deconvatac.tl.destvi#
Functions#
|
Run DestVI |
Module Contents#
- deconvatac.tl.destvi.destvi(adata_spatial, adata_ref, labels_key=None, layer_spatial=None, layer_ref=None, use_gpu=True, max_epochs_spatial=2000, max_epochs_ref=300, return_adatas=False, plots=True, results_path='./destvi_results', model_ref_kwargs={}, train_ref_kwargs={}, model_spatial_kwargs={}, train_spatial_kwargs={})#
Run DestVI
Parameters#
- adata_spatialAnnData
AnnData of the spatial data, filtered by highly variable features. Feature space needs to be the same as the one of adata_ref.
- adata_refAnnData
AnnData of the reference data, filtered by highly variable features. Feature space needs to be the same as the one of adata_spatial.
- labels_keystr
Cell type key in adata_ref.obs for label information
- layer_spatialstr
Layer of adata_spatial to use for deconvolution. If None, uses adata_spatial.X.
- layer_refstr
Layer of adata_ref to use for deconvolution. If None, uses adata_ref.X.
- use_gpubool
Whether to use the GPU.
- max_epochs_spatial: int
Number of epochs for the stLVM.
- max_epochs_ref: int
Number of epochs for the scLVM.
- return_adatas: bool
Whether to return AnnDatas with deconvolution results. Returns tupel: (adata_spatial, adata_ref).
- plots: bool
Whether to plot ELBO plots and UMAP of scLVM latent space.
- results_path: str
Path to save estimated cell type abundances to.
- model_ref_kwargs: dict
Parameters for scvi.model.CondSCVI()
- train_ref_kwargs: dict
Parameters for scvi.model.CondSCVI.train()
- model_spatial_kwargs: dict
Parameters for scvi.model.DestVI.from_rna_model()
- train_spatial_kwargs: dict
Parameters for scvi.model.DestVI.train()
Returns#
Saves estimated proportions as csv-file to results_path.
If return_adatas=True, returns tupel (adata_spatial, adata_ref) with saved deconvolution results.