the epigenetic signatures and the genetic signatures separately

by a consistency validation about how the discovered

ally expressed genes and the discovered differentially methylated

correlated in the relationship with a disease. For instance, this

was used to discover aberrant genes in association with breast

hari, et al., 2010]. The cluster analysis algorithms have also been

examine whether the epigenetic signatures and the genetic

s show similar abundance pattern in relation with diseases

et al., 2012].

econd type of approach models the epigenetic-genetic interplay

For instance, the correlation analysis was used to examine how

copy number and the methylation ratio interact with the gene

n based on the deep sequencing profiling transcript expression

a breast cancer study [Sun, et al., 2011]. When studying the low

otility, a mixture model has been constructed based on the pooled

on data and the gene expression data [Pacheco, et al., 2011]. The

on between the methylation and sperm motility was examined

gression models, where the positive regression coefficient was

o a hypo-methylated site and the negative one a hyper-methylated

ths, 2004]. In one study, 62 primary fibroblast samples were used

e how the epigenetic signatures influence the genetic signatures

species, where the gene expressions were regressed on the

ring methylation sites or SNP [Wagner, et al., 2014]. Another

o employed only local and few epigenetic signatures to examine

tributions to the regulation of genes [Ping, et al., 2015].

ession analysis

hod of regression analysis has been researched since the 19^th

Galton, 1986] and has been continuously researched till today

et al., 2000; Bishop, 2006; Chatterjee and Hadi, 2015;

mery, et al., 2021]. Regression analysis can be the best candidate

e how two sets of experiments interact and correlate. The main

of regression analysis is to establish a quantitative model to