Geometric Interpretation of Gene Coexpression Network Analysis

By Steve Horvath, Jun Dong. PLoS Comput. Biol., 2008

826 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/555f8df4825d96a4af877dd8b8df20d26de02327

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In Molecular Biology

1. Steve Horvath
2. Peter Langfelder
3. Ye Deng
4. Lin Song
5. Jizhong Zhou
6. Daniel H. Geschwind
7. Zhili He
8. Feng Luo

Citations in the corpus

• Aging effects on DNA methylation modules in human brain and blood tissue (2012, background): “Module membership measures allow one to efficiently annotate all methylation profiles on the array [51].”
• Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood (2012, methodology): “We employed weighted gene co-methylation network analysis [35,50,51] as described in R [52] to find weighted signed co-methylation networks (modules) in the cortex, cerebellum and peripheral blood using log transformed MEDIPS scores.”
• Strategies for aggregating gene expression data: The collapseRows R function (2011, methodology): “The connectivity (or degree) is the most widely used network statistic for describing the topological properties of a network node (gene) [13].”
• The responses and adaptations of microbial communities to salinity in farmland soils: a molecular ecological network analysis. (2017, methodology): “Furthermore, eigengene analysis (Langfelder and Horvath, 2007; Horvath and Dong, 2008) was performed to reveal the higher-order organization of the constructed pMENs.”
• Functional Molecular Ecological Networks (2010, background): “Because using many unrelated individual variables may mask the signature of any significant variables, the trait-based gene significance measure (30), defined as the square of the correlation between the signal intensity of a gene and each soil or”
• Weighted Gene Coexpression Network Analysis: State of the Art (2010, background): “Many biological networks share topological properties (Horvath and Dong, 2008; Horvath et al., 2006; Langfelder and Horvath, 2007; Oldham et al., 2006).”
• WGCNA: an R package for weighted correlation network analysis (2008, methodology): “Many topological properties of networks can be succinctly described using network concepts, also known as network statistics or indices [11,33].”
• Is My Network Module Preserved and Reproducible? (2011, background): “In particular, the geometric interpretation of correlation networks [29,45] can be Figure 5.”
• Functional organization of the transcriptome in human brain (2008, background): “This quantity, which we refer to as a gene's 'module membership', describes the extent to which a gene conforms to the characteristic expression pattern of a modul”
• Epigenetic Predictor of Age (2011, methodology): “We previously described weighted correlation network analysis (WGCNA) as a data reduction scheme [10,11].”
• Comparison of co-expression measures: mutual information, correlation, and model based indices (2012, methodology): “Sixth, the correlation allows for a straightforward angular interpretation, which facilitates a geometric interpretation of network methods and concepts [59].”
• Molecular ecological network analyses (2012, methodology): “Horvath S, Dong J: Geometric interpretation of gene coexpression network analysis.”
• Phylogenetic Molecular Ecological Network of Soil Microbial Communities in Response to Elevated CO2 (2011, methodology): “We used the trait-based OTU significance measure (24), which is referred to as the square of the correlation between the signal intensity of an OTU and each soil variable, to determine a common subgroup of soil properties important to network interactions.”