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 | NoneOptional[int] (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.

plan_kwargs : dict | NoneOptional[dict] (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.

view_anndata_setup#

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 : AnnData | MuData | NoneUnion[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 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 | NoneOptional[str] (default: None)

Prefix of saved file names.

Return type:

None