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Statistical indicators of collective behavior and functional clusters in gene networks of yeast

Author

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  • J. Živković
  • B. Tadić
  • N. Wick
  • S. Thurner

Abstract

We analyze gene expression time-series data of yeast (S. cerevisiae) measured along two full cell-cycles. We quantify these data by using q-exponentials, gene expression ranking and a temporal mean-variance analysis. We construct gene interaction networks based on correlation coefficients and study the formation of the corresponding giant components and minimum spanning trees. By coloring genes according to their cell function we find functional clusters in the correlation networks and functional branches in the associated trees. Our results suggest that a percolation point of functional clusters can be identified on these gene expression correlation networks. Copyright EDP Sciences/Società Italiana di Fisica/Springer-Verlag 2006

Suggested Citation

  • J. Živković & B. Tadić & N. Wick & S. Thurner, 2006. "Statistical indicators of collective behavior and functional clusters in gene networks of yeast," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 50(1), pages 255-258, March.
    • Handle: RePEc:spr:eurphb:v:50:y:2006:i:1:p:255-258
    DOI: 10.1140/epjb/e2006-00103-4
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    Cited by:

    1. Wang, Luo-Qing & Xu, Yong-Xiang, 2018. "Distribution of individual status in the invisibility similarity network of new social strata in Shanghai," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 426-434.
    2. Andjelković, Miroslav & Tadić, Bosiljka & Maletić, Slobodan & Rajković, Milan, 2015. "Hierarchical sequencing of online social graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 582-595.
    3. Qing Cai & Hai-Chuan Xu & Wei-Xing Zhou, 2016. "Taylor's Law of temporal fluctuation scaling in stock illiquidity," Papers 1610.01149, arXiv.org.

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