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
Gene Bionetwork Analysis of Ovarian Primordial Follicle Development
Eric E. Nilsson,Marina I. Savenkova,Ryan Schindler,Bin Zhang,Eric E. Schadt,Michael K. Skinner +5 more
TL;DR: The relevant gene network associated with primordial follicle development was validated and the critical genes and pathways involved in this process were identified, one of the first applications of network analysis to a normal developmental process.
Journal ArticleDOI
Genes located in a chromosomal inversion are correlated with territorial song in white-throated sparrows
Wendy M. Zinzow-Kramer,Brent M. Horton,Clifton D. McKee,Justin M. Michaud,Gregory K. Tharp,James W. Thomas,Elaina M. Tuttle,Soojin V. Yi,Donna L. Maney +8 more
TL;DR: A set of candidate genes may mediate the effects of a chromosomal inversion on territorial behavior, which is linked to predictable variation in a suite of phenotypic traits including plumage color, aggression and parental behavior.
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Landscape of tumor suppressor long noncoding RNAs in breast cancer
TL;DR: The identified long noncoding RNAs correlated with a favorable prognosis in breast cancer patients and the patients in the pancancer cohort, and were transcriptionally regulated by epigenetic modification, including DNA methylation and histone methylation modification.
Journal ArticleDOI
Assessing Concordance of Drug-Induced Transcriptional Response in Rodent Liver and Cultured Hepatocytes.
TL;DR: The baseline state of untreated cultured cells relative to untreated rat liver shows striking similarity with toxicant-exposed cells in vivo, indicating that gross systems level perturbation in the underlying networks in culture may contribute to the low concordance.
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Optimal rates for community estimation in the weighted stochastic block model
Min Xu,Varun Jog,Po-Ling Loh +2 more
TL;DR: In this article, the optimal rate of misclustering error of the weighted SBM in terms of the Renyi divergence of order 1/2 between the weight distributions of within-community and between-community edges is characterized.
References
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