Journal ArticleDOI
A General Framework for Weighted Gene Co-Expression Network Analysis
Bin Zhang,Steve Horvath +1 more
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
Citations
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Journal ArticleDOI
A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility
Mark P. Keller,YounJeong Choi,Ping Wang,Dawn Belt Davis,Mary E. Rabaglia,Angie T. Oler,Donald S. Stapleton,Carmen Argmann,Kathryn L. Schueler,Seve Edwards,H Adam Steinberg,Elias Chaibub Neto,Robert R. Kleinhanz,Scott Turner,Marc K. Hellerstein,Eric E. Schadt,Brian S. Yandell,Christina Kendziorski,Alan D. Attie +18 more
TL;DR: A strong correlation is found between (2)H(2)O incorporation into islet DNA in vivo and the expression pattern of the cell cycle module and the pattern is highly correlated with that of several individual genes in insulin target tissues, including Igf2, which has been shown to promote beta-cell proliferation.
Journal ArticleDOI
Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types
TL;DR: It is found that prognostic mRNA genes tend not to be hub genes, and this pattern is unique to the corresponding cancer-type specific network, and the target genes of prognostic miRNA genes show similar patterns.
Journal ArticleDOI
Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks.
Marc R. J. Carlson,Bin Zhang,Zixing Fang,Zixing Fang,Paul S. Mischel,Steve Horvath,Stanley F. Nelson +6 more
TL;DR: Application of these techniques can allow a finer scale prediction of relative gene importance for a particular process within a group of similarly expressed genes.
Journal ArticleDOI
Weighted gene coexpression network analysis strategies applied to mouse weight
Tova F Fuller,Anatole Ghazalpour,Jason E. Aten,Thomas A. Drake,Aldons J. Lusis,Steve Horvath +5 more
TL;DR: The results demonstrate the utility of WGCNA in identifying genetic drivers and in finding genetic pathways represented by gene modules and provide evidence that integration of network properties may well help chart the path across the gene–trait chasm.
Journal ArticleDOI
Co-regulatory networks of human serum proteins link genetics to disease
Valur Emilsson,Marjan Ilkov,John Lamb,Nancy Finkel,Elias F. Gudmundsson,Rebecca Pitts,Heather Hoover,Valborg Gudmundsdottir,Shane R. Horman,Thor Aspelund,Le Shu,Vladimir Trifonov,Sigurdur Sigurdsson,Andrei Manolescu,Jun Zhu,Orn Olafsson,Johanna Jakobsdottir,Scott A. Lesley,Jeremy To,Jia Zhang,Tamara B. Harris,Lenore J. Launer,Bin Zhang,Gudny Eiriksdottir,Xia Yang,Anthony P. Orth,Lori L. Jennings,Vilmundur Gudnason +27 more
TL;DR: A deep proteome analysis of human serum reveals the relationship between disease and genetics and revealed co-regulated groups of circulating proteins that incorporated regulatory control between tissues and demonstrated close relationships to past, current, and future disease states.
References
More filters
Journal ArticleDOI
Emergence of Scaling in Random Networks
TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Journal ArticleDOI
Statistical mechanics of complex networks
TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
Journal ArticleDOI
Cluster analysis and display of genome-wide expression patterns
TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
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
TL;DR: In this article, the authors discuss their experience designing and implementing a statistical computing language, which combines what they felt were useful features from two existing computer languages, and they feel that the new language provides advantages in the areas of portability, computational efficiency, memory management, and scope.
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