Gene network interconnectedness and the generalized topological overlap measure

By Andy M. Yip, Steve Horvath. BMC Bioinformatics, 2007

613 citations recorded by the source corpus.

BackgroundNetwork methods are increasingly used to represent the interactions of genes and/or proteins. Genes or proteins that are directly linked may have a similar biological function or may be part of the same biological pathway. Since the information on the connection (adjacency) between 2 nodes may be noisy or incomplete, it can be desirable to consider alternative measures of pairwise interconnectedness. Here we study a class of measures that are proportional to the number of neighbors that a pair of nodes share in common. For example, the topological overlap measure by Ravasz et al. [1] can be interpreted as a measure of agreement between the m = 1 step neighborhoods of 2 nodes. Several studies have shown that two proteins having a higher topological overlap are more likely to belong to the same functional class than proteins having a lower topological overlap. Here we address the question whether a measure of topological overlap based on higher-order neighborhoods could give rise to a more robust and sensitive measure of interconnectedness.ResultsWe generalize the topological overlap measure from m = 1 step neighborhoods to m ≥ 2 step neighborhoods. This allows us to define the m-th order generalized topological overlap measure (GTOM) by (i) counting the number of m-step neighbors that a pair of nodes share and (ii) normalizing it to take a value between 0 and 1. Using theoretical arguments, a yeast co-expression network application, and a fly protein network application, we illustrate the usefulness of the proposed measure for module detection and gene neighborhood analysis.ConclusionTopological overlap can serve as an important filter to counter the effects of spurious or missing connections between network nodes. The m-th order topological overlap measure allows one to trade-off sensitivity versus specificity when it comes to defining pairwise interconnectedness and network modules.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/afa73141e57a32e201d646a025c852504ea9c6b2

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Citations in the corpus

WGCNA: an R package for weighted correlation network analysis (2008, methodology): “As default, we we use the topological overlap measure [5,25-27] since it has worked well in several applications.”
Aging effects on DNA methylation modules in human brain and blood tissue (2012, background): “Studies of our group [42,45-48] and of independent groups [49] provide empirical evidence that the topological overlap measure is a biologically meaningful network similarity measure.”
Weighted Gene Coexpression Network Analysis: State of the Art (2010, methodology): “Because ASPM is expressed at a very low level in normal brain (and normal body tissues) relative”
Eigengene networks for studying the relationships between co-expression modules (2007, background): “11) since this yields relatively large and robust modules [4,6,7,22,24].”
Comparison of co-expression measures: mutual information, correlation, and model based indices (2012, background): “The topological overlap measure can serve as a ﬁlter that decreases the eﬀect of spurious or weak connections and it can lead to more robust networks [17,18,68].”
Analysis of global control of Escherichia coli carbohydrate uptake (2007, neutral): “There is evidence that replacing A by TopOverlap may counter the adverse effects of spurious or missing adjacencies [25,26].”
Geometric Interpretation of Gene Coexpression Network Analysis (2008, background): “This module detection approach has led to biologically meaningful modules in several applications [1,8,23–25,33,39–43] but our theoretical results transcend this particular module detection method.”
Fast R Functions for Robust Correlations and Hierarchical Clustering (2012, background): “, the topological overlap matrix (TOM) which has been found to be relatively robust with respect to noise and to lead to biologically meaningful results (Ravasz et al. 2002; Yip and Horvath 2007).”
hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023, background): “Co-expression networks are based on transformed pairwise correlations of input features, resulting in a quantitative measure of relatedness between genes.(1,2) Hierarchical clustering on the network structure allows us to uncover functional modules of genes whose expression profiles are tightly inte”
Sequence to Medical Phenotypes: A Framework for Interpretation of Human Whole Genome DNA Sequence Data (2015, methodology): “A softthresholding parameter β was chosen to satisfy scale-free topology criterion based on r2 maximization for a linear fit with slope –1 to log(k) versus log(n(k)), and the topological overlap between genes was calculated [24], generating a network adjacency based on shared network neighbors.”