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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Single-Cell Analysis in Cancer Genomics.
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A network module-based method for identifying cancer prognostic signatures
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Single cell transcriptome analysis of human, marmoset and mouse embryos reveals common and divergent features of preimplantation development.
Thorsten Boroviak,Giuliano Giuseppe Stirparo,Sabine Dietmann,Irene Hernando-Herraez,Hisham Mohammed,Wolf Reik,Austin Smith,Erika Sasaki,Jennifer Nichols,Paul Bertone +9 more
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Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms.
Rebecca L. Walker,Gokul Ramaswami,Christopher Hartl,Nicholas Mancuso,Michael J. Gandal,Luis de la Torre-Ubieta,Bogdan Pasaniuc,Jason L. Stein,Daniel H. Geschwind +8 more
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Weighted gene co-expression network analysis of the peripheral blood from Amyotrophic Lateral Sclerosis patients
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References
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