Co-expression networks: graph properties and topological comparisons

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.

[1]  N. Meinshausen,et al.  High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.

[2]  Susan A. Murphy,et al.  Monographs on statistics and applied probability , 1990 .

[3]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[4]  M. Newman,et al.  Mixing patterns in networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Nicola J. Rinaldi,et al.  Transcriptional regulatory code of a eukaryotic genome , 2004, Nature.

[6]  Kara Dolinski,et al.  The BioGRID Interaction Database: 2008 update , 2008, Nucleic Acids Res..

[7]  M. Gerstein,et al.  Relating whole-genome expression data with protein-protein interactions. , 2002, Genome research.

[8]  Christian von Mering,et al.  STRING: known and predicted protein–protein associations, integrated and transferred across organisms , 2004, Nucleic Acids Res..

[9]  Korbinian Strimmer,et al.  An empirical Bayes approach to inferring large-scale gene association networks , 2005, Bioinform..

[10]  LuHui,et al.  Correlation between gene expression profiles and protein--protein interactions within and across genomes , 2005 .

[11]  Joshua M. Stuart,et al.  A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules , 2003, Science.

[12]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[13]  Hui Lu,et al.  Correlation between gene expression profiles and protein-protein interactions within and across genomes , 2005, Bioinform..

[14]  J. Schmee An Introduction to Multivariate Statistical Analysis , 1986 .

[15]  L. Kruglyak,et al.  Genetic Dissection of Transcriptional Regulation in Budding Yeast , 2002, Science.

[16]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[17]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[18]  Hongzhe Li,et al.  Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of genetic networks. , 2006, Biostatistics.

[19]  Jun Dong,et al.  Geometric Interpretation of Gene Coexpression Network Analysis , 2008, PLoS Comput. Biol..

[20]  Peng Qiu,et al.  Fast calculation of pairwise mutual information for gene regulatory network reconstruction , 2009, Comput. Methods Programs Biomed..

[21]  Martin Steffen,et al.  Automated modelling of signal transduction networks , 2002, BMC Bioinformatics.

[22]  K. Strimmer,et al.  Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics , 2011 .

[23]  John D. Storey,et al.  Genetic interactions between polymorphisms that affect gene expression in yeast , 2005, Nature.

[24]  Christian von Mering,et al.  STRING 8—a global view on proteins and their functional interactions in 630 organisms , 2008, Nucleic Acids Res..

[25]  Alessandro Vespignani,et al.  Large-scale topological and dynamical properties of the Internet. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  Philip S. Yu,et al.  A graph-based approach to systematically reconstruct human transcriptional regulatory modules , 2007, ISMB/ECCB.

[27]  S. Horvath,et al.  A General Framework for Weighted Gene Co-Expression Network Analysis , 2005, Statistical applications in genetics and molecular biology.

[28]  G. Church,et al.  Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae , 2001, Nature Genetics.