scvi.external.SOLO#
- class scvi.external.SOLO(adata, **classifier_kwargs)[source]#
Doublet detection in scRNA-seq [Bernstein et al., 2020].
Original implementation: calico/solo.
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 viasetup_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 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. |
|
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. |
|
Simulate doublets. |
|
Deregisters the |
|
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. |
|
Return the full registry saved with the model. |
|
Return doublet predictions. |
|
Registers an |
|
Save the state of the model. |
|
Sets up the |
|
Move model to device. |
|
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#
Methods#
- classmethod SOLO.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:
- 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 (
Sequence
[int
] |None
(default:None
)) – Indices of cells in adata to use. If None, all cells are used.seed (
int
(default:1
)) – Seed for reproducibility
- Return type:
- SOLO.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.
- 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-trainedSCVI
model. The AnnData object used to initialize this model should have only been setup with count data, and optionally a batch_key. Extra categorical and continuous covariates are currenty unsupported.adata (
AnnData
|None
(default:None
)) – Optional AnnData to use that is compatible with scvi_model.restrict_to_batch (
str
|None
(default:None
)) – Batch category to restrict the SOLO model to if scvi_model was set up with a batch_key. This allows the model to be trained on the subset of cells belonging to restrict_to_batch when scvi_model was trained on multiple batches. If None, all cells are used.doublet_ratio (
int
(default:2
)) – 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
- SOLO.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:
- 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.
- classmethod SOLO.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_....
- static SOLO.load_registry(dir_path, prefix=None)[source]#
Return the full registry saved with the model.
- SOLO.predict(soft=True, include_simulated_doublets=False, return_logits=False)[source]#
Return doublet predictions.
- Parameters:
- Return type:
- Returns:
DataFrame with prediction, index corresponding to cell barcode.
- 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
.
- SOLO.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 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 (
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.layer (
str
|None
(default:None
)) – if not None, uses this as the key in adata.layers for raw count data.
- SOLO.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
- SOLO.train(max_epochs=400, lr=0.001, accelerator='auto', devices='auto', train_size=0.9, validation_size=None, shuffle_set_split=True, batch_size=128, datasplitter_kwargs=None, 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
(default:400
)) – Number of epochs to train forlr (
float
(default:0.001
)) – 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.datasplitter_kwargs (
dict
|None
(default:None
)) – Additional keyword arguments passed intoDataSplitter
.plan_kwargs (
dict
|None
(default:None
)) – Keyword args forClassifierTrainingPlan
.early_stopping (
bool
(default:True
)) – Adds callback for early stopping on validation_lossearly_stopping_patience (
int
(default:30
)) – Number of times early stopping metric can not improve over early_stopping_min_deltaearly_stopping_min_delta (
float
(default:0.0
)) – Threshold for counting an epoch torwards patience train() will overwrite values present in plan_kwargs, when appropriate.**kwargs – Other keyword args for
Trainer
.