Gene expression data analysis using Hellinger correlation in weighted gene co-expression networks (WGCNA).

ABSTRACT: Weighted gene co-expression network analysis (WGCNA) is used to detect clusters with highly correlated genes. Measurements of correlation most typically rely on linear relationships. However, a linear relationship does not always model pairwise functional-related dependence between genes. In this paper, we first compared 6 different correlation methods in their ability to capture complex dependence between genes in three different tissues. Next, we compared their gene-pairwise coefficient results and corresponding WGCNA results. Finally, we applied a recently proposed correlation method, Hellinger correlation, as a more sensitive correlation measurement in WGCNA. To test this method, we constructed gene networks containing co-expression gene modules from RNA-seq data of human frontal corte

SUBMITTER: Zhang T 

PROVIDER: S-EPMC9307959 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature