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)
>>> library_size = adata.X.sum(1)
>>> adata.obs["size_factor"] = library_size / np.mean(library_size)
>>> 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='size_factor')
>>> model.train()
>>> predictions = model.predict(bdata)

Notes

Size factors in the R implementation of CellAssign are computed using scran. An approximate approach computes the sum of UMI counts (library size) over all genes and divides by the mean library size.

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.

scvi.model.MULTIVI.train scvi.external.CellAssign.device