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Comparing Statistical Methods for Constructing Large Scale Gene Networks

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  • Jeffrey D Allen
  • Yang Xie
  • Min Chen
  • Luc Girard
  • Guanghua Xiao

Abstract

The gene regulatory network (GRN) reveals the regulatory relationships among genes and can provide a systematic understanding of molecular mechanisms underlying biological processes. The importance of computer simulations in understanding cellular processes is now widely accepted; a variety of algorithms have been developed to study these biological networks. The goal of this study is to provide a comprehensive evaluation and a practical guide to aid in choosing statistical methods for constructing large scale GRNs. Using both simulation studies and a real application in E. coli data, we compare different methods in terms of sensitivity and specificity in identifying the true connections and the hub genes, the ease of use, and computational speed. Our results show that these algorithms performed reasonably well, and each method has its own advantages: (1) GeneNet, WGCNA (Weighted Correlation Network Analysis), and ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) performed well in constructing the global network structure; (2) GeneNet and SPACE (Sparse PArtial Correlation Estimation) performed well in identifying a few connections with high specificity.

Suggested Citation

  • Jeffrey D Allen & Yang Xie & Min Chen & Luc Girard & Guanghua Xiao, 2012. "Comparing Statistical Methods for Constructing Large Scale Gene Networks," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-9, January.
    • Handle: RePEc:plo:pone00:0029348
    DOI: 10.1371/journal.pone.0029348
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    Cited by:

    1. Xinhai Ye & Yi Yang & Can Zhao & Shan Xiao & Yu H. Sun & Chun He & Shijiao Xiong & Xianxin Zhao & Bo Zhang & Haiwei Lin & Jiamin Shi & Yang Mei & Hongxing Xu & Qi Fang & Fei Wu & Dunsong Li & Gongyin , 2022. "Genomic signatures associated with maintenance of genome stability and venom turnover in two parasitoid wasps," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    2. Alfonso Monaco & Nicola Amoroso & Loredana Bellantuono & Eufemia Lella & Angela Lombardi & Anna Monda & Andrea Tateo & Roberto Bellotti & Sabina Tangaro, 2019. "Shannon entropy approach reveals relevant genes in Alzheimer’s disease," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-29, December.
    3. 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.
    4. Samantha Riccadonna & Giuseppe Jurman & Roberto Visintainer & Michele Filosi & Cesare Furlanello, 2016. "DTW-MIC Coexpression Networks from Time-Course Data," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-29, March.

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