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
Network neighborhood analysis with the multi-node topological overlap measure
Ai Li,Steve Horvath +1 more
TL;DR: The pairwise topological overlap measure is generalized to multiple network nodes and subsequently used in a recursive neighborhood construction method, and empirical evidence that the resulting neighborhoods are biologically meaningful is provided.
Journal ArticleDOI
Huntington's disease accelerates epigenetic aging of human brain and disrupts DNA methylation levels.
Steve Horvath,Peter Langfelder,Seung Kwak,Jeff Aaronson,Jim Rosinski,Thomas F. Vogt,Marika Eszes,Richard L.M. Faull,Maurice A. Curtis,Henry J. Waldvogel,Oi-Wa Choi,Spencer Tung,Harry V. Vinters,Giovanni Coppola,Giovanni Coppola,X. William Yang,X. William Yang +16 more
TL;DR: HD is associated with an accelerated epigenetic age of specific brain regions and more broadly with substantial changes in brain methylation levels, and 11 co-methylation modules with a significant association with HD status across 3 broad cortical regions are identified.
Journal ArticleDOI
A lymphocyte-microglia-astrocyte axis in chronic active multiple sclerosis.
Martina Absinta,Martina Absinta,Martina Absinta,Dragan Maric,Marjan Gharagozloo,Thomas Garton,Matthew D. Smith,Jing Jin,Kathryn C. Fitzgerald,Anya Song,Poching Liu,Jing-Ping Lin,Tianxia Wu,Kory R. Johnson,Dorian B. McGavern,Dorothy P. Schafer,Peter A. Calabresi,Daniel S. Reich +17 more
TL;DR: In this article, the authors used MRI-informed single-nucleus RNA sequencing to profile the edge of demyelinated white matter lesions at various stages of inflammation and uncovered notable glial and immune cell diversity, especially at the chronically inflamed lesion edge.
Journal ArticleDOI
A survey of the genetics of stomach, liver, and adipose gene expression from a morbidly obese cohort
Danielle M. Greenawalt,Radu Dobrin,Eugene Chudin,Ida J. Hatoum,Christine Suver,John Beaulaurier,Bin Zhang,Victor M. Castro,Jun Zhu,Solveig K. Sieberts,Susanna Wang,Cliona Molony,Steven B. Heymsfield,Daniel M. Kemp,Marc L. Reitman,Pek Yee Lum,Eric E. Schadt,Lee M. Kaplan +17 more
TL;DR: It is demonstrated how these eSNPs provide a high-quality disease map for each tissue in morbidly obese patients to not only inform genetic associations identified in this cohort, but in previously published genome-wide association studies as well.
Journal ArticleDOI
CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses.
Pedro de Sá Tavares Russo,Gustavo Rodrigues Ferreira,Lucas Esteves Cardozo,Matheus Carvalho Bürger,Raúl Arias-Carrasco,Sandra Regina Maruyama,Thiago Dominguez Crespo Hirata,Diógenes S. de Lima,Fernando M. Passos,Kiyoshi F. Fukutani,Melissa Lever,João Santana da Silva,Vinicius Maracaja-Coutinho,Helder I. Nakaya +13 more
TL;DR: The CEMiTool R package provides users with an easy-to-use method to automatically implement gene co-expression network analyses, obtain key information about the discovered gene modules using additional downstream analyses and retrieve publication-ready results via a high-quality interactive report.
References
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