scvi.external.GIMVI#
- class scvi.external.GIMVI(adata_seq, adata_spatial, generative_distributions=None, model_library_size=None, n_latent=10, **model_kwargs)[source]#
Joint VAE for imputing missing genes in spatial data [Lopez et al., 2019].
- Parameters:
adata_seq (AnnData) – AnnData object that has been registered via
setup_anndata()
and contains RNA-seq data.adata_spatial (AnnData) – AnnData object that has been registered via
setup_anndata()
and contains spatial data.n_hidden – Number of nodes per hidden layer.
generative_distributions (Optional[List[str]]) – List of generative distribution for adata_seq data and adata_spatial data. Defaults to [‘zinb’, ‘nb’].
model_library_size (Optional[List[bool]]) – List of bool of whether to model library size for adata_seq and adata_spatial. Defaults to [True, False].
n_latent (int) – Dimensionality of the latent space.
**model_kwargs – Keyword args for
JVAE
Examples
>>> adata_seq = anndata.read_h5ad(path_to_anndata_seq) >>> adata_spatial = anndata.read_h5ad(path_to_anndata_spatial) >>> scvi.external.GIMVI.setup_anndata(adata_seq) >>> scvi.external.GIMVI.setup_anndata(adata_spatial) >>> vae = scvi.model.GIMVI(adata_seq, adata_spatial) >>> vae.train(n_epochs=400)
Notes
See further usage examples in the following tutorials:
/user_guide/notebooks/gimvi_tutorial
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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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 GIMVI model (<v0.15.0) to the updated save format. |
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Retrieves the |
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Return the ELBO for the data. |
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Returns the object in AnnData associated with the key in the data registry. |
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Return imputed values for all genes for each dataset. |
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Return the latent space embedding for each dataset. |
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Return the marginal LL for the data. |
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Return the reconstruction error for the data. |
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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#
adata
adata_manager
device
history
is_trained
test_indices
train_indices
validation_indices
Methods#
convert_legacy_save
- classmethod GIMVI.convert_legacy_save(dir_path, output_dir_path, overwrite=False, prefix=None)[source]#
Converts a legacy saved GIMVI model (<v0.15.0) to the updated save format.
get_anndata_manager
- GIMVI.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.
get_elbo
- GIMVI.get_elbo(adata=None, indices=None, batch_size=None)[source]#
Return the ELBO for the data.
The ELBO is a lower bound on the log likelihood of the data used for optimization of VAEs. Note, this is not the negative ELBO, higher is better.
- Parameters:
adata (Optional[AnnData]) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Optional[Sequence[int]]) – Indices of cells in adata to use. If
None
, all cells are used.batch_size (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Return type:
get_from_registry
- GIMVI.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.
get_imputed_values
- GIMVI.get_imputed_values(adatas=None, deterministic=True, normalized=True, decode_mode=None, batch_size=128)[source]#
Return imputed values for all genes for each dataset.
- Parameters:
adatas (List[AnnData]) – List of adata seq and adata spatial
deterministic (bool) – If true, use the mean of the encoder instead of a Gaussian sample for the latent vector.
normalized (bool) – Return imputed normalized values or not.
decode_mode (Optional[int]) – If a
decode_mode
is given, use the encoder specific to each dataset as usual but use the decoder of the dataset of iddecode_mode
to impute values.batch_size (int) – Minibatch size for data loading into model.
- Return type:
get_latent_representation
- GIMVI.get_latent_representation(adatas=None, deterministic=True, batch_size=128)[source]#
Return the latent space embedding for each dataset.
get_marginal_ll
- GIMVI.get_marginal_ll(adata=None, indices=None, n_mc_samples=1000, batch_size=None)[source]#
Return the marginal LL for the data.
The computation here is a biased estimator of the marginal log likelihood of the data. Note, this is not the negative log likelihood, higher is better.
