Journal ArticleDOI
A General Framework for Weighted Gene Co-Expression Network Analysis
Bin Zhang,Steve Horvath +1 more
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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
Integrative network analysis reveals molecular mechanisms of blood pressure regulation
Tianxiao Huan,Qingying Meng,Mohamed A. Saleh,Mohamed A. Saleh,Allison E. Norlander,Roby Joehanes,Jun Zhu,Brian H. Chen,Bin Zhang,Andrew D. Johnson,Saixia Ying,Paul Courchesne,Nalini Raghavachari,Richard J. Wang,Poching Liu,Christopher J. O'Donnell,Ramachandran S. Vasan,Peter J. Munson,Meena S. Madhur,David G. Harrison,Xia Yang,Daniel Levy +21 more
TL;DR: A significant number of genes predicted to be regulated by SH2B3 in gene networks are perturbed in Sh2b3−/− mice, which demonstrate an exaggerated pressor response to angiotensin II infusion.
Journal ArticleDOI
Inferring Interaction Networks From Multi-Omics Data.
TL;DR: State-of-the-art techniques for inferring the topology of interaction networks from functional multi-omics data are reviewed, encompassing graphical models with multiple node types and quantitative-trait-loci based approaches.
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Transcriptome analyses reveal molecular mechanisms underlying functional recovery after spinal cord injury
Hongmei Duan,Weihong Ge,Aifeng Zhang,Yue Xi,Zhihua Chen,Dandan Luo,Yin Cheng,Kevin S. Fan,Steve Horvath,Michael V. Sofroniew,Liming Cheng,Zhaoyang Yang,Yi E. Sun,Yi E. Sun,Xiaoguang Li +14 more
TL;DR: It is discovered that neurotrophin-3 (NT3)-loaded chitosan provided an excellent microenvironment to facilitate nerve growth, new neurogenesis, and functional recovery of completely transected spinal cord in rats.
Journal ArticleDOI
Integrating GWAS and Co-expression Network Data Identifies Bone Mineral Density Genes SPTBN1 and MARK3 and an Osteoblast Functional Module.
Gina M. Calabrese,Larry D. Mesner,Joseph P. Stains,Steven M. Tommasini,Mark C. Horowitz,Clifford J. Rosen,Charles R. Farber +6 more
TL;DR: A bone co-expression network is used to predict causal genes at BMD GWAS loci based on the premise that genes underlying a disease are often functionally related and functionally related genes are often co-expressed.
Journal ArticleDOI
Ecogenomics of virophages and their giant virus hosts assessed through time series metagenomics.
Simon Roux,Simon Roux,Leong-Keat Chan,Rob Egan,Rex R. Malmstrom,Katherine D. McMahon,Matthew B. Sullivan +6 more
TL;DR: Time series metagenomics data is used to identify and study the dynamics of 25 uncultivated virophage populations, 17 of which represented by complete or near-complete genomes, in two North American freshwater lakes, revealing new viroPHage genera and their putative ecological interactions in two freshwater lakes.
References
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