pycallingcards.tools.rank_peak_groups_df#

pycallingcards.tools.rank_peak_groups_df(adata, key='rank_peak_groups', group=None, pval_cutoff=None, pval_adj_cutoff=None, logfc_min=None, logfc_max=None)[source]#

pycallingcards.tl.rank_peak_groups() results in the form of a DataFrame.

Parameters:

  • adata (AnnData) – Object to get results from.

  • key (str (default: 'rank_peak_groups')) – Key differential expression groups were stored under.

  • group (Optional[list] (default: None)) – 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.

  • pval_cutoff (Optional[float] (default: None)) – Return only p-values below the cutoff.

  • pval_adj_cutoff (Optional[float] (default: None)) – Return only adjusted p-values below the cutoff.

  • logfc_min (Optional[float] (default: None)) – Minimum logfc to return.

  • logfc_max (Optional[float] (default: None)) – Maximum logfc to return.

Return type:

DataFrame

Example

>>> import pycallingcards as cc
>>> adata_cc = cc.datasets.mousecortex_data(data="CC")
>>> cc.tl.rank_peak_groups(adata_cc,'cluster',method = 'binomtest',key_added = 'binomtest')
>>> cc.tl.rank_peak_groups_df(adata_cc,'Astrocyte','binomtest')