scvi.external.CellAssign#
- class scvi.external.CellAssign(adata, cell_type_markers, **model_kwargs)[source]#
Reimplementation of CellAssign for reference-based annotation [Zhang et al., 2019].
Original implementation: irrationone/cellassign.
- Parameters
adata (
AnnData
) – single-cell AnnData object that has been registered viasetup_anndata()
. The object should be subset to contain the same genes as the cell type marker dataframe.cell_type_markers (
DataFrame
) – Binary marker gene DataFrame of genes by cell types. Gene names corresponding to adata.var_names should be in DataFrame index, and cell type labels should be the columns.**model_kwargs – Keyword args for
CellAssignModule
Examples
>>> adata = scvi.data.read_h5ad(path_to_anndata) >>> library_size = adata.X.sum(1) >>> adata.obs["size_factor"] = library_size / np.mean(library_size) >>> marker_gene_mat = pd.read_csv(path_to_marker_gene_csv) >>> bdata = adata[:, adata.var.index.isin(marker_gene_mat.index)].copy() >>> CellAssign.setup_anndata(bdata, size_factor_key="size_factor") >>> model = CellAssign(bdata, marker_gene_mat) >>> model.train() >>> predictions = model.predict(bdata)
Notes
Size factors in the R implementation of CellAssign are computed using scran. An approximate approach computes the sum of UMI counts (library size) over all genes and divides by the mean library size.
See further usage examples in the following tutorial:
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. |
|
Deregisters the |
|
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. |
|
Predict soft cell type assignment probability for each cell. |
|
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 CellAssign.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 (
Optional
[str
] (default:None
)) – Prefix of saved file names.**save_kwargs – Keyword arguments passed into
save()
.
- Return type
- CellAssign.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.
- CellAssign.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.
- CellAssign.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 CellAssign.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 (
Union
[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 (
Optional
[str
] (default:None
)) – Prefix of saved file names.backup_url (
Optional
[str
] (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) # use the name of the model class used to save >>> model.get_....
- static CellAssign.load_registry(dir_path, prefix=None)[source]#
Return the full registry saved with the model.
- CellAssign.predict()[source]#
Predict soft cell type assignment probability for each cell.
- Return type
- classmethod CellAssign.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
.
- CellAssign.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 (
Optional
[str
] (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 (
Optional
[dict
] (default:None
)) – Keyword arguments passed intosave()
.anndata_write_kwargs – Kwargs for
write()
- classmethod CellAssign.setup_anndata(adata, size_factor_key, batch_key=None, categorical_covariate_keys=None, continuous_covariate_keys=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
adata (
AnnData
) – AnnData object. Rows represent cells, columns represent features.size_factor_key (
str
) – key in adata.obs with continuous valued size factors.batch_key (
Optional
[str
] (default:None
)) – key in adata.obs for batch information. Categories will automatically be converted into integer categories and saved to adata.obs[‘_scvi_batch’]. If None, assigns the same batch to all the data.layer (
Optional
[str
] (default:None
)) – if not None, uses this as the key in adata.layers for raw count data.categorical_covariate_keys (
Optional
[list
[str
]] (default:None
)) – keys in adata.obs that correspond to categorical data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.continuous_covariate_keys (
Optional
[list
[str
]] (default:None
)) – keys in adata.obs that correspond to continuous data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.
- CellAssign.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
- CellAssign.train(max_epochs=400, lr=0.003, accelerator='auto', devices='auto', train_size=0.9, validation_size=None, shuffle_set_split=True, batch_size=1024, datasplitter_kwargs=None, plan_kwargs=None, early_stopping=True, early_stopping_patience=15, 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:0.003
)) – 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 (
Optional
[float
] (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:1024
)) – Minibatch size to use during training.datasplitter_kwargs (
Optional
[dict
] (default:None
)) – Additional keyword arguments passed intoDataSplitter
.plan_kwargs (
Optional
[dict
] (default:None
)) – Keyword args forTrainingPlan
.early_stopping (
bool
(default:True
)) – Adds callback for early stopping on validation_lossearly_stopping_patience (
int
(default:15
)) – 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
.