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CCor: A whole genome network‐based similarity measure between two genes

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  • Yiming Hu
  • Hongyu Zhao

Abstract

Measuring the similarity between genes is often the starting point for building gene regulatory networks. Most similarity measures used in practice only consider pairwise information with a few also consider network structure. Although theoretical properties of pairwise measures are well understood in the statistics literature, little is known about their statistical properties of those similarity measures based on network structure. In this article, we consider a new whole genome network‐based similarity measure, called CCor, that makes use of information of all the genes in the network. We derive a concentration inequality of CCor and compare it with the commonly used Pearson correlation coefficient for inferring network modules. Both theoretical analysis and real data example demonstrate the advantages of CCor over existing measures for inferring gene modules.

Suggested Citation

  • Yiming Hu & Hongyu Zhao, 2016. "CCor: A whole genome network‐based similarity measure between two genes," Biometrics, The International Biometric Society, vol. 72(4), pages 1216-1225, December.
    • Handle: RePEc:bla:biomet:v:72:y:2016:i:4:p:1216-1225
    DOI: 10.1111/biom.12508
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    References listed on IDEAS

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    1. Zhang Bin & Horvath Steve, 2005. "A General Framework for Weighted Gene Co-Expression Network Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-45, August.
    2. Dobra, Adrian & Hans, Chris & Jones, Beatrix & Nevins, J.R.Joseph R. & Yao, Guang & West, Mike, 2004. "Sparse graphical models for exploring gene expression data," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 196-212, July.
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    Cited by:

    1. Gong, Tingnan & Zhang, Weiping & Chen, Yu, 2023. "Uncovering block structures in large rectangular matrices," Journal of Multivariate Analysis, Elsevier, vol. 198(C).

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