scvi.external.RNAStereoscope#
- class scvi.external.RNAStereoscope(sc_adata, **model_kwargs)[source]#
Reimplementation of Stereoscope [Andersson et al., 2020] for deconvolution of spatial transcriptomics from single-cell transcriptomics.
https://github.com/almaan/stereoscope.
- Parameters:
sc_adata (
AnnData
) – single-cell AnnData object that has been registered viasetup_anndata()
.**model_kwargs – Keyword args for
RNADeconv
Examples
>>> sc_adata = anndata.read_h5ad(path_to_sc_anndata) >>> scvi.external.RNAStereoscope.setup_anndata(sc_adata, labels_key="labels") >>> stereo = scvi.external.stereoscope.RNAStereoscope(sc_adata) >>> stereo.train()
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. |
|
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. |
|
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. |
|
Registers an |
|
Save the state of the model. |
|
Sets up the |
|
Move model to device. |
|
Trains the model using MAP inference. |
|
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 RNAStereoscope.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.
- 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.
- Return type:
get_anndata_manager
- RNAStereoscope.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
- RNAStereoscope.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 RNAStereoscope.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 (
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.use_gpu (
Union
[str
,int
,bool
,None
] (default:None
)) – 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).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_....
load_registry
- static RNAStereoscope.load_registry(dir_path, prefix=None)[source]#
Return the full registry saved with the model.
register_manager
- classmethod RNAStereoscope.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
.
save
- RNAStereoscope.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
] (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 anndataanndata_write_kwargs – Kwargs for
write()
setup_anndata
- classmethod RNAStereoscope.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
] (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 (
Optional
[str
] (default:None
)) – if not None, uses this as the key in adata.layers for raw count data.
to_device
- RNAStereoscope.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
- RNAStereoscope.train(max_epochs=400, lr=0.01, use_gpu=None, train_size=1, validation_size=None, batch_size=128, plan_kwargs=None, **kwargs)[source]#
Trains the model using MAP inference.
- Parameters:
max_epochs (
int
(default:400
)) – Number of epochs to train forlr (
float
(default:0.01
)) – Learning rate for optimization.use_gpu (
Union
[str
,int
,bool
,None
] (default:None
)) – 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
(default:1
)) – 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.batch_size (
int
(default:128
)) – Minibatch size to use during training.plan_kwargs (
Optional
[dict
] (default:None
)) – Keyword args forTrainingPlan
. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.**kwargs – Other keyword args for
Trainer
.
view_anndata_setup
- RNAStereoscope.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