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Gene expression data analysis using Hellinger correlation in weighted gene co-expression networks (WGCNA)
Zhang, Tianjiao; Wong, Garry
2022-07
Source PublicationComputational Structural Biotechnology
ISSN2001-0370
Volume20Pages:3851-3863
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 rela-
tionship 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
cortex from Alzheimer’s disease patients. To test the generality, we also used a microarray data set from
human frontal cortex, single cell RNA-seq data from human prefrontal cortex, RNA-seq data from human
temporal cortex, and GTEx data from heart. The Hellinger correlation method captures essentially similar
results as other linear correlations in WGCNA, but provides additional new functional relationships as
exemplified by uncovering a link between inflammation and mitochondria function. We validated the
network constructed with the microarray and single cell sequencing data sets and a RNA-seq dataset
of temporal cortex. We observed that this new correlation method enables the detection of non-linear
biologically meaningful relationships among genes robustly and provides a complementary new
approach to WGCNA. Thus, the application of Hellinger correlation to WGCNA provides a more flexible
correlation approach to modelling networks in gene expression analysis that uncovers novel network
relationships.

Language英語English
Funding ProjectRole of piRNAs in Alzheimer’s and Parkinson’s Disease
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorZhang, Tianjiao; Wong, Garry
AffiliationUniversity of Macau
Recommended Citation
GB/T 7714
Zhang, Tianjiao,Wong, Garry. Gene expression data analysis using Hellinger correlation in weighted gene co-expression networks (WGCNA)[J]. Computational Structural Biotechnology,2022,20:3851-3863.
APA Zhang, Tianjiao,&Wong, Garry.(2022).Gene expression data analysis using Hellinger correlation in weighted gene co-expression networks (WGCNA).Computational Structural Biotechnology,20,3851-3863.
MLA Zhang, Tianjiao,et al."Gene expression data analysis using Hellinger correlation in weighted gene co-expression networks (WGCNA)".Computational Structural Biotechnology 20(2022):3851-3863.
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