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
Differences in DNA methylation between human neuronal and glial cells are concentrated in enhancers and non-CpG sites
Alexey Kozlenkov,Panos Roussos,Alisa Timashpolsky,Mihaela Barbu,Sergei Rudchenko,Marina Bibikova,Brandy Klotzle,William Byne,Rebecca Lyddon,Antonio Fabio Di Narzo,Yasmin L. Hurd,Eugene V. Koonin,Stella Dracheva +12 more
TL;DR: Neuron-type differential methylation was overrepresented in CpG island shores, enriched within gene bodies but not in intergenic regions, and preferentially harbored binding motifs for a distinct set of transcription factors, including neuron-specific activity-dependent factors.
Journal ArticleDOI
Mirna expression profiles identify drivers in colorectal and pancreatic cancers.
Ada Piepoli,Francesca Tavano,Massimiliano Copetti,Tommaso Mazza,Orazio Palumbo,Anna Panza,Francesco Fabio di Mola,Valerio Pazienza,Gianluigi Mazzoccoli,Giuseppe Biscaglia,Annamaria Gentile,Nicola Mastrodonato,Massimo Carella,Fabio Pellegrini,Pierluigi Di Sebastiano,Angelo Andriulli +15 more
TL;DR: MiRNA expression profiles may identify cancer-specific signatures and potentially useful biomarkers for the diagnosis of tissue specific cancers and miRNA-network analysis help identify altered miRNA regulatory networks that could play a role in tumor pathogenesis.
Journal ArticleDOI
Replicable and Coupled Changes in Innate and Adaptive Immune Gene Expression in Two Case-Control Studies of Blood Microarrays in Major Depressive Disorder.
Leday Ggr,Petra E. Vértes,Sylvia Richardson,Greene,Tim Regan,Shahid M. Khan,Henderson R,Tom C. Freeman,Carmine M. Pariante,Neil A. Harrison,Perry Vh,Wayne C. Drevets,Gayle M. Wittenberg,E.T. Bullmore +13 more
TL;DR: MDD was replicably associated with proinflammatory activation of the peripheral innate immune system, coupled with relative inactivation of the adaptiveimmune system, indicating the potential of transcriptional biomarkers for immunological stratification of patients with depression.
Journal ArticleDOI
Modeling Rett Syndrome Using TALEN-Edited MECP2 Mutant Cynomolgus Monkeys
Yongchang Chen,Yongchang Chen,Juehua Yu,Yuyu Niu,Yuyu Niu,Dongdong Qin,Hailiang Liu,Gang Li,Yingzhou Hu,Jiaojian Wang,Yi Lu,Yu Kang,Yu Kang,Yong Jiang,Kunhua Wu,Siguang Li,Jingkuan Wei,Jingkuan Wei,Jing He,Jing He,Junbang Wang,Xiaojing Liu,Yuping Luo,Chenyang Si,Chenyang Si,Raoxian Bai,Raoxian Bai,Kunshan Zhang,Jie Liu,Shaoyong Huang,Shaoyong Huang,Zhenzhen Chen,Zhenzhen Chen,Shuang Wang,Shuang Wang,Xiaoying Chen,Xinhua Bao,Qingping Zhang,Fuxing Li,Rui Geng,Aibin Liang,Dinggang Shen,Tianzi Jiang,Tianzi Jiang,Xintian Hu,Yuanye Ma,Yuanye Ma,Weizhi Ji,Weizhi Ji,Yi Eve Sun,Yi Eve Sun +50 more
TL;DR: Detailed genotypes and phenotypes of TALEN-edited MECP2 mutant cynomolgus monkeys serving as a model for a neurodevelopmental disorder, Rett syndrome (RTT), are reported, suggesting that gene-edited RTT founder monkeys would be of value for disease mechanistic studies as well as development of potential therapeutic interventions for RTT.
Journal ArticleDOI
Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases
Manikandan Narayanan,Jimmy L. Huynh,Kai Wang,Xia Yang,Seungyeul Yoo,Joshua McElwee,Bin Zhang,Chunsheng Zhang,John Lamb,Tao Xie,Christine Suver,Cliona Molony,Stacey Melquist,Andrew D. Johnson,Guoping Fan,David J. Stone,Eric E. Schadt,Patrizia Casaccia,Valur Emilsson,Jun Zhu +19 more
TL;DR: Using expression profiles from postmortem prefrontal cortex samples of 624 dementia patients and non‐demented controls, this work identified a 242‐gene subnetwork enriched for independent AD/HD signatures, which revealed a surprising dichotomy of gained/lost correlations among two inter‐connected processes, chromatin organization and neural differentiation.
References
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