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
Extensive rewiring of epithelial-stromal co-expression networks in breast cancer.
Eun-Yeong Oh,Eun-Yeong Oh,Stephen M. Christensen,Stephen M. Christensen,Sindhu Ghanta,Sindhu Ghanta,Jong Cheol Jeong,Jong Cheol Jeong,Octavian Bucur,Octavian Bucur,Benjamin Glass,Benjamin Glass,Laleh Montaser-Kouhsari,Laleh Montaser-Kouhsari,Nicholas W. Knoblauch,Nicholas W. Knoblauch,Nicholas Bertos,Sadiq M. Saleh,Benjamin Haibe-Kains,Benjamin Haibe-Kains,Morag Park,Andrew H. Beck,Andrew H. Beck +22 more
TL;DR: A framework for building genome-wide epithelial-stromal co- expression networks composed of pairwise co-expression relationships between mRNA levels of genes expressed in the epithelium and stroma across a population of patients is developed and a new approach for systems-level analyses of spatially localized transcriptomic data is provided.
Posted Content
In Defense of the Indefensible: A Very Naive Approach to High-Dimensional Inference
TL;DR: It is shown that under a certain set of assumptions, with high probability, the set of variables selected by the lasso is identical to the oneselected by the noiseless lasso and is hence deterministic, and the naive two-step approach can yield asymptotically valid inference.
Journal ArticleDOI
Predicting glioblastoma prognosis networks using weighted gene co-expression network analysis on TCGA data.
TL;DR: The eQCM algorithm predicted a set of gene co-expression networks which are related to glioblastoma multiforme (GBM) data obtained from the TCGA project and suggested important epigenetic events in GBM development and prognosis.
Journal ArticleDOI
Disease Severity Is Associated with Differential Gene Expression at the Early and Late Phases of Infection in Nonhuman Primates Infected with Different H5N1 Highly Pathogenic Avian Influenza Viruses
Yukiko Muramoto,Jason E. Shoemaker,Mai Quynh Le,Yasushi Itoh,Daisuke Tamura,Yuko Sakai-Tagawa,Hirotaka Imai,Ryuta Uraki,Ryo Takano,Eiryo Kawakami,Mutsumi Ito,Kiyoko Okamoto,Hirohito Ishigaki,Hitomi Mimuro,Chihiro Sasakawa,Yukiko Matsuoka,Takeshi Noda,Satoshi Fukuyama,Kazumasa Ogasawara,Hiroaki Kitano,Yoshihiro Kawaoka +20 more
TL;DR: The data suggest that the attenuated interferon-induced activation of innate immunity, apoptosis, and antigen presentation in the early phase of H5N1 virus infection leads to subsequent severe disease outcome, and provides insight into the pathogenesis of H 5N1 viruses in mammals.
Journal ArticleDOI
Long Noncoding RNA SBF2-AS1 Is Critical for Tumorigenesis of Early-Stage Lung Adenocarcinoma.
Rui Chen,Wenjia Xia,Siwei Wang,Youtao Xu,Zhifei Ma,W. Xu,Erbao Zhang,Jie Wang,Tian Fang,Quan'an Zhang,Gaochao Dong,William C. Cho,Patrick C. Ma,Giovanni Brandi,Simona Tavolari,Peter Ujhazy,Giulio Metro,Helmut Popper,Rong Yin,Mantang Qiu,Lin Xu +20 more
TL;DR: This study demonstrates that SBF2-AS1, an early-stage-specific lncRNA, promotes LUAD tumorigenesis by sponging miR-338-3p and miR -362- 3p and increasing E2F1 expression and shows that the SBF1-as1-miR- 338-3 p/362-3P-E2F 1 axis could promote LUAD tumorsigenesis in vitro and in vivo.
References
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Finding Groups in Data: An Introduction to Cluster Analysis
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