scvi.external.SpatialStereoscope#
- class scvi.external.SpatialStereoscope(st_adata, sc_params, cell_type_mapping, prior_weight='n_obs', **model_kwargs)[source]#
Reimplementation of Stereoscope [Andersson20] for deconvolution of spatial transcriptomics from single-cell transcriptomics.
https://github.com/almaan/stereoscope.
- Parameters:
- st_adata :
AnnData
spatial transcriptomics AnnData object that has been registered via
setup_anndata()
.- sc_params :
Tuple
[ndarray
] parameters of the model learned from the single-cell RNA seq data for deconvolution.
- cell_type_mapping :
ndarray
numpy array mapping for the cell types used in the deconvolution
- prior_weight : {‘n_obs’, ‘minibatch’}
Literal
[‘n_obs’, ‘minibatch’] (default:'n_obs'
) how to reweight the minibatches for stochastic optimization. “n_obs” is the valid procedure, “minibatch” is the procedure implemented in Stereoscope.
- **model_kwargs
Keyword args for
SpatialDeconv
- st_adata :
Examples
>>> sc_adata = anndata.read_h5ad(path_to_sc_anndata) >>> scvi.external.RNAStereoscope.setup_anndata(sc_adata, labels_key="labels") >>> sc_model = scvi.external.stereoscope.RNAStereoscope(sc_adata) >>> sc_model.train() >>> st_adata = anndata.read_h5ad(path_to_st_anndata) >>> scvi.external.SpatialStereoscope.setup_anndata(st_adata) >>> stereo = scvi.external.stereoscope.SpatialStereoscope.from_rna_model(st_adata, sc_model) >>> stereo.train() >>> st_adata.obsm["deconv"] = stereo.get_proportions()
Notes
See further usage examples in the following tutorials:
/user_guide/notebooks/stereoscope_heart_LV_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. |
|
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. |
|
Alternate constructor for exploiting a pre-trained model on RNA-seq data. |
|
Retrieves the |
|
Returns the object in AnnData associated with the key in the data registry. |
|
Returns the estimated cell type proportion for the spatial data. |
Calculate the cell type specific expression. |
|
|
Instantiate a model from the saved output. |
|
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#
- SpatialStereoscope.adata_manager#
Manager instance associated with self.adata.
- Return type:
device#
history#
- SpatialStereoscope.history#
Returns computed metrics during training.
is_trained#
test_indices#
train_indices#
validation_indices#
Methods#
convert_legacy_save#
- classmethod SpatialStereoscope.convert_legacy_save(dir_path, output_dir_path, overwrite=False, prefix=None)#
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. If
False
and directory already exists atoutput_dir_path
, error will be raised.- prefix :
str
|None
Optional
[str
] (default:None
) Prefix of saved file names.
- dir_path :
- Return type:
from_rna_model#
- classmethod SpatialStereoscope.from_rna_model(st_adata, sc_model, prior_weight='n_obs', layer=None, **model_kwargs)[source]#
Alternate constructor for exploiting a pre-trained model on RNA-seq data.
- Parameters:
- st_adata :
AnnData
registed anndata object
- sc_model :
RNAStereoscope
trained RNADeconv model
- prior_weight : {‘n_obs’, ‘minibatch’}
Literal
[‘n_obs’, ‘minibatch’] (default:'n_obs'
) how to reweight the minibatches for stochastic optimization. “n_obs” is the valid procedure, “minibatch” is the procedure implemented in Stereoscope.
- layer :
str
|None
Optional
[str
] (default:None
) if not None, uses this as the key in adata.layers for raw count data.
- **model_kwargs
Keyword args for
SpatialDeconv
- st_adata :
get_anndata_manager#
- SpatialStereoscope.get_anndata_manager(adata, required=False)#
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.- Parameters:
- Return type:
get_from_registry#
- SpatialStereoscope.get_from_registry(adata, registry_key)#
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.
get_proportions#
- SpatialStereoscope.get_proportions(keep_noise=False)[source]#
Returns the estimated cell type proportion for the spatial data.
Shape is n_cells x n_labels OR n_cells x (n_labels + 1) if keep_noise
- Parameters:
- keep_noise
whether to account for the noise term as a standalone cell type in the proportion estimate.
- Return type:
get_scale_for_ct#
load#
- classmethod SpatialStereoscope.load(dir_path, adata=None, use_gpu=None, prefix=None, backup_url=None)#
Instantiate a model from the saved output.
- Parameters:
- dir_path :
str
Path to saved outputs.
- adata :
AnnData
|None
Optional
[AnnData
] (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 :
str
|int
|bool
|None
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 :
str
|None
Optional
[str
] (default:None
) Prefix of saved file names.
- backup_url :
str
|None
Optional
[str
] (default:None
) URL to retrieve saved outputs from if not present on disk.
- dir_path :
- 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_....
register_manager#
- classmethod SpatialStereoscope.register_manager(adata_manager)#
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#
- SpatialStereoscope.save(dir_path, prefix=None, overwrite=False, save_anndata=False, **anndata_write_kwargs)#
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
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 anndata
- anndata_write_kwargs
Kwargs for
write()
- dir_path :
setup_anndata#
to_device#
- SpatialStereoscope.to_device(device)#
Move model to device.
- Parameters:
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#
- SpatialStereoscope.train(max_epochs=400, lr=0.01, use_gpu=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 for
- lr :
float
(default:0.01
) Learning rate for optimization.
- use_gpu :
str
|int
|bool
|None
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).
- batch_size :
int
(default:128
) Minibatch size to use during training.
- plan_kwargs :
dict
|None
Optional
[dict
] (default:None
) Keyword args for
TrainingPlan
. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.- **kwargs
Other keyword args for
Trainer
.
- max_epochs :
view_anndata_setup#
- SpatialStereoscope.view_anndata_setup(adata=None, hide_state_registries=False)#
Print summary of the setup for the initial AnnData or a given AnnData object.