scanpy.queries.enrich(container, *, org='hsapiens', gprofiler_kwargs=mappingproxy({}))
scanpy.queries.enrich(adata, group, *, org='hsapiens', key='rank_genes_groups', pval_cutoff=0.05, log2fc_min=None, log2fc_max=None, gene_symbols=None, gprofiler_kwargs=mappingproxy({}))

Get enrichment for DE results.

This is a thin convenience wrapper around the very useful gprofiler.

This method dispatches on the first argument, leading to the following two signatures:

enrich(container, ...)
enrich(adata: AnnData, group, key: str, ...)


enrich(adata, group, key, ...) = enrich(adata.uns[key]["names"][group], ...)
container : Iterable[str], MappingUnion[Iterable[str], Mapping[str, Iterable[str]]]

Contains list of genes you’d like to search. If container is a dict all enrichment queries are made at once.


AnnData object whose group will be looked for.


The group whose genes should be used for enrichment.


Key in uns to find group under.

org : strstr (default: 'hsapiens')

Organism to query. Must be an organism in ensembl biomart. “hsapiens”, “mmusculus”, “drerio”, etc.

gprofiler_kwargs : MappingMapping[str, Any] (default: mappingproxy({}))

Keyword arguments to pass to GProfiler.profile, see gprofiler. Some useful options are no_evidences=False which reports gene intersections, sources=['GO:BP'] which limits gene sets to only GO biological processes and all_results=True which returns all results including the non-significant ones.


All other keyword arguments are passed to sc.get.rank_genes_groups_df. E.g. pval_cutoff, log2fc_min.

Return type



Dataframe of enrichment results.


Using sc.queries.enrich on a list of genes:

>>> import scanpy as sc
>>> sc.queries.enrich(['KLF4', 'PAX5', 'SOX2', 'NANOG'], org="hsapiens")
>>> sc.queries.enrich({'set1':['KLF4', 'PAX5'], 'set2':['SOX2', 'NANOG']}, org="hsapiens")

Using sc.queries.enrich on an anndata.AnnData object:

>>> pbmcs = sc.datasets.pbmc68k_reduced()
>>>, "bulk_labels")
>>> sc.queries.enrich(pbmcs, "CD34+")