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
Citations
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Journal ArticleDOI
Spatiotemporal DNA methylome dynamics of the developing mouse fetus.
Yupeng He,Yupeng He,Manoj Hariharan,David U. Gorkin,Diane E. Dickel,Chongyuan Luo,Rosa Castanon,Joseph R. Nery,Ah Young Lee,Yuan Zhao,Yuan Zhao,Hui Huang,Hui Huang,Brian A. Williams,Diane Trout,Henry Amrhein,Rongxin Fang,Rongxin Fang,Huaming Chen,Bin Li,Axel Visel,Axel Visel,Len A. Pennacchio,Len A. Pennacchio,Bing Ren,Bing Ren,Joseph R. Ecker +26 more
TL;DR: These spatiotemporal epigenome maps provide a resource for studies of gene regulation during tissue or organ progression, and a starting point for investigating regulatory elements that are involved in human developmental disorders.
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Determining sequencing depth in a single-cell RNA-seq experiment.
TL;DR: A mathematical framework is developed which reveals that, for estimating many important gene properties, the optimal allocation is to sequence at a depth of around one read per cell per gene.
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Large-scale assessment of the gliomasphere model system
Dan R. Laks,Thomas J. Crisman,Michelle Y. S. Shih,Jack Mottahedeh,Fuying Gao,Jantzen Sperry,Matthew C. Garrett,William H. Yong,Timothy F. Cloughesy,Linda M. Liau,Albert Lai,Giovanni Coppola,Harley I. Kornblum +12 more
TL;DR: A comprehensive assessment reveals advantages and limitations of using gliomasphere cultures to model GBM biology, and provides a novel strategy for selecting genes for future study.
Journal ArticleDOI
Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data
TL;DR: This review summarizes and categorizes the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data, and overview each of strategies and introduce representative methods respectively.
Journal ArticleDOI
Large scale microarray profiling and coexpression network analysis of CHO cells identifies transcriptional modules associated with growth and productivity.
Colin Clarke,Padraig Doolan,Niall Barron,Paula Meleady,Finbarr O'Sullivan,Patrick Gammell,Mark Melville,Mark Leonard,Martin Clynes +8 more
TL;DR: The approach presented in this study provides a novel perspective on the CHO cell transcriptome as well as identifying a number of significant biological processes within coexpressed gene clusters including cell cycle, protein secretion and vesicle transport.
References
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Finding Groups in Data: An Introduction to Cluster Analysis
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