scvi.external.SOLO#
- class scvi.external.SOLO(adata, **classifier_kwargs)[source]#
Doublet detection in scRNA-seq [Bernstein et al., 2020].
Most users will initialize the model using the class method
from_scvi_model()
, which takes as input a pre-trainedSCVI
object.- Parameters:
adata (AnnData) – AnnData object that has been registered via
setup_anndata()
. Object should contain latent representation of real cells and doublets asadata.X
. Object should also be registered, using.X
andlabels_key="_solo_doub_sim"
.**classifier_kwargs – Keyword args for
Classifier
Examples
In the case of scVI trained with multiple batches:
>>> adata = anndata.read_h5ad(path_to_anndata) >>> scvi.model.SCVI.setup_anndata(adata, batch_key="batch") >>> vae = scvi.model.SCVI(adata) >>> vae.train() >>> solo_batch_1 = scvi.external.SOLO.from_scvi_model(vae, restrict_to_batch="batch 1") >>> solo_batch_1.train() >>> solo_batch_1.predict()
Otherwise:
>>> adata = anndata.read_h5ad(path_to_anndata) >>> scvi.model.SCVI.setup_anndata(adata) >>> vae = scvi.model.SCVI(adata) >>> vae.train() >>> solo = scvi.external.SOLO.from_scvi_model(vae) >>> solo.train() >>> solo.predict()
Notes
Solo should be trained on one lane of data at a time. An
SCVI
instance that was trained with multiple batches can be used as input, but Solo should be created and run multiple times, each with a newrestrict_to_batch
infrom_scvi_model()
.
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. |
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Whether the model has been trained. |
|
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. |
|
Simulate doublets. |
|
Instantiate a SOLO model from an scvi model. |
|
Retrieves the |
|
Returns the object in AnnData associated with the key in the data registry. |
|
Instantiate a model from the saved output. |
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Return the full registry saved with the model. |
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Return doublet predictions. |
|
Registers an |
|
Save the state of the model. |
|
Sets up the |
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Move model to device. |
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Trains the model. |
|
Print summary of the setup for the initial AnnData or a given AnnData object. |
|
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 SOLO.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.
create_doublets
- classmethod SOLO.create_doublets(adata_manager, doublet_ratio, indices=None, seed=1)[source]#
Simulate doublets.
- Parameters:
adata – AnnData object setup with setup_anndata.
doublet_ratio (int) – Ratio of generated doublets to produce relative to number of cells in adata or length of indices, if not
None
.indices (Optional[Sequence[int]]) – Indices of cells in adata to use. If
None
, all cells are used.seed (int) – Seed for reproducibility
adata_manager (AnnDataManager) –
- Return type:
from_scvi_model
- classmethod SOLO.from_scvi_model(scvi_model, adata=None, restrict_to_batch=None, doublet_ratio=2, **classifier_kwargs)[source]#
Instantiate a SOLO model from an scvi model.
- Parameters:
scvi_model (SCVI) – Pre-trained model of
SCVI
. The adata object used to initialize this model should have only been setup with count data, and optionally abatch_key
; i.e., no extra covariates or labels, etc.adata (Optional[AnnData]) – Optional anndata to use that is compatible with scvi_model.
restrict_to_batch (Optional[str]) – Batch category in
batch_key
used to setup adata for scvi_model to restrict Solo model to. This allows to train a Solo model on one batch of a scvi_model that was trained on multiple batches.doublet_ratio (int) – Ratio of generated doublets to produce relative to number of cells in adata or length of indices, if not
None
.**classifier_kwargs – Keyword args for
Classifier
- Returns:
SOLO model
get_anndata_manager
- SOLO.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_from_registry
- SOLO.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.
load
- classmethod SOLO.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 (Optional[Union[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 the saved
scvi
setup dictionary. 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
>>> model = ModelClass.load(save_path, adata) # use the name of the model class used to save >>> model.get_....
load_registry
- static SOLO.load_registry(dir_path, prefix=None)[source]#
Return the full registry saved with the model.
predict
register_manager
- classmethod SOLO.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
- SOLO.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
write()
setup_anndata
- classmethod SOLO.setup_anndata(adata, 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:
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
- SOLO.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
- SOLO.train(max_epochs=400, lr=0.001, use_gpu=None, train_size=0.9, validation_size=None, batch_size=128, plan_kwargs=None, early_stopping=True, early_stopping_patience=30, early_stopping_min_delta=0.0, **kwargs)[source]#
Trains the model.
- Parameters:
max_epochs (int) – Number of epochs to train for
lr (float) – Learning rate for optimization.
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).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
ClassifierTrainingPlan
. Keyword arguments passed toearly_stopping (bool) – Adds callback for early stopping on validation_loss
early_stopping_patience (int) – Number of times early stopping metric can not improve over early_stopping_min_delta
early_stopping_min_delta (float) – Threshold for counting an epoch torwards patience
train()
will overwrite values present inplan_kwargs
, when appropriate.**kwargs – Other keyword args for
Trainer
.
view_anndata_setup
- SOLO.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