Modular gene expression in Poplar: a multilayer network approach

Authors

  • Andreas Grönlund,

    1. Umeå Plant Science Center Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, SE-901 83 Umeå, Sweden;
    2. Computational Life Science Cluster (CLIC), KBC, Umeå University, SE-901 87 Umeå, Sweden
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  • Rishikesh P. Bhalerao,

    1. Umeå Plant Science Center, Department of Plant Physiology, Umeå University, SE-901 87 Umeå, Sweden;
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  • Jan Karlsson

    1. Umeå Plant Science Center, Department of Plant Physiology, Umeå University, SE-901 87 Umeå, Sweden;
    2. Computational Life Science Cluster (CLIC), KBC, Umeå University, SE-901 87 Umeå, Sweden
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Author for correspondence:
Andreas Grönlund
Tel:+46 907 867 989
Fax:+46 907 868 165
Email: andreas.gronlund@plantphys.umu.se

Summary

  • • By applying a multilayer network approach to an extensive set of Poplar microarray data, a genome-wide coexpression network has been detected and explored.
  • • Multilayer networks were generated from minimum spanning trees (MSTs) using Kruskal's algorithm from random jack-knife resamplings of half of the full data set. The final network is obtained from the union of all the generated MSTs.
  • • The gene expression correlations display a highly clustered topology, which is more pronounced when introducing links appearing in relatively few of the generated MSTs. The network also reveals a modular architecture, reflecting functional groups with relatively frequent gene-to-gene communication. Furthermore, the observed modular structure overlaps with different gene activities in different tissues, and closely related tissues show similar over- and/or under-expression patterns at the modular scale.
  • • It is shown that including links that appear in a few of the generated MSTs increases the information quality of the network. In other words, a link may be ‘weak’ because it reflects rare signaling events rather than merely a signal weakened by noise. The method allows, from comparisons of random ‘null networks’, tuning to maximize the information obtainable.

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