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 viasetup_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 networksmodel_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:
Attributes table#
Data attached to model instance. |
|
Manager instance associated with self.adata. |
|
The current device that the module's params are on. |
|
Returns computed metrics during training. |
|
Whether the model has been trained. |
|
Summary string of the model. |
|
Observations that are in test set. |
|
Observations that are in train set. |
|
Observations that are in validation set. |
Methods table#
|
Converts a legacy saved model (<v0.15.0) to the updated save format. |
|
Deregisters the |
|
. |
|
Impute the full accessibility matrix. |
|
Retrieves the |
|
Compute the evidence lower bound (ELBO) on the data. |
|
Returns the object in AnnData associated with the key in the data registry. |
|
Compute the latent representation of the data. |
|
Return library size factors. |
|
Compute the marginal log-likehood of the data. |
|
Compute the reconstruction error on the data. |
Return region-specific factors. |
|
|
Instantiate a model from the saved output. |
|
Online update of a reference model with scArches algorithm [Lotfollahi et al., 2021]. |
|
Return the full registry saved with the model. |
|
Prepare data for query integration. |
|
Prepare multimodal dataset for query integration. |
|
Registers an |
|
Save the state of the model. |
|
Sets up the |
|
Move model to device. |
|
Trains the model using amortized variational inference. |
|
Print summary of the setup for the initial AnnData or a given AnnData object. |
|
Print args used to setup a saved model. |
Attributes#
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. IfFalse
and directory already exists atoutput_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:
- 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 inpandas.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 insetup_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:
- 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 totalregion_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 modelreturn_numpy (
bool
(default:False
)) – If True and threshold=None, returnndarray
. If True and threshold is given, returncsr_matrix
. If False, returnDataFrame
. DataFrame includes regions names as columns.
- Return type:
- PEAKVI.get_anndata_manager(adata, required=False)[source]#
Retrieves the
AnnDataManager
for a given AnnData object.Requires
self.id
has been set. Checks for anAnnDataManager
specific to this model instance.- Parameters:
- Return type:
- PEAKVI.get_elbo(adata=None, indices=None, batch_size=None, dataloader=None, return_mean=True, **kwargs)[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 withvar_names
in the same order as the ones used to train the model. IfNone
anddataloader
is alsoNone
, it defaults to the object used to initialize the model.indices (
Sequence
[int
] |None
(default:None
)) – Indices of observations inadata
to use. IfNone
, defaults to all observations. Ignored ifdataloader
is notNone
.batch_size (
int
|None
(default:None
)) – Minibatch size for the forward pass. IfNone
, defaults toscvi.settings.batch_size
. Ignored ifdataloader
is notNone
.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 ofTensor
with keys as expected by the model. IfNone
, a dataloader is created fromadata
.return_mean (
bool
(default:True
)) – Whether to return the mean of the ELBO or the ELBO for each observation.**kwargs – Additional keyword arguments to pass into the forward method of the module.
- Return type:
- 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.
- 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 withvar_names
in the same order as the ones used to train the model. IfNone
anddataloader
is alsoNone
, it defaults to the object used to initialize the model.indices (
Sequence
[int
] |None
(default:None
)) – Indices of observations inadata
to use. IfNone
, defaults to all observations. Ignored ifdataloader
is notNone
give_mean (
bool
(default:True
)) – IfTrue
, returns the mean of the latent distribution. IfFalse
, returns an estimate of the mean usingmc_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 ifgive_mean
isTrue
or ifreturn_dist
isTrue
.batch_size (
int
|None
(default:None
)) – Minibatch size for the forward pass. IfNone
, defaults toscvi.settings.batch_size
. Ignored ifdataloader
is notNone
return_dist (
bool
(default:False
)) – IfTrue
, 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 ofTensor
with keys as expected by the model. IfNone
, a dataloader is created fromadata
.
