velovi.VELOVI#
- class velovi.VELOVI(adata, n_hidden=256, n_latent=10, n_layers=1, dropout_rate=0.1, gamma_init_data=False, linear_decoder=False, **model_kwargs)[source]#
Velocity Variational Inference.
- Parameters:
adata (AnnData) – AnnData object that has been registered via
setup_anndata()
.n_hidden (int) – Number of nodes per hidden layer.
n_latent (int) – Dimensionality of the latent space.
n_layers (int) – Number of hidden layers used for encoder and decoder NNs.
dropout_rate (float) – Dropout rate for neural networks.
gamma_init_data (bool) – Initialize gamma using the data-driven technique.
linear_decoder (bool) – Use a linear decoder from latent space to time.
**model_kwargs – Keyword args for
VELOVAE
Attributes table#
Data attached to model instance. |
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Manager instance associated with self.adata. |
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The current device that the module's params are on. |
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Returns computed metrics during training. |
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Whether the model has been trained. |
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Summary string of the model. |
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Observations that are in test set. |
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Observations that are in train set. |
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Observations that are in validation set. |
Methods table#
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Converts a legacy saved model (<v0.15.0) to the updated save format. |
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Deregisters the |
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Retrieves the |
|
|
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Return the ELBO for the data. |
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Returns the fitted spliced and unspliced abundance (s(t) and u(t)). |
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Returns the object in AnnData associated with the key in the data registry. |
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Returns the likelihood per gene. |
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Return the latent representation for each cell. |
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Returns the cells by genes latent time. |
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Return the marginal LL for the data. |
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Compute permutation scores. |
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Return the reconstruction error for the data. |
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Returns cells by genes by states probabilities. |
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Returns cells by genes velocity estimates. |
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Instantiate a model from the saved output. |
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Return the full registry saved with the model. |
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Registers an |
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Save the state of the model. |
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Sets up the |
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Move model to device. |
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Train the model. |
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Print summary of the setup for the initial AnnData or a given AnnData object. |
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Print args used to setup a saved model. |
Attributes#
Methods#
- classmethod VELOVI.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) – Overwrite existing data or not. If
False
and directory already exists atoutput_dir_path
, error will be raised.prefix (str | None) – Prefix of saved file names.
**save_kwargs – Keyword arguments passed into
save()
.
- Return type:
None
- VELOVI.deregister_manager(adata=None)[source]#
Deregisters the
AnnDataManager
instance associated withadata
.If
adata
isNone
, deregisters allAnnDataManager
instances in both the class and instance-specific manager stores, except for the one associated with this model instance.- Parameters:
adata (AnnData | None) –
- VELOVI.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 anAnnDataManager
specific to this model instance.- Parameters:
- Return type:
AnnDataManager | None
- VELOVI.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 | None) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Sequence[int] | None) – Indices of cells in adata to use. If
None
, all cells are used.batch_size (int | None) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Return type:
- VELOVI.get_expression_fit(adata=None, indices=None, gene_list=None, n_samples=1, batch_size=None, return_mean=True, return_numpy=None, restrict_to_latent_dim=None)[source]#
Returns the fitted spliced and unspliced abundance (s(t) and u(t)).
- Parameters:
adata (AnnData | None) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Sequence[int] | None) – Indices of cells in adata to use. If
None
, all cells are used.gene_list (Sequence[str] | None) – Return frequencies of expression for a subset of genes. This can save memory when working with large datasets and few genes are of interest.
n_samples (int) – Number of posterior samples to use for estimation.
batch_size (int | None) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.return_mean (bool) – Whether to return the mean of the samples.
return_numpy (bool | None) – Return a
ndarray
instead of aDataFrame
. DataFrame includes gene names as columns. If eithern_samples=1
orreturn_mean=True
, defaults toFalse
. Otherwise, it defaults toTrue
.restrict_to_latent_dim (int | None) –
- Returns:
If
n_samples
> 1 andreturn_mean
is False, then the shape is(samples, cells, genes)
. Otherwise, shape is(cells, genes)
. In this case, return type isDataFrame
unlessreturn_numpy
is True.- Return type:
- VELOVI.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.
- VELOVI.get_gene_likelihood(adata=None, indices=None, gene_list=None, n_samples=1, batch_size=None, return_mean=True, return_numpy=None)[source]#
Returns the likelihood per gene. Higher is better.
This is denoted as \(\rho_n\) in the scVI paper.
- Parameters:
adata (AnnData | None) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Sequence[int] | None) – Indices of cells in adata to use. If
None
, all cells are used.transform_batch –
Batch to condition on. If transform_batch is:
None, then real observed batch is used.
int, then batch transform_batch is used.
gene_list (Sequence[str] | None) – Return frequencies of expression for a subset of genes. This can save memory when working with large datasets and few genes are of interest.
library_size – Scale the expression frequencies to a common library size. This allows gene expression levels to be interpreted on a common scale of relevant magnitude. If set to
"latent"
, use the latent libary size.n_samples (int) – Number of posterior samples to use for estimation.
batch_size (int | None) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.return_mean (bool) – Whether to return the mean of the samples.
return_numpy (bool | None) – Return a
ndarray
instead of aDataFrame
. DataFrame includes gene names as columns. If eithern_samples=1
orreturn_mean=True
, defaults toFalse
. Otherwise, it defaults toTrue
.
