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When Is Hub Gene Selection Better than Standard Meta-Analysis?

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  • Peter Langfelder
  • Paul S Mischel
  • Steve Horvath

Abstract

Since hub nodes have been found to play important roles in many networks, highly connected hub genes are expected to play an important role in biology as well. However, the empirical evidence remains ambiguous. An open question is whether (or when) hub gene selection leads to more meaningful gene lists than a standard statistical analysis based on significance testing when analyzing genomic data sets (e.g., gene expression or DNA methylation data). Here we address this question for the special case when multiple genomic data sets are available. This is of great practical importance since for many research questions multiple data sets are publicly available. In this case, the data analyst can decide between a standard statistical approach (e.g., based on meta-analysis) and a co-expression network analysis approach that selects intramodular hubs in consensus modules. We assess the performance of these two types of approaches according to two criteria. The first criterion evaluates the biological insights gained and is relevant in basic research. The second criterion evaluates the validation success (reproducibility) in independent data sets and often applies in clinical diagnostic or prognostic applications. We compare meta-analysis with consensus network analysis based on weighted correlation network analysis (WGCNA) in three comprehensive and unbiased empirical studies: (1) Finding genes predictive of lung cancer survival, (2) finding methylation markers related to age, and (3) finding mouse genes related to total cholesterol. The results demonstrate that intramodular hub gene status with respect to consensus modules is more useful than a meta-analysis p-value when identifying biologically meaningful gene lists (reflecting criterion 1). However, standard meta-analysis methods perform as good as (if not better than) a consensus network approach in terms of validation success (criterion 2). The article also reports a comparison of meta-analysis techniques applied to gene expression data and presents novel R functions for carrying out consensus network analysis, network based screening, and meta analysis.

Suggested Citation

  • Peter Langfelder & Paul S Mischel & Steve Horvath, 2013. "When Is Hub Gene Selection Better than Standard Meta-Analysis?," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-16, April.
    • Handle: RePEc:plo:pone00:0061505
    DOI: 10.1371/journal.pone.0061505
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    References listed on IDEAS

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    1. Elin Shaddox & Francesco C. Stingo & Christine B. Peterson & Sean Jacobson & Charmion Cruickshank-Quinn & Katerina Kechris & Russell Bowler & Marina Vannucci, 2018. "A Bayesian Approach for Learning Gene Networks Underlying Disease Severity in COPD," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 59-85, April.
    2. Holger Weishaupt & Patrik Johansson & Christopher Engström & Sven Nelander & Sergei Silvestrov & Fredrik J Swartling, 2017. "Loss of Conservation of Graph Centralities in Reverse-engineered Transcriptional Regulatory Networks," Methodology and Computing in Applied Probability, Springer, vol. 19(4), pages 1089-1105, December.
    3. Tiffany C Armenta & Steve W Cole & Daniel H Geschwind & Daniel T Blumstein & Robert K Wayne, 2019. "Gene expression shifts in yellow-bellied marmots prior to natal dispersal," Behavioral Ecology, International Society for Behavioral Ecology, vol. 30(2), pages 267-277.

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