- 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)
ifreturn_dist
isFalse
. 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:
- 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 withvar_names
in the same order as the ones used to train the model. IfNone
anddataloader
is alsoNone
, it defaults to the object used to initialize the model.indices (
Sequence
[int
] |None
(default:None
)) – Indices of observations inadata
to use. IfNone
, defaults to all observations. Ignored ifdataloader
is notNone
.n_mc_samples (
int
(default:1000
)) – Number of Monte Carlo samples to use for the estimator. Passed into the module’smarginal_ll
method.batch_size (
int
|None
(default:None
)) – Minibatch size for the forward pass. IfNone
, defaults toscvi.settings.batch_size
. Ignored ifdataloader
is notNone
.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 ofTensor
with keys as expected by the model. IfNone
, a dataloader is created fromadata
.**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, return_mean=True, **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 withvar_names
in the same order as the ones used to train the model. IfNone
anddataloader
is alsoNone
, it defaults to the object used to initialize the model.indices (
Sequence
[int
] |None
(default:None
)) – Indices of observations inadata
to use. IfNone
, defaults to all observations. Ignored ifdataloader
is notNone
batch_size (
int
|None
(default:None
)) – Minibatch size for the forward pass. IfNone
, defaults toscvi.settings.batch_size
. Ignored ifdataloader
is notNone
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 ofTensor
with keys as expected by the model. IfNone
, a dataloader is created fromadata
.return_mean (
bool
(default:True
)) – Whether to return the mean reconstruction loss or the reconstruction loss for each observation.**kwargs – Additional keyword arguments to pass into the forward method of the module.
- Return type:
- Returns:
Reconstruction error for the data.
Notes
This is not the negative reconstruction error, so higher is better.
- 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 (
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
|MuData
) – AnnData organized in the same way as data used to train model. It is not necessary to run setup_anndata, as AnnData is validated against theregistry
.reference_model (
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 (
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 modelfreeze_dropout (
bool
(default:False
)) – Whether to freeze dropout during trainingfreeze_expression (
bool
(default:True
)) – Freeze neurons corersponding to expression in first layerfreeze_decoder_first_layer (
bool
(default:True
)) – Freeze neurons corersponding to first layer in decoderfreeze_batchnorm_encoder (
bool
(default:True
)) – Whether to freeze batchnorm weight and bias during training for encoderfreeze_batchnorm_decoder (
bool
(default:False
)) – Whether to freeze batchnorm weight and bias during training for decoderfreeze_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.
- 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 theregistry
.reference_model (
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:
- Returns:
Query adata ready to use in load_query_data unless return_reference_var_names in which case a pd.Index of reference var names is returned.
- static PEAKVI.prepare_query_mudata(mdata, reference_model, return_reference_var_names=False, inplace=True)[source]#
Prepare multimodal dataset for query integration.
This function will return a new MuData object such that the AnnData objects for individual modalities are given padded zeros for missing features, as well as correctly sorted features.
- Parameters:
mdata (
MuData
) – MuData organized in the same way as data used to train model. It is not necessary to run setup_mudata, as MuData is validated against theregistry
.reference_model (
str
|BaseModelClass
) – Either an already instantiated model of the same class, or a path to saved outputs for reference model.return_reference_var_names (
bool
(default:False
)) – Only load and return reference var names if True.inplace (
bool
(default:True
)) – Whether to subset and rearrange query vars inplace or return new MuData.
- Return type:
- Returns:
Query mudata ready to use in load_query_data unless return_reference_var_names in which case a dictionary of pd.Index of reference var names is returned.
- classmethod 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 thesetup_anndata()
class method followed up by retrieval of theAnnDataManager
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 previousAnnDataManager
.
- PEAKVI.save(dir_path, prefix=None, overwrite=False, save_anndata=False, save_kwargs=None, legacy_mudata_format=False, **anndata_write_kwargs)[source]#
Save the state of the model.
Neither the trainer optimizer state nor the trainer history are saved. Model files are not expected to be reproducibly saved and loaded across versions until we reach version 1.0.
- Parameters:
dir_path (
str
) – Path to a directory.prefix (
str
|None
(default:None
)) – Prefix to prepend to saved file names.overwrite (
bool
(default:False
)) – Overwrite existing data or not. If False and directory already exists at dir_path, error will be raised.save_anndata (
bool
(default:False
)) – If True, also saves the anndatasave_kwargs (
dict
|None
(default:None
)) – Keyword arguments passed intosave()
.legacy_mudata_format (
bool
(default:False
)) – IfTrue
, saves the modelvar_names
in the legacy format if the model was trained with aMuData
object. The legacy format is a flat array with variable names across all modalities concatenated, while the new format is a dictionary with keys corresponding to the modality names and values corresponding to the variable names for each modality.anndata_write_kwargs – Kwargs for
write()
- classmethod 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 optimizationeps (
float
(default:1e-08
)) – Optimizer epsearly_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 stoppingsave_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 procedurecheck_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 intoDataSplitter
.plan_kwargs (
dict
|None
(default:None
)) – Keyword args forTrainingPlan
. 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 useenable_checkpointing
instead.