scvi.external.CellAssign

class scvi.external.CellAssign(adata, cell_type_markers, size_factor_key, **model_kwargs)[source]

Reimplementation of CellAssign for reference-based annotation [Zhang19].

Parameters
adata : AnnDataAnnData

single-cell AnnData object that has been registered via setup_anndata(). The object should be subset to contain the same genes as the cell type marker dataframe.

cell_type_markers : DataFrameDataFrame

Binary marker gene DataFrame of genes by cell types. Gene names corresponding to adata.var_names should be in DataFrame index, and cell type labels should be the columns.

size_factor_key : strstr

Key in adata.obs with continuous valued size factors.

**model_kwargs

Keyword args for CellAssignModule

Examples

>>> adata = scvi.data.read_h5ad(path_to_anndata)
>>> marker_gene_mat = pd.read_csv(path_to_marker_gene_csv)
>>> bdata = adata[:, adata.var.index.isin(marker_gene_mat.index)].copy()
>>> scvi.data.setup_anndata(bdata)
>>> model = CellAssign(bdata, marker_gene_mat, size_factor_key='S')
>>> model.train()
>>> predictions = model.predict(bdata)

Attributes

device

history

Returns computed metrics during training.

is_trained

test_indices

train_indices

validation_indices

Methods

load(dir_path[, adata, use_gpu])

Instantiate a model from the saved output.

predict()

Predict soft cell type assignment probability for each cell.

save(dir_path[, overwrite, save_anndata])

Save the state of the model.

to_device(device)

Move model to device.

train([max_epochs, lr, use_gpu, train_size, …])

Trains the model.