Understanding network concepts in modules
Top Cited Papers
Open Access
- 4 June 2007
- journal article
- research article
- Published by Springer Science and Business Media LLC in BMC Systems Biology
- Vol. 1 (1), 1-20
- https://doi.org/10.1186/1752-0509-1-24
Abstract
Network concepts are increasingly used in biology and genetics. For example, the clustering coefficient has been used to understand network architecture; the connectivity (also known as degree) has been used to screen for cancer targets; and the topological overlap matrix has been used to define modules and to annotate genes. Dozens of potentially useful network concepts are known from graph theory. Here we study network concepts in special types of networks, which we refer to as approximately factorizable networks. In these networks, the pairwise connection strength (adjacency) between 2 network nodes can be factored into node specific contributions, named node 'conformity'. The node conformity turns out to be highly related to the connectivity. To provide a formalism for relating network concepts to each other, we define three types of network concepts: fundamental-, conformity-based-, and approximate conformity-based concepts. Fundamental concepts include the standard definitions of connectivity, density, centralization, heterogeneity, clustering coefficient, and topological overlap. The approximate conformity-based analogs of fundamental network concepts have several theoretical advantages. First, they allow one to derive simple relationships between seemingly disparate networks concepts. For example, we derive simple relationships between the clustering coefficient, the heterogeneity, the density, the centralization, and the topological overlap. The second advantage of approximate conformity-based network concepts is that they allow one to show that fundamental network concepts can be approximated by simple functions of the connectivity in module networks. Using protein-protein interaction, gene co-expression, and simulated data, we show that a) many networks comprised of module nodes are approximately factorizable and b) in these types of networks, simple relationships exist between seemingly disparate network concepts. Our results are implemented in freely available R software code, which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/ModuleConformity/ModuleNetworksKeywords
This publication has 50 references indexed in Scilit:
- Regulation of metabolic networks by small molecule metabolitesBMC Bioinformatics, 2007
- Assembly rules for protein networks derived from phylogenetic-statistical analysis of whole genomesBMC Evolutionary Biology, 2007
- Conservation and evolution of gene coexpression networks in human and chimpanzee brainsProceedings of the National Academy of Sciences of the United States of America, 2006
- Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular targetProceedings of the National Academy of Sciences of the United States of America, 2006
- Identification of inflammatory gene modules based on variations of human endothelial cell responses to oxidized lipidsProceedings of the National Academy of Sciences of the United States of America, 2006
- Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse WeightPLoS Genetics, 2006
- A metabolic network in the evolutionary context: Multiscale structure and modularityProceedings of the National Academy of Sciences of the United States of America, 2006
- A General Framework for Weighted Gene Co-Expression Network AnalysisStatistical Applications in Genetics and Molecular Biology, 2005
- Evidence for dynamically organized modularity in the yeast protein–protein interaction networkNature, 2004
- Network biology: understanding the cell's functional organizationNature Reviews Genetics, 2004