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. 2018 Jun 13;17(3).
doi: 10.1515/sagmb-2016-0059.

A statistical method for measuring activation of gene regulatory networks

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Free article

A statistical method for measuring activation of gene regulatory networks

Gustavo H Esteves et al. Stat Appl Genet Mol Biol. .
Free article

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.

Keywords: gene regulatory networks; hypothesis tests; systems biology.

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References

    1. Alberich, R., M. Llabrés, D. Sánchez, M. Simeoni, and M. Tuduri (2014): “MP-Align: alignment of metabolic pathways,” BMC Syst. Biol., 8, 58.
    1. Ashburner, M., C. A. Ball, J. A. Blake, D. Botstein, H. Butler, J. M. Cherry, A. P. Davis, K. Dolinski, S. S. Dwight, J. T. Eppig, M. A. Harris, D. P. Hill, L. Issel-Tarver, A. Kasarskis, S. Lewis, J. C. Matese, J. E. Richardson, M. Ringwald, G. M. Rubin, and G. Sherlock (2000): “Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium,” Nat. Genet., 25, 25–29.
    1. Benjamini, Y. and Y. Hochberg (1995): “Controling the false discorevy rate: a practical and powerful approach to multiple testing,” J. R. Stat. Soc. B, 57, 289–300.
    1. Boersema, P. J., A. Kahraman, and P. Picotti (2015): “Proteomics beyond large-scale protein expression analysis,” Curr. Opin. Biotechnol., 34, 162–170.
    1. Butte, A. J. and I. S. Kohane (2000): “Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements,” Pac. Symp. Biocomput., 5, 415–426.

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