Centrality-based pathway enrichment: a systematic approach for finding significant pathways dominated by key genes

Abstract

BackgroundBiological pathways are important for understanding biological mechanisms. Thus, finding important pathways that underlie biological problems helps researchers to focus on the most relevant sets of genes. Pathways resemble networks with complicated structures, but most of the existing pathway enrichment tools ignore topological information embedded within pathways, which limits their applicability.ResultsA systematic and extensible pathway enrichment method in which nodes are weighted by network centrality was proposed. We demonstrate how choice of pathway structure and centrality measurement, as well as the presence of key genes, affects pathway significance. We emphasize two improvements of our method over current methods. First, allowing for the diversity of genes’ characters and the difficulty of covering gene importance from all aspects, we set centrality as an optional parameter in the model. Second, nodes rather than genes form the basic unit of pathways, such that one node can be composed of several genes and one gene may reside in different nodes. By comparing our methodology to the original enrichment method using both simulation data and real-world data, we demonstrate the efficacy of our method in finding new pathways from biological perspective.ConclusionsOur method can benefit the systematic analysis of biological pathways and help to extract more meaningful information from gene expression data. The algorithm has been implemented as an R package CePa, and also a web-based version of CePa is provided.

Distinct Profiles for Mitochondrial t-RNAs and Small Nucleolar RNAs in Locally Invasive and Metastatic Colorectal Cancer

“…Key biological premises that underlie relationships of genes regarding “drivers’ and ‘passengers” consist of the following: highly co-expressed genes are more likely to be co-regulated; and those genes that display prominent connectivity patterns tend to play biologically influential or regulatory roles in disease-related processes (18-20). Measures of node centrality in biological networks may detect genes with critical functional roles.…”

“…Measures of node centrality in biological networks may detect genes with critical functional roles. In gene co-expression networks, highly connected genes (i.e., candidate hubs) have been associated with key drivers of disease pathways and gene connectivity has been shown to be a measure of functional relevance (18-20). Thus to explore whether there were potential cancer “driver” sRNAs, we performed correlational analyses among 41 differentially expressed sRNAs (9 MT-tRNAs, 32 snoRNAs), 204 miRNAs, and 665 mRNAs in 14 tumor pairs.…”

Analysis of Gene Expression Variance in Schizophrenia Using Structural Equation Modeling

“…Using the variances calculated above, with a simple indicator for whether the variance was greater in SCZ, we applied an overrepresentation analysis (ORA) using the EASE score, a modified one-tail Fisher exact p -value implemented in DAVID (Hosack et al, 2003). We also applied a node-based ORA method (Gu et al, 2012). The node-based enrichment score is a sum of nodes containing at least one gene with increased variance.…”

Protein Tyrosine Phosphatase Receptor Type γ Is a JAK Phosphatase and Negatively Regulates Leukocyte Integrin Activation

“…4A). Graph topological analysis performed by applying mathematical algorithms computing network node centrality indexes, categorizing proteins by topological relevance (11,(23)(24)(25)(26), and predicting functional significance (27) shows that SRC appears by far the most connected and central signaling protein in the network, followed by KIT, PTK2, ITGB1, STAT1, EGFR, PDGFRB, ABL1, and BTK (Supplemental Table I), suggesting that, in the context of chemoattractant signaling, Src family kinases are possibly main functional targets of PTPRG activity. Network modular decomposition (28)(29)(30) allowed isolating two modules, prevalently including activated (Fig.…”