Journal ArticleDOI
A General Framework for Weighted Gene Co-Expression Network Analysis
Bin Zhang,Steve Horvath +1 more
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
Toll-like receptor 4 in glial inflammatory responses to air pollution in vitro and in vivo.
Nicholas C. Woodward,Morgan C. Levine,Amin Haghani,Farimah Shirmohammadi,Arian Saffari,Constantinos Sioutas,Todd E. Morgan,Caleb E. Finch +7 more
TL;DR: Results show TLR4 activation is integral in brain inflammatory responses to air pollution, and warrant further study ofTLR4 in accelerated cognitive aging by air pollution.
Journal ArticleDOI
Gene coexpression network topology of cardiac development, hypertrophy, and failure.
Frederick E. Dewey,Marco V Perez,Matthew T. Wheeler,Clifton Watt,Joshua M. Spin,Peter Langfelder,Stephen Horvath,Sridhar Hannenhalli,Thomas P. Cappola,Euan A. Ashley +9 more
TL;DR: Although the analysis did not find evidence for a global coordinated program of fetal gene expression in adult myocardial adaptation, the analysis revealed specific gene expression modules active during both development and disease and specific candidates for their regulation.
Journal ArticleDOI
Unified QSAR and network-based computational chemistry approach to antimicrobials, part 1: multispecies activity models for antifungals.
TL;DR: This work developed a multispecies QSAR classification model, in which the outputs were the inputs of the aforementioned network, and represented it as a large network, which may be used to identify drugs with similar mechanism of action.
Journal ArticleDOI
The Pattern of Cortical Dysfunction in a Mouse Model of a Schizophrenia-Related Microdeletion
Karine Fénelon,Bin Xu,Cora Sau Wan Lai,Jun Mukai,Sander Markx,Kimberly L. Stark,Pei Ken Hsu,Wen-Biao Gan,Gerald D. Fischbach,Amy B. MacDermott,Maria Karayiorgou,Joseph A. Gogos +11 more
TL;DR: Reduction in Dgcr8 levels appears to be a major driver of altered short-term synaptic plasticity in prefrontal cortex and working memory but not of long-term plasticity and cytoarchitecture, and provides insight into the link between micro-RNA dysregulation and genetic liability to schizophrenia and cognitive dysfunction.
Journal ArticleDOI
Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes.
Eric E. Schadt,Pek Yee Lum +1 more
TL;DR: Several integrative approaches that involve integrating genetic, expression, and clinical data to elucidate networks underlying disease can lead to defining pathways underlying disease more objectively and to identifying biomarkers and more-robust points for therapeutic intervention.
References
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