- Returns:
If
n_samples
> 1 andreturn_mean
is False, then the shape is(samples, cells, genes)
. Otherwise, shape is(cells, genes)
. In this case, return type isDataFrame
unlessreturn_numpy
is True.- Return type:
- VELOVI.get_latent_representation(adata=None, indices=None, give_mean=True, mc_samples=5000, batch_size=None, return_dist=False)[source]#
Return the latent representation for each cell.
This is typically denoted as \(z_n\).
- Parameters:
adata (AnnData | None) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Sequence[int] | None) – Indices of cells in adata to use. If
None
, all cells are used.give_mean (bool) – Give mean of distribution or sample from it.
mc_samples (int) – For distributions with no closed-form mean (e.g.,
logistic normal
), how many Monte Carlo samples to take for computing mean.batch_size (int | None) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.return_dist (bool) – Return (mean, variance) of distributions instead of just the mean. If
True
, ignoresgive_mean
andmc_samples
. In the case of the latter,mc_samples
is used to compute the mean of a transformed distribution. Ifreturn_dist
is true the untransformed mean and variance are returned.
- Returns:
Low-dimensional representation for each cell or a tuple containing its mean and variance.
- Return type:
- VELOVI.get_latent_time(adata=None, indices=None, gene_list=None, time_statistic='mean', n_samples=1, n_samples_overall=None, batch_size=None, return_mean=True, return_numpy=None)[source]#
Returns the cells by genes latent time.
- Parameters:
adata (AnnData | None) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Sequence[int] | None) – Indices of cells in adata to use. If
None
, all cells are used.gene_list (Sequence[str] | None) – Return frequencies of expression for a subset of genes. This can save memory when working with large datasets and few genes are of interest.
time_statistic (Literal['mean', 'max']) – Whether to compute expected time over states, or maximum a posteriori time over maximal probability state.
n_samples (int) – Number of posterior samples to use for estimation.
n_samples_overall (int | None) – Number of overall samples to return. Setting this forces n_samples=1.
batch_size (int | None) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.return_mean (bool) – Whether to return the mean of the samples.
return_numpy (bool | None) – Return a
ndarray
instead of aDataFrame
. DataFrame includes gene names as columns. If eithern_samples=1
orreturn_mean=True
, defaults toFalse
. Otherwise, it defaults toTrue
.
- Returns:
If
n_samples
> 1 andreturn_mean
is False, then the shape is(samples, cells, genes)
. Otherwise, shape is(cells, genes)
. In this case, return type isDataFrame
unlessreturn_numpy
is True.- Return type:
- VELOVI.get_marginal_ll(adata=None, indices=None, n_mc_samples=1000, batch_size=None, return_mean=True, **kwargs)[source]#
Return the marginal LL for the data.
The computation here is a biased estimator of the marginal log likelihood of the data. Note, this is not the negative log likelihood, higher is better.
- Parameters:
adata (AnnData | None) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Sequence[int] | None) – Indices of cells in adata to use. If
None
, all cells are used.n_mc_samples (int) – Number of Monte Carlo samples to use for marginal LL estimation.
batch_size (int | None) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.return_mean (bool | None) – If False, return the marginal log likelihood for each observation. Otherwise, return the mmean arginal log likelihood.
- Return type:
- VELOVI.get_permutation_scores(labels_key, adata=None)[source]#
Compute permutation scores.
- Parameters:
- Returns:
Tuple of DataFrame and AnnData. DataFrame is genes by cell types with score per cell type. AnnData is the permutated version of the original AnnData.
- Return type:
- VELOVI.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 | None) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Sequence[int] | None) – Indices of cells in adata to use. If
None
, all cells are used.batch_size (int | None) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Return type:
- VELOVI.get_state_assignment(adata=None, indices=None, gene_list=None, hard_assignment=False, n_samples=20, batch_size=None, return_mean=True, return_numpy=None)[source]#
Returns cells by genes by states probabilities.
- Parameters:
adata (AnnData | None) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Sequence[int] | None) – Indices of cells in adata to use. If
None
, all cells are used.gene_list (Sequence[str] | None) – Return frequencies of expression for a subset of genes. This can save memory when working with large datasets and few genes are of interest.
hard_assignment (bool) – Return a hard state assignment
n_samples (int) – Number of posterior samples to use for estimation.
batch_size (int | None) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.return_mean (bool) – Whether to return the mean of the samples.
return_numpy (bool | None) – Return a
ndarray
instead of aDataFrame
. DataFrame includes gene names as columns. If eithern_samples=1
orreturn_mean=True
, defaults toFalse
. Otherwise, it defaults toTrue
.
