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A statistical method for measuring activation of gene regulatory networks

Author

Listed:
  • Esteves Gustavo H.

    (Statistics Department, University of Paraíba State, Campina Grande, PB, Brazil)

  • Reis Luiz F. L.

    (Teaching and Research Institute, Sírio-Libânes Hospital, São Paulo-SP, Brazil)

Abstract

Motivation: Gene expression data analysis is of great importance for modern molecular biology, given our ability to measure the expression profiles of thousands of genes and enabling studies rooted in systems biology. In this work, we propose a simple statistical model for the activation measuring of gene regulatory networks, instead of the traditional gene co-expression networks. Results: We present the mathematical construction of a statistical procedure for testing hypothesis regarding gene regulatory network activation. The real probability distribution for the test statistic is evaluated by a permutation based study. To illustrate the functionality of the proposed methodology, we also present a simple example based on a small hypothetical network and the activation measuring of two KEGG networks, both based on gene expression data collected from gastric and esophageal samples. The two KEGG networks were also analyzed for a public database, available through NCBI-GEO, presented as Supplementary Material. Availability: This method was implemented in an R package that is available at the BioConductor project website under the name maigesPack.

Suggested Citation

  • Esteves Gustavo H. & Reis Luiz F. L., 2018. "A statistical method for measuring activation of gene regulatory networks," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 17(3), pages 1-11, June.
    • Handle: RePEc:bpj:sagmbi:v:17:y:2018:i:3:p:11:n:1
    DOI: 10.1515/sagmb-2016-0059
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    References listed on IDEAS

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    1. Schäfer Juliane & Strimmer Korbinian, 2005. "A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-32, November.
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