scanpy.tl.ingest

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

Map labels and embeddings from reference data to new data.

→ tutorial: integrating-data-using-ingest

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 [McInnes18] 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 : Union[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 : Union[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 : Optional[str] (default: None): If not specified, ingest looks adata_ref.uns[‘neighbors’] for neighbors settings and adata_ref.obsp[‘distances’] for distances (default storage places for pp.neighbors). If specified, ingest looks 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:

: * if inplace=False returns a copy of adata

with mapped embeddings and labels in obsm and obs correspondingly

if inplace=True returns None and updates adata.obsm and adata.obs with mapped embeddings and labels

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')