- Returns:
If
n_samples
> 1 andreturn_mean
is False, then the shape is(samples, cells, genes)
. Otherwise, shape is(cells, genes)
. In this case, return type isDataFrame
unlessreturn_numpy
is True.- Return type:
- VELOVI.get_velocity(adata=None, indices=None, gene_list=None, n_samples=1, n_samples_overall=None, batch_size=None, return_mean=True, return_numpy=None, velo_statistic='mean', velo_mode='spliced', clip=True)[source]#
Returns cells by genes velocity estimates.
- Parameters:
adata (AnnData | None) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Sequence[int] | None) – Indices of cells in adata to use. If
None
, all cells are used.gene_list (Sequence[str] | None) – Return velocities for a subset of genes. This can save memory when working with large datasets and few genes are of interest.
n_samples (int) – Number of posterior samples to use for estimation for each cell.
n_samples_overall (int | None) – Number of overall samples to return. Setting this forces n_samples=1.
batch_size (int | None) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.return_mean (bool) – Whether to return the mean of the samples.
return_numpy (bool | None) – Return a
ndarray
instead of aDataFrame
. DataFrame includes gene names as columns. If eithern_samples=1
orreturn_mean=True
, defaults toFalse
. Otherwise, it defaults toTrue
.velo_statistic (str) – Whether to compute expected velocity over states, or maximum a posteriori velocity over maximal probability state.
velo_mode (Literal['spliced', 'unspliced']) – Compute ds/dt or du/dt.
clip (bool) – Clip to minus spliced value
- Returns:
If
n_samples
> 1 andreturn_mean
is False, then the shape is(samples, cells, genes)
. Otherwise, shape is(cells, genes)
. In this case, return type isDataFrame
unlessreturn_numpy
is True.- Return type:
- classmethod VELOVI.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) – 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) – Supports passing different accelerator types
("cpu", "gpu", "tpu", "ipu", "hpu", "mps, "auto")
as well as custom accelerator instances.device (int | str) – The device to use. Can be set to a non-negative index (
int
orstr
) or"auto"
for automatic selection based on the chosen accelerator. If set to"auto"
andaccelerator
is not determined to be"cpu"
, thendevice
will be set to the first available device.prefix (str | None) – Prefix of saved file names.
backup_url (str | 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_....
- static VELOVI.load_registry(dir_path, prefix=None)[source]#
Return the full registry saved with the model.
- classmethod VELOVI.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
.- Parameters:
adata_manager (AnnDataManager) –
- VELOVI.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) – Prefix to prepend to saved file names.
overwrite (bool) – Overwrite existing data or not. If
False
and directory already exists atdir_path
, error will be raised.save_anndata (bool) – If True, also saves the anndata
save_kwargs (dict | None) – Keyword arguments passed into
save()
.anndata_write_kwargs – Kwargs for
write()
- classmethod VELOVI.setup_anndata(adata, spliced_layer, unspliced_layer, **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:
- Returns:
None. Adds the following fields:
- .uns[‘_scvi’]
scvi
setup dictionary- .obs[‘_scvi_labels’]
labels encoded as integers
- .obs[‘_scvi_batch’]
batch encoded as integers
- Return type:
AnnData | None
- VELOVI.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
- VELOVI.train(max_epochs=500, lr=0.01, weight_decay=0.01, accelerator='auto', devices='auto', train_size=0.9, validation_size=None, batch_size=256, early_stopping=True, gradient_clip_val=10, plan_kwargs=None, **trainer_kwargs)[source]#
Train the model.
- Parameters:
max_epochs (int | None) – Number of passes through the dataset. If
None
, defaults tonp.min([round((20000 / n_cells) * 400), 400])
lr (float) – Learning rate for optimization
weight_decay (float) – Weight decay for optimization
accelerator (str) – Supports passing different accelerator types
("cpu", "gpu", "tpu", "ipu", "hpu", "mps, "auto")
as well as custom accelerator instances.devices (int | list[int] | str) – The devices to use. Can be set to a non-negative index (
int
orstr
), a sequence of device indices (list
or comma-separatedstr
), the value-1
to indicate all available devices, or"auto"
for automatic selection based on the chosenaccelerator
. If set to"auto"
andaccelerator
is not determined to be"cpu"
, thendevices
will be set to the first available device.train_size (float) – Size of training set in the range [0.0, 1.0].
validation_size (float | None) – Size of the test set. If
None
, defaults to 1 -train_size
. Iftrain_size + validation_size < 1
, the remaining cells belong to a test set.batch_size (int) – Minibatch size to use during training.
early_stopping (bool) – Perform early stopping. Additional arguments can be passed in
**kwargs
. SeeTrainer
for further options.gradient_clip_val (float) – Val for gradient clipping
plan_kwargs (dict | None) – Keyword args for
TrainingPlan
. Keyword arguments passed totrain()
will overwrite values present inplan_kwargs
, when appropriate.**trainer_kwargs – Other keyword args for
Trainer
.
- VELOVI.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 | None) – AnnData object setup with
setup_anndata
ortransfer_fields()
.hide_state_registries (bool) – If True, prints a shortened summary without details of each state registry.
- Return type:
None