The Prediction of Local Modular Structures in a Co-Expression Network Based on Gene Expression Datasets


Yoshiyuki Ogata[1] (yogata@kazusa.or.jp)
Nozomu Sakurai[1] (sakurai@kazusa.or.jp)
Hideyuki Suzuki[1] (hsuzuki@kazusa.or.jp)
Koh Aoki[1] (kaoki@kazusa.or.jp)
Kazuki Saito[2][3] (ksaito@faculty.chiba-u.jp)
Daisuke Shibata[1] (shibata@kazusa.or.jp)

[1] Department of Biotechnology Research, Kazusa DNA Research Institute, 2-6-7 Kazusa-Kamatari, Kisarazu, Chiba 292-0818, Japan
[2] Graduate School of Pharmaceutical Science, Chiba University, Chiba 263-8522, Japan
[3] Plant Science Center, RIKEN, Yokohama, Kanagawa 230-0045, Japan

Abstract

In scientific fields such as systems biology, evaluation of the relationship between network members (vertices) is approached using a network structure. In a co-expression network, comprising genes (vertices) and gene-to-gene links (edges) representing co-expression relationships, local modular structures with tight intra-modular connections include genes that are co-expressed with each other. For detecting such modules from among the whole network, an approach to evaluate network topology between modules as well as intra-modular network topology is useful. To detect such modules, we combined a novel inter-modular index with network density, the representative intra-modular index, instead of a single use of network density. We designed an algorithm to optimize the combinatory index for a module and applied it to Arabidopsis co-expression analysis. To verify the relation between modules obtained using our algorithm and biological knowledge, we compared it to the other tools for co-expression network analyses using the KEGG pathways, indicating that our algorithm detected network modules representing better associations with the pathways. It is also applicable to a large dataset of gene expression profiles, which is difficult to calculate in a mass.

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Japanese Society for Bioinformatics