scvi.external.SOLO¶

class scvi.external.SOLO(adata, **classifier_kwargs)[source]¶

Doublet detection in scRNA-seq [Bernstein19].

Most users will initialize the model using the class method from_scvi_model(), which takes as input a pre-trained SCVI object.

Parameters

adata : AnnDataAnnData: AnnData object that has been registered via setup_anndata(). Object should contain latent representation of real cells and doublets as adata.X. Object should also be registered, using .X and labels_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 new restrict_to_batch in from_scvi_model().

Attributes

`device`
`history`	Returns computed metrics during training.
`is_trained`
`test_indices`
`train_indices`
`validation_indices`

Methods

`create_doublets`(adata, doublet_ratio[, ...])	Simulate doublets.
`from_scvi_model`(scvi_model[, adata, ...])	Instantiate a SOLO model from an scvi model.
`load`(dir_path[, adata, use_gpu])	Instantiate a model from the saved output.
`predict`([soft, include_simulated_doublets])	Return doublet predictions.
`save`(dir_path[, overwrite, save_anndata])	Save the state of the model.
`setup_anndata`(adata, labels_key[, layer, copy])	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.

scvi.external.SpatialStereoscope.train

scvi.external.SOLO.device