deconvatac.tl.cell2location

Functions

cell2location(adata_spatial, adata_ref, ...[, ...])

Run Cell2Location

Module Contents

deconvatac.tl.cell2location.cell2location(adata_spatial, adata_ref, N_cells_per_location, detection_alpha, labels_key=None, layer_spatial=None, layer_ref=None, use_gpu=True, max_epochs_spatial=30000, max_epochs_ref=None, return_adatas=False, plots=True, results_path='./cell2location_results', setup_ref_kwargs={}, train_ref_kwargs={}, setup_spatial_kwargs={}, train_spatial_kwargs={})

Run Cell2Location

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.

N_cells_per_locationfloat

Expected cell number per location.

detection_alphafloat

Regularisation of per-location normalisation.

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 spatial mapping model. If None, defaults to np.min([round((20000 / n_cells) * 400), 400]).

max_epochs_ref: int

Number of epochs for the reference model. If None, defaults to np.min([round((20000 / n_cells) * 400), 400]).

return_adatas: bool

Whether to return AnnDatas with deconvolution results. Returns tupel: (adata_spatial, adata_ref).

plots: bool

Whether to plot QC and ELBO plots.

results_path: str

Path to save estimated cell type abundances to.

setup_ref_kwargs: dict

Parameters for cell2location.models.RegressionModel.setup_anndata()

train_ref_kwargs: dict

Parameters for cell2location.models.RegressionModel.train()

setup_spatial_kwargs: dict

Parameters for cell2location.models.Cell2location.setup_anndata()

train_spatial_kwargs: dict

Parameters for cell2location.models.Cell2location.train()

Returns

• Saves ‘q05_cell_abundance_w_sf’ and ‘means_cell_abundance_w_sf’ as csv-files to results_path.

• If return_adatas=True, returns tupel (adata_spatial, adata_ref) with saved deconvolution results.