pycallingcards.tools.GWAS_adata_sc#

pycallingcards.tools.GWAS_adata_sc(adata, number=100, bindings=['Chr', 'Start', 'End'], clusters=None, cluster_name='cluster')[source]#

Plot GWAS results for different cell types for single-cell calling cards data. It considers the relative number of insertions in each cell type. GWAS data is downloaded from GWAS Catalog.

Parameters:

  • adata (AnnData) – The anndata object of scCC data.

  • number (int (default: 100)) – The minimun total number for each SNP.

  • bindings (list (default: ['Chr', 'Start', 'End'])) – The name for binding information.

  • clusters (Optional[list] (default: None)) – The cluter to consider. If None, it will use all the clusters in adata.obs[cluster_name]

  • cluster_name (str (default: 'cluster')) – The name of cluster in adata.obs.

Return type:

DataFrame

Example:

>>> import pycallingcards as cc
>>> adata_cc = cc.datasets.mousecortex_data(data="CC")
>>> adata_cc = cc.tl.liftover(adata_cc, bindings = ['Chr_liftover', 'Start_liftover', 'End_liftover'])
>>> cc.tl.GWAS_adata_sc(adata_cc)