scanpy.tl.ingest

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scanpy.tl.ingest#

scanpy.tl.ingest(adata, adata_ref, *, obs=None, embedding_method=('umap', 'pca'), labeling_method='knn', neighbors_key=None, inplace=True, **kwargs)[source]#

Map labels and embeddings from reference data to new data.

Integrating data using ingest and BBKNN

Integrates embeddings and annotations of an adata with a reference dataset adata_ref through projecting on a PCA (or alternate model) that has been fitted on the reference data. The function uses a knn classifier for mapping labels and the UMAP package [McInnes et al., 2018] for mapping the embeddings.

Note

We refer to this asymmetric dataset integration as ingesting annotations from reference data to new data. This is different from learning a joint representation that integrates both datasets in an unbiased way, as CCA (e.g. in Seurat) or a conditional VAE (e.g. in scVI) would do.

You need to run neighbors() on adata_ref before passing it.

Parameters:
adata AnnData

The annotated data matrix of shape n_obs × n_vars. Rows correspond to cells and columns to genes. This is the dataset without labels and embeddings.

adata_ref AnnData

The annotated data matrix of shape n_obs × n_vars. Rows correspond to cells and columns to genes. Variables (n_vars and var_names) of adata_ref should be the same as in adata. This is the dataset with labels and embeddings which need to be mapped to adata.

obs str | Iterable[str] | None (default: None)

Labels’ keys in adata_ref.obs which need to be mapped to adata.obs (inferred for observation of adata).

embedding_method str | Iterable[str] (default: ('umap', 'pca'))

Embeddings in adata_ref which need to be mapped to adata. The only supported values are ‘umap’ and ‘pca’.

labeling_method str (default: 'knn')

The method to map labels in adata_ref.obs to adata.obs. The only supported value is ‘knn’.

neighbors_key str | None (default: None)

If not specified, ingest looks at adata_ref.uns[‘neighbors’] for neighbors settings and adata_ref.obsp[‘distances’] for distances (default storage places for pp.neighbors). If specified, ingest looks at adata_ref.uns[neighbors_key] for neighbors settings and adata_ref.obsp[adata_ref.uns[neighbors_key][‘distances_key’]] for distances.

inplace bool (default: True)

Only works if return_joint=False. Add labels and embeddings to the passed adata (if True) or return a copy of adata with mapped embeddings and labels.

Returns:

Returns None if copy=False, else returns an AnnData object. Sets the following fields:

adata.obs[obs]pandas.Series (dtype category)

Mapped labels.

adata.obsm['X_umap' | 'X_pca']numpy.ndarray (dtype float)

Mapped embeddings. 'X_umap' if embedding_method is 'umap', 'X_pca' if embedding_method is 'pca'.

Example

Call sequence:

>>> import scanpy as sc
>>> sc.pp.neighbors(adata_ref)
>>> sc.tl.umap(adata_ref)
>>> sc.tl.ingest(adata, adata_ref, obs="cell_type")
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