Journal ArticleDOI
A General Framework for Weighted Gene Co-Expression Network Analysis
Bin Zhang,Steve Horvath +1 more
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TLDR
A general framework for `soft' thresholding that assigns a connection weight to each gene pair is described and several node connectivity measures are introduced and provided empirical evidence that they can be important for predicting the biological significance of a gene.Abstract:
Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An important question is whether it is biologically meaningful to encode gene co-expression using binary information (connected=1, unconnected=0). We describe a general framework for ;soft' thresholding that assigns a connection weight to each gene pair. This leads us to define the notion of a weighted gene co-expression network. For soft thresholding we propose several adjacency functions that convert the co-expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion). We generalize the following important network concepts to the case of weighted networks. First, we introduce several node connectivity measures and provide empirical evidence that they can be important for predicting the biological significance of a gene. Second, we provide theoretical and empirical evidence that the ;weighted' topological overlap measure (used to define gene modules) leads to more cohesive modules than its ;unweighted' counterpart. Third, we generalize the clustering coefficient to weighted networks. Unlike the unweighted clustering coefficient, the weighted clustering coefficient is not inversely related to the connectivity. We provide a model that shows how an inverse relationship between clustering coefficient and connectivity arises from hard thresholding. We apply our methods to simulated data, a cancer microarray data set, and a yeast microarray data set.read more
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Journal ArticleDOI
An Immune Response Network Associated with Blood Lipid Levels
Michael Inouye,Michael Inouye,Kaisa Silander,Eija Hämäläinen,Veikko Salomaa,Kennet Harald,Pekka Jousilahti,Satu Männistö,Johan G. Eriksson,Janna Saarela,Janna Saarela,Samuli Ripatti,Markus Perola,Gert-Jan B. van Ommen,Marja-Riitta Taskinen,Aarno Palotie,Emmanouil T. Dermitzakis,Emmanouil T. Dermitzakis,Leena Peltonen,Leena Peltonen,Leena Peltonen +20 more
TL;DR: A gene network linking blood lipids and circulating cell types is uncovered and insight is offered into the hypothesis that the inflammatory response plays a prominent role in metabolism and the potential control of atherogenesis is offered.
Journal ArticleDOI
Weighted Correlation Network Analysis (WGCNA) Applied to the Tomato Fruit Metabolome
Matthew V. DiLeo,Matthew V. DiLeo,Gary D. Strahan,Meghan den Bakker,Meghan den Bakker,Owen A. Hoekenga,Owen A. Hoekenga +6 more
TL;DR: The capabilities of three conventional and novel statistical approaches to summarize and decipher the tomato metabolome are compared, with WGCNA holding several advantages in the analysis of highly multivariate, complex data.
Journal ArticleDOI
From correlation to causation: analysis of metabolomics data using systems biology approaches
Antonio Rosato,Leonardo Tenori,Marta Cascante,Pedro Ramon De Atauri Carulla,Vitor A. P. Martins dos Santos,Edoardo Saccenti +5 more
TL;DR: Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolites studies.
Posted Content
A Multivariate Regression Approach to Association Analysis of Quantitative Trait Network
TL;DR: In this article, a graph-guided fused lasso (GFlasso) was proposed to identify genetic variations associated simultaneously with correlated traits, where the dependency structure among the quantitative traits was represented as a network and leveraged this trait network to encode structured regularizations in a multivariate regression model over the genotypes and traits.
Journal ArticleDOI
Using genetic markers to orient the edges in quantitative trait networks: The NEO software
TL;DR: Network Edge Orienting methods and software are developed that address the challenges of inferring unconfounded and directed gene networks from microarray-derived gene expression data by integrating mRNA levels with genetic marker data and Structural Equation Model (SEM) comparisons.
References
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Cluster analysis and display of genome-wide expression patterns
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Book
Finding Groups in Data: An Introduction to Cluster Analysis
TL;DR: An electrical signal transmission system, applicable to the transmission of signals from trackside hot box detector equipment for railroad locomotives and rolling stock, wherein a basic pulse train is transmitted whereof the pulses are of a selected first amplitude and represent a train axle count.
Journal ArticleDOI
R: A Language for Data Analysis and Graphics
Ross Ihaka,Robert Gentleman +1 more
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