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 : AnnDataAnnData: spatial transcriptomics AnnData object that has been registered via setup_anndata().
sc_params : Tuple[ndarray]Tuple[ndarray]: parameters of the model learned from the single-cell RNA seq data for deconvolution.
cell_type_mapping : ndarrayndarray: 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

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

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

Methods

`from_rna_model`(st_adata, sc_model[, ...])	Alternate constructor for exploiting a pre-trained model on RNA-seq data.
`get_proportions`([keep_noise])	Returns the estimated cell type proportion for the spatial data.
`get_scale_for_ct`(y)	Calculate the cell type specific expression.
`load`(dir_path[, adata, use_gpu])	Instantiate a model from the saved output.
`save`(dir_path[, overwrite, save_anndata])	Save the state of the model.
`setup_anndata`(adata[, layer, copy])	Sets up the `AnnData` object for this model.
`to_device`(device)	Move model to device.
`train`([max_epochs, lr, use_gpu, batch_size, ...])	Trains the model using MAP inference.

scvi.external.RNAStereoscope.train

scvi.external.SpatialStereoscope.device