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
More filters
Journal ArticleDOI
Network Analysis as an Alternative Approach to Conceptualizing Eating Disorders: Implications for Research and Treatment.
TL;DR: In this paper, the authors discuss the relevance of the network approach for the conceptualization of eating disorders (ED) and highlight novel applications of NA, specifically the ability to identify within-person maintaining symptoms and the potential treatment implications for ED that network methods may hold.
Journal ArticleDOI
Contribution of Gene Regulatory Networks to Heritability of Coronary Artery Disease.
Lingyao Zeng,Husain A. Talukdar,Simon Koplev,Chiara Giannarelli,Torbjörn Ivert,Li-Ming Gan,Arno Ruusalepp,Eric E. Schadt,Jason C. Kovacic,Aldons J. Lusis,Tom Michoel,Heribert Schunkert,Johan Björkegren +12 more
TL;DR: GRNs capture a major portion of genetic variance and contribute to heritability beyond that of genetic loci currently known to affect CAD risk.
Journal ArticleDOI
Omics-based analyses revealed metabolic responses of Clostridium acetobutylicum to lignocellulose-derived inhibitors furfural, formic acid and phenol stress for butanol fermentation
TL;DR: Integrated omics platforms provide insight into the cellular responses of C. acetobutylicum to cytotoxic inhibitors released during the deconstruction of lignocellulose to fully improve the strain to adapt to a challenging culture environment.
Journal ArticleDOI
Comparative Systems Analyses Reveal Molecular Signatures of Clinically tested Vaccine Adjuvants.
Thorunn A. Olafsdottir,Madelene Lindqvist,Intawat Nookaew,Peter Andersen,Jeroen Maertzdorf,Josefine Persson,Dennis Christensen,Yuan Zhang,Jenna Anderson,Sakda Khoomrung,Partho Sen,Else Marie Agger,Rhea N. Coler,Darrick Carter,Andreas Meinke,Rino Rappuoli,Stefan H. E. Kaufmann,Steven G. Reed,Ali M. Harandi +18 more
TL;DR: Co-expression analysis revealed blood gene modules highly enriched for molecules with documented roles in T follicular helper (TFH) and germinal center (GC) responses, and it was shown that all adjuvants enhanced, although with different magnitude and kinetics, TFH and GC B cell responses in draining lymph nodes.
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.
Related Papers (5)
WGCNA: an R package for weighted correlation network analysis.
Peter Langfelder,Steve Horvath +1 more
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.
Gene Ontology: tool for the unification of biology
M Ashburner,Catherine A. Ball,Judith A. Blake,David Botstein,Heather Butler,J. M. Cherry,Allan Peter Davis,Kara Dolinski,Selina S. Dwight,J.T. Eppig,Midori A. Harris,David P. Hill,Laurie Issel-Tarver,Andrew Kasarskis,Suzanna E. Lewis,John C. Matese,Joel E. Richardson,M. Ringwald,Gerald M. Rubin,Gavin Sherlock +19 more