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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A protein network descriptor server and its use in studying protein, disease, metabolic and drug targeted networks
Peng Zhang,Peng Zhang,Lin Tao,Xian Zeng,Chu Qin,Shangying Chen,Feng Zhu,Zerong Li,Zerong Li,Yuyang Jiang,Weiping Chen,Yu Zong Chen +11 more
TL;DR: The usefulness of the PROFEAT computed network descriptors is illustrated by their literature-reported applications in studying the protein–protein, gene regulatory, gene co-expression, protein–drug and metabolic networks.
Journal ArticleDOI
A set of genes previously implicated in the hypoxia response might be an important modulator in the rat ear tissue response to mechanical stretch
Vishal Saxena,Vishal Saxena,Dennis P. Orgill,Dennis P. Orgill,Isaac S. Kohane,Isaac S. Kohane,Isaac S. Kohane +6 more
TL;DR: It appears that the hypoxia pathway may be an important modulator of response of soft tissue to forces, and insights are given into clinical interventions that could be designed to mimic within wounded tissue the effects of forces without all the negative effects that forces themselves create.
Journal ArticleDOI
Identification of Novel Potentially Pleiotropic Variants Associated With Osteoporosis and Obesity Using the cFDR Method.
Yuan Hu,Li-Jun Tan,Xiang-Ding Chen,Zhen Liu,Shi-Shi Min,Qin Zeng,Hui Shen,Hong-Wen Deng,Hong-Wen Deng +8 more
TL;DR: This study identified seven potentially pleiotropic genes associated with osteoporosis and obesity that may provide new insights into a potential genetic determination and codetermination mechanism of arthritis and obesity.
Journal ArticleDOI
Comparative transcriptome profiling of longissimus muscle tissues from Qianhua Mutton Merino and Small Tail Han sheep.
TL;DR: The results suggested that some DEGs, including MRFs, GXP1 and STAC3, play crucial roles in muscle growth and development processes, and genome-wide transcriptome analysis of QHMM and STH muscle is reported for the first time.
Journal ArticleDOI
Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective.
Su H. Chu,Mengna Huang,Rachel S. Kelly,Elisa Benedetti,Jalal K. Siddiqui,Oana A. Zeleznik,Alexandre C. Pereira,David M. Herrington,Craig E. Wheelock,Jan Krumsiek,Michael J. McGeachie,Steven C. Moore,Peter Kraft,Ewy Mathé,Jessica Lasky-Su +14 more
TL;DR: In this review, epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, are discussed in the context of a multi-omic investigation, and the strengths and limitations of various techniques of data integration across multi-omics data types are discussed.
References
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Finding Groups in Data: An Introduction to Cluster Analysis
TL;DR: An electrical signal transmission system, applicable to the transmission of signals from trackside hot box detector equipment for railroad locomotives and rolling stock, wherein a basic pulse train is transmitted whereof the pulses are of a selected first amplitude and represent a train axle count.
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R: A Language for Data Analysis and Graphics
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