ArticleComputational and structural biotechnology journal2022
Gene expression data analysis using Hellinger correlation in weighted gene co-expression networks (WGCNA).
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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40 citing papers in PubMed.
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- PacBio full-length transcriptome and WGCNA reveal varietal co-expression networks and regulatory dynamics during tea plant (Frontiers in plant science · 2026Article
- RNA stability: a novel perspective on gene regulatory networks in bovine mastitis.BMC genomics · 2025Article
- Library size-stabilized metacells construction enhances co-expression network analysis in single-cell data.PLoS computational biology · 2025Article
- Screening and experimental study of potential biomarkers for ulcerative colitis based on weighted gene co-expression network analysis and machine learning.Journal of translational medicine · 2025Article
- Comprehensive review of multiomics applications and remediation of plant heavy metal toxicity.Stress biology · 2025Review
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- Unlocking TB-COPD Pathogenesis: A Biotechnological Approach Targeting the PI3K/Akt Pathway.Iranian journal of biotechnology · 2025Article
- Identification and verification of immune and oxidative stress-related diagnostic indicators for malignant lung nodules through WGCNA and machine learning.Scientific reports · 2025Article
- Characteristic genes and immune infiltration analysis of gastric cancer based on bioinformatics analysis and machine learning.Discover oncology · 2025Article
- Harnessing neo-domestication of wild pigmented rice for enhanced nutrition and sustainable agriculture.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Revealing VCAN as a Potential Common Diagnostic Biomarker of Renal Tubules and Glomerulus in Diabetic Kidney Disease Based on Machine Learning, Single-Cell Transcriptome Analysis and Mendelian Randomization.Diabetes & metabolism journal · 2025Article
- Machine Learning Identification of Neutrophil Extracellular Trap-Related Genes as Potential Biomarkers and Therapeutic Targets for Bronchopulmonary Dysplasia.International journal of molecular sciences · 2025Article
- STAIG: Spatial transcriptomics analysis via image-aided graph contrastive learning for domain exploration and alignment-free integration.Nature communications · 2025Article
- Hypergraph-based analysis of weighted gene co-expression hypernetwork.Frontiers in genetics · 2025Article
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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 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.
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