- Parameters:
adata (Optional[AnnData]) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Optional[Sequence[int]]) – 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 (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Return type:
get_reconstruction_error
- GIMVI.get_reconstruction_error(adata=None, indices=None, batch_size=None)[source]#
Return the reconstruction error for the data.
This is typically written as \(p(x \mid z)\), the likelihood term given one posterior sample. Note, this is not the negative likelihood, higher is better.
- Parameters:
adata (Optional[AnnData]) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Optional[Sequence[int]]) – Indices of cells in adata to use. If
None
, all cells are used.batch_size (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Return type:
load
- classmethod GIMVI.load(dir_path, adata_seq=None, adata_spatial=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_seq (Optional[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 savedscvi
setup dictionary. AnnData must be registered viasetup_anndata()
.adata_spatial (Optional[AnnData]) – AnnData organized in the same way as data used to train model. If None, will check for and load anndata saved with the model.
use_gpu (Optional[Union[str, int, bool]]) – 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).
backup_url (Optional[str]) – URL to retrieve saved outputs from if not present on disk.
- Returns:
Model with loaded state dictionaries.
Examples
>>> vae = GIMVI.load(adata_seq, adata_spatial, save_path) >>> vae.get_latent_representation()
load_registry
- static GIMVI.load_registry(dir_path, prefix=None)[source]#
Return the full registry saved with the model.
register_manager
- classmethod GIMVI.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) –
save
- GIMVI.save(dir_path, prefix=None, overwrite=False, save_anndata=False, **anndata_write_kwargs)[source]#
Save the state of the model.
Neither the trainer optimizer state nor the trainer history are saved. Model files are not expected to be reproducibly saved and loaded across versions until we reach version 1.0.
- Parameters:
dir_path (str) – Path to a directory.
prefix (Optional[str]) – 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
anndata_write_kwargs – Kwargs for anndata write function
setup_anndata
- classmethod GIMVI.setup_anndata(adata, batch_key=None, labels_key=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 (Optional[str]) – key in
adata.obs
for batch information. Categories will automatically be converted into integer categories and saved toadata.obs['_scvi_batch']
. IfNone
, assigns the same batch to all the data.labels_key (Optional[str]) – key in
adata.obs
for label information. Categories will automatically be converted into integer categories and saved toadata.obs['_scvi_labels']
. IfNone
, assigns the same label to all the data.layer (Optional[str]) – if not
None
, uses this as the key inadata.layers
for raw count data.adata (AnnData) –
to_device
- GIMVI.to_device(device)[source]#
Move model to device.
- Parameters:
device (Union[str, int]) – Device to move model to. Options: ‘cpu’ for CPU, integer GPU index (eg. 0), or ‘cuda:X’ where X is the GPU index (eg. ‘cuda:0’). See torch.device for more info.
Examples
>>> adata = scvi.data.synthetic_iid() >>> model = scvi.model.SCVI(adata) >>> model.to_device('cpu') # moves model to CPU >>> model.to_device('cuda:0') # moves model to GPU 0 >>> model.to_device(0) # also moves model to GPU 0
train
- GIMVI.train(max_epochs=200, use_gpu=None, kappa=5, train_size=0.9, validation_size=None, batch_size=128, plan_kwargs=None, **kwargs)[source]#
Train the model.
- Parameters:
max_epochs (int) – Number of passes through the dataset. If
None
, defaults tonp.min([round((20000 / n_cells) * 400), 400])
use_gpu (Optional[Union[str, int, bool]]) – 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).kappa (int) – Scaling parameter for the discriminator loss.
train_size (float) – Size of training set in the range [0.0, 1.0].
validation_size (Optional[float]) – 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.
plan_kwargs (Optional[dict]) – Keyword args for model-specific Pytorch Lightning task. Keyword arguments passed to
train()
will overwrite values present inplan_kwargs
, when appropriate.**kwargs – Other keyword args for
Trainer
.
view_anndata_setup
- GIMVI.view_anndata_setup(adata=None, hide_state_registries=False)[source]#
Print summary of the setup for the initial AnnData or a given AnnData object.
view_setup_args