title: Co-expression network-based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm.
abstract: Recent progress in bioinformatics has facilitated the clarification of biological processes associated with complex diseases. Numerous methods of co-expression analysis have been proposed for use in the study of pairwise relationships among genes. In the present study, a combined network based on gene pairs was constructed following the conversion and combination of gene pair score values using a novel algorithm across multiple approaches. Three hippocampal expression profiles of patients with Alzheimer's disease (AD) and normal controls were extracted from the ArrayExpress database, and a total of 144 differentially expressed (DE) genes across multiple studies were identified by a rank product (RP) method. Five groups of co-expression gene pairs and five networks were identified and constructed using four existing methods [weighted gene co-expression network analysis (WGCNA), empirical Bayesian (EB), differentially co-expressed genes and links (DCGL), search tool for the retrieval of interacting genes/proteins database (STRING)] and a novel rank-based algorithm with combined score, respectively. Topological analysis indicated that the co-expression network constructed by the WGCNA method had the tendency to exhibit small-world characteristics, and the combined co-expression network was confirmed to be a scale-free network. Functional analysis of the co-expression gene pairs was conducted by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The co-expression gene pairs were mostly enriched in five pathways, namely proteasome, oxidative phosphorylation, Parkinson's disease, Huntington's disease and AD. This study provides a new perspective to co-expression analysis. Since different methods of analysis often present varying abilities, the novel combination algorithm may provide a more credible and robust outcome, and could be used to complement to traditional co-expression analysis.
authors.affiliation: Department of Neurology (No. 2), Rizhao People's Hospital, Rizhao, Shandong 276826, P.R. China.
authors.forename: Hong
authors.initials: H
authors.lastname: Yue
authors.affiliation: Department of Neurology (No. 2), Rizhao People's Hospital, Rizhao, Shandong 276826, P.R. China.
authors.forename: B O
authors.initials: BO
authors.lastname: Yang
authors.affiliation: Department of Neurology (No. 2), Rizhao People's Hospital, Rizhao, Shandong 276826, P.R. China.
authors.forename: Fang
authors.initials: F
authors.lastname: Yang
authors.affiliation: Department of Neurology (No. 2), Rizhao People's Hospital, Rizhao, Shandong 276826, P.R. China.
authors.forename: Xiao-Li
authors.initials: XL
authors.lastname: Hu
authors.affiliation: Department of Neurology (No. 2), Rizhao People's Hospital, Rizhao, Shandong 276826, P.R. China.
authors.forename: Fan-Bin
authors.initials: FB
authors.lastname: Kong
keywords: Alzheimer's disease; differentially co-expressed genes and links; empirical Bayesian; gene co-expression analysis; search tool for the retrieval of interacting genes/proteins database; weighted gene co-expression network analysis