Journal Article

Co-expression networks: graph properties and topological comparisons

Ramon Xulvi-Brunet and Hongzhe Li

in Bioinformatics

Volume 26, issue 2, pages 205-214
Published in print January 2010 | ISSN: 1367-4803
Published online November 2009 | e-ISSN: 1460-2059 | DOI:
Co-expression networks: graph properties and topological comparisons

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Motivation: Microarray-based gene expression data have been generated widely to study different biological processes and systems. Gene co-expression networks are often used to extract information about groups of genes that are ‘functionally’ related or co-regulated. However, the structural properties of such co-expression networks have not been rigorously studied and fully compared with known biological networks. In this article, we aim at investigating the structural properties of co-expression networks inferred for the species Saccharomyces Cerevisiae and comparing them with the topological properties of the known, well-established transcriptional network, MIPS physical network and protein–protein interaction (PPI) network of yeast.

Results: These topological comparisons indicate that co-expression networks are not distinctly related with either the PPI or the MIPS physical interaction networks, showing important structural differences between them. When focusing on a more literal comparison, vertex by vertex and edge by edge, the conclusion is the same: the fact that two genes exhibit a high gene expression correlation degree does not seem to obviously correlate with the existence of a physical binding between the proteins produced by these genes or the existence of a MIPS physical interaction between the genes. The comparison of the yeast regulatory network with inferred yeast co-expression networks would suggest, however, that they could somehow be related.

Conclusions: We conclude that the gene expression-based co-expression networks reflect more on the gene regulatory networks but less on the PPI or MIPS physical interaction networks.


Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  8055 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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