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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Mouse Genome-Wide Association and Systems Genetics Identify Asxl2 As a Regulator of Bone Mineral Density and Osteoclastogenesis
Charles R. Farber,Brian J. Bennett,Luz D. Orozco,Wei Zou,Ana Lira,Emrah Kostem,Hyun Min Kang,Nicholas A. Furlotte,Ani Berberyan,Anatole Ghazalpour,Jaijam Suwanwela,Thomas A. Drake,Eleazar Eskin,Q. Tian Wang,Steven L. Teitelbaum,Aldons J. Lusis +15 more
TL;DR: This study identifies a new regulator of BMD and osteoclastogenesis and highlights the power of GWA and systems genetics in the mouse for dissecting complex genetic traits.
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Fatty Acid Oxidation is Impaired in An Orthologous Mouse Model of Autosomal Dominant Polycystic Kidney Disease
TL;DR: It is shown that sex is a major determinant of the transcriptional profile of mouse kidneys and that some of this difference is due to genes involved in lipid metabolism, suggesting PKD could be a disease of altered cellular metabolism.
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Link Weight Prediction Using Supervised Learning Methods and Its Application to Yelp Layered Network
TL;DR: This paper proposed a series of new centrality indices for links in line graph, and designed three supervised learning methods to realize link weight prediction both in the networks of single layer and multiple layers, which perform much better than several recently proposed baseline methods.
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Revealing differences in gene network inference algorithms on the network level by ensemble methods
Gökmen Altay,Frank Emmert-Streib +1 more
TL;DR: A statistical analysis investigating differences and similarities of four network inference algorithms, ARACNE, CLR, MRNET and RN, with respect to local network-based measures reveals the bias and provides guidance in the interpretation of inferred regulatory networks from expression data.
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Whole Genome Gene Expression Meta-Analysis of Inflammatory Bowel Disease Colon Mucosa Demonstrates Lack of Major Differences between Crohn's Disease and Ulcerative Colitis
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References
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Finding Groups in Data: An Introduction to Cluster Analysis
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