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
Biostatistical approaches for the reconstruction of gene co-expression networks based on transcriptomic data
TL;DR: Recent methodologies used for plant gene expression data are reviewed and the results, advantages and disadvantages are compared in order to help researchers in their choice of a method for the construction of GCNs.
Journal ArticleDOI
Loss of MeCP2 function is associated with distinct gene expression changes in the striatum.
TL;DR: Describing the gene expression changes in the striatum of Mecp2 mutant mice suggests that the differential expression of genes following loss of MeCP2 occurs in a tissue- or cell-type specific manner and thus Me CP2 function should be understood in a cellular context.
Journal ArticleDOI
De novo Mutations From Whole Exome Sequencing in Neurodevelopmental and Psychiatric Disorders: From Discovery to Application.
TL;DR: This review will assist researchers and clinicians in the interpretation of variants obtained from WES studies, and highlights the need to develop consensus analytical protocols and validated lists of genes appropriate for clinical laboratory analysis, in order to reach the growing demands.
Journal ArticleDOI
Comparative analysis of the drought-responsive transcriptome in soybean lines contrasting for canopy wilting.
Silvas J. Prince,Trupti Joshi,Raymond N. Mutava,Naeem H. Syed,Maldonado dos Santos Joao Vitor,Gunvant Patil,Li Song,Jiao Jiao Wang,Li Lin,Wei Chen,J. Grover Shannon,Babu Valliyodan,Dong Xu,Henry T. Nguyen +13 more
TL;DR: In this article, a whole genome transcriptome analysis was performed for leaf tissues of two contrasting soybean lines: drought-susceptible (DS) Pana and drought-tolerant (DT) PI 567690.
Posted Content
Network Structure Inference, A Survey: Motivations, Methods, and Applications
TL;DR: How network representations are constructed from underlying data, the variety of questions and tasks on these representations over several domains, and validation strategies for measuring the inferred network's capability of answering questions on the system of interest are examined.
References
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R: A Language for Data Analysis and Graphics
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