pycallingcards.tools.result_mapping#

pycallingcards.tools.result_mapping(data, torlerance=10, original_genome='mm10', new_genome='hg38')[source]#

Map from one genome to another for the result table [Hinrichs et al., 2006].

Parameters:

  • data (DataFrame) – pd.DataFrame of result. It contains ‘Peak’ and ‘Gene’.

  • torlerance (int (default: 10)) – The max multiples allows for the length of new_genome to be compared with to the original one.

  • original_genome (str (default: 'mm10')) – The original genome.

  • new_genome (str (default: 'hg38')) – The new genome.

Return type:

DataFrame

Example:

>>> import pycallingcards as cc
>>> import scanpy as sc
>>> adata_cc = sc.read("Mouse-Cortex_cc.h5ad")
>>> adata = cc.datasets.mousecortex_data(data="RNA")
>>> qbed_data = cc.datasets.mousecortex_data(data="qbed")
>>> peak_data = cc.pp.callpeaks(qbed_data, method = "CCcaller", reference = "mm10",  maxbetween = 2000, pvalue_cutoff = 0.01,
            lam_win_size = 1000000,  pseudocounts = 1, record = True)
>>> peak_annotation = cc.pp.annotation(peak_data, reference = "mm10")
>>> peak_annotation = cc.pp.combine_annotation(peak_data,peak_annotation)
>>> sc.tl.rank_genes_groups(adata,'cluster')
>>> result = cc.tl.pair_peak_gene_sc(adata_cc,adata,peak_annotation)
>>> result = cc.tl.result_mapping(result)