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
Molecular insight into cotton leaf curl geminivirus disease resistance in cultivated cotton (Gossypium hirsutum).
Syed Shan-e-Ali Zaidi,Syed Shan-e-Ali Zaidi,Syed Shan-e-Ali Zaidi,Rubab Zahra Naqvi,Rubab Zahra Naqvi,Muhammad Asif,Susan R. Strickler,Sara Shakir,Sara Shakir,Sara Shakir,Muhammad Shafiq,Abdul Manan Khan,Imran Amin,Bharat Mishra,M. Shahid Mukhtar,Brian E. Scheffler,Jodi A. Scheffler,Lukas A. Mueller,Shahid Mansoor +18 more
TL;DR: The results indicated that replication of pathogenicity determinant betasatellite is significantly attenuated in Mac7 and probably responsible for resistance phenotype, which has important implications in understanding CLCuD resistance mechanism and developing a durable resistance in cultivated cotton.
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Omics and the Search for Blood Biomarkers in Chronic Obstructive Pulmonary Disease. Insights from COPDGene.
Elizabeth A. Regan,Craig P. Hersh,Peter J. Castaldi,Dawn L. DeMeo,Edwin K. Silverman,James D. Crapo,Russell P. Bowler +6 more
TL;DR: COPDGene has been a useful resource in the identification and validation of multiple biomarkers for COPD, including DNA methylation, transcriptomic, proteomic, and metabolomic blood biomarkers.
Journal ArticleDOI
Evolutionary Conservation and Divergence of Gene Coexpression Networks in Gossypium (Cotton) Seeds
Guanjing Hu,Ran Hovav,Corrinne E. Grover,Adi Faigenboim-Doron,Noa Kadmon,Justin T. Page,Joshua A. Udall,Jonathan F. Wendel +7 more
TL;DR: This work demonstrates how network inference informs the understanding of the transcriptomic architecture of phenotypic variation associated with temporal scales ranging from thousands (domestication) to millions (speciation) of years, and by polyploidy.
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Single-Cell Transcriptomics Reveals Spatial and Temporal Turnover of Keratinocyte Differentiation Regulators.
Alex I Finnegan,Raymond J. Cho,Alan Luu,Paymann Harirchian,Paymann Harirchian,Jerry Lee,Jerry Lee,Jeffrey B. Cheng,Jeffrey B. Cheng,Jun S. Song +9 more
TL;DR: Recovering single-cell RNA-seq data from 22,338 human foreskin keratinocytes is utilizes to reconstruct the transcriptional regulation of skin development and homeostasis genes, organizing them by differentiation stage and also into transcription factor (TF)–associated modules.
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Characterization of transcriptional modules related to fibrosing-NAFLD progression
TL;DR: Findings highlighted a module and affiliated genes as playing important roles in the regulation of fibrosis in NAFLD, which may point to potential targets for therapeutic interventions.
References
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