Abstract
The merging of network theory and microarray data analysis techniques has spawned a new field: gene coexpression network analysis. While network methods are increasingly used in biology, the network vocabulary of computational biologists tends to be far more limited than that of, say, social network theorists. Here we review and propose several potentially useful network concepts. We take advantage of the relationship between network theory and the field of microarray data analysis to clarify the meaning of and the relationship among network concepts in gene coexpression networks. Network theory offers a wealth of intuitive concepts for describing the pairwise relationships among genes, which are depicted in cluster trees and heat maps. Conversely, microarray data analysis techniques (singular value decomposition, tests of differential expression) can also be used to address difficult problems in network theory. We describe conditions when a close relationship exists between network analysis and microarray data analysis techniques, and provide a rough dictionary for translating between the two fields. Using the angular interpretation of correlations, we provide a geometric interpretation of network theoretic concepts and derive unexpected relationships among them. We use the singular value decomposition of module expression data to characterize approximately factorizable gene coexpression networks, i.e., adjacency matrices that factor into node specific contributions. High and low level views of coexpression networks allow us to study the relationships among modules and among module genes, respectively. We characterize coexpression networks where hub genes are significant with respect to a microarray sample trait and show that the network concept of intramodular connectivity can be interpreted as a fuzzy measure of module membership. We illustrate our results using human, mouse, and yeast microarray gene expression data. The unification of coexpression network methods with traditional data mining methods can inform the application and development of systems biologic methods.
Highlights
We show how coexpression network language affects our understanding of biology
We provide a short dictionary for translating between microarray data analysis language and network theory language to facilitate communication between the two fields
We are interested in gene significance measures that are based on a microarray sample trait, e.g., a clinical outcome
Summary
Common local topological properties include the presence of recurring patterns of interconnections (‘network motifs’) in regulation networks [5,6,7]. One goal of this article is to describe existing and novel network concepts ( known as network statistics or indices [8]) that can be used to describe local and global network properties. The clustering coefficient [9] is a network concept, which measures the cohesiveness of the neighborhood of a node. Gene significance measures are of great practical importance since they allow one to incorporate external gene information into the network analysis. A gene significance measure could indicate pathway membership. The Student t-test of differential expression leads to a gene significance measure. Gene filtering methods aim to find a list of (differentially expressed) genes that are significantly associated with the microarray sample trait; another example are microarraybased prediction methods that aim to accurately predict the sample trait on the basis of the gene expression data
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