scanpy.get.rank_genes_groups_df

scanpy.get.rank_genes_groups_df(adata, group, *, key='rank_genes_groups', pval_cutoff=None, log2fc_min=None, log2fc_max=None, gene_symbols=None)

scanpy.tl.rank_genes_groups() results in the form of a DataFrame.

Parameters:

adata : AnnData: Object to get results from.
group : Union[str, Iterable[str]]: Which group (as in scanpy.tl.rank_genes_groups()’s groupby argument) to return results from. Can be a list. All groups are returned if groups is None.
key : str (default: 'rank_genes_groups'): Key differential expression groups were stored under.
pval_cutoff : Optional[float] (default: None): Return only adjusted p-values below the cutoff.
log2fc_min : Optional[float] (default: None): Minimum logfc to return.
log2fc_max : Optional[float] (default: None): Maximum logfc to return.
gene_symbols : Optional[str] (default: None): Column name in .var DataFrame that stores gene symbols. Specifying this will add that column to the returned dataframe.

Return type:

DataFrame

Example

>>> import scanpy as sc
>>> pbmc = sc.datasets.pbmc68k_reduced()
>>> sc.tl.rank_genes_groups(pbmc, groupby="louvain", use_raw=True)
>>> dedf = sc.get.rank_genes_groups_df(pbmc, group="0")