The prediction of local modular structures in a co-expression network based on gene expression datasets.
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.
Ogata
Yoshiyuki
Y
Department of Biotechnology Research, Kazusa DNA Research Institute, 2-6-7 Kazusa-Kamatari. Kisarazu, Chiba 292-0818, Japan. yogata@kazusa.or.jp
Sakurai
Nozomu
N
Suzuki
Hideyuki
H
Aoki
Koh
K
Saito
Kazuki
K
Shibata
Daisuke
D
D000465:Algorithms; D015870:Gene Expression; D020411:Oligonucleotide Array Sequence Analysis