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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Unifying immunology with informatics and multiscale biology
TL;DR: Some of the computational analysis tools for high-dimensional data and how they can be applied to immunology are reviewed.
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Genome-wide, integrative analysis implicates microRNA dysregulation in autism spectrum disorder
TL;DR: Findings support a role for miRNA dysregulation in ASD pathophysiology and provide a rich data set and framework for future analyses of miRNAs in neuropsychiatric diseases.
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A regulatory network-based approach dissects late maturation processes related to the acquisition of desiccation tolerance and longevity of Medicago truncatula seeds.
Jerome Verdier,David Lalanne,Sandra Pelletier,Ivone Torres-Jerez,Karima Righetti,Kaustav Bandyopadhyay,Olivier Leprince,Emilie Chatelain,Benoit Ly Vu,Jérôme Gouzy,Pascal Gamas,Michael K. Udvardi,Julia Buitink +12 more
TL;DR: This study captures the coordinated regulation of seed maturation and identifies distinct regulatory networks underlying the preparation for the dry and quiescent states.
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Gene Networks and microRNAs Implicated in Aggressive Prostate Cancer
Liang Wang,Hui Tang,Venugopal Thayanithy,Subbaya Subramanian,Ann L. Oberg,Julie M. Cunningham,James R. Cerhan,Clifford J. Steer,Stephen N. Thibodeau +8 more
TL;DR: It is suggested that cell cycle is likely to be a molecular pathway causing aggressive phenotype of prostate cancer and further characterization of cell cycle-related genes (particularly, the hub genes) and miRNAs that regulate these hub genes could facilitate identification of candidate genes responsible for the aggressive phenotype.
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Differences in human and chimpanzee gene expression patterns define an evolving network of transcription factors in brain
TL;DR: It is suggested that concerted changes in a relatively small number of interacting TFs may coordinate major gene expression differences in human and chimpanzee brain.
References
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