Journal ArticleDOI
A General Framework for Weighted Gene Co-Expression Network Analysis
Bin Zhang,Steve Horvath +1 more
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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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Behavioral and neurogenomic transcriptome changes in wild-derived zebrafish with fluoxetine treatment
TL;DR: It is demonstrated that fluoxetine reduces anxiety-related behaviors in wild-derived zebrafish and alters their neurogenomic state and two biological processes, lipid and amino acid metabolic synthesis that characterize differences in the fluoxettine treated fish are identified.
Journal ArticleDOI
A proteomic network approach across the ALS-FTD disease spectrum resolves clinical phenotypes and genetic vulnerability in human brain.
Mfon E. Umoh,Eric B. Dammer,Jingting Dai,Duc M. Duong,James J. Lah,Allan I. Levey,Marla Gearing,Jonathan D. Glass,Nicholas T. Seyfried +8 more
TL;DR: A module enriched with astrocyte and microglia proteins was significantly increased in ALS cases carrying the C9orf72 mutation compared to sporadic ALS cases, suggesting that the genetic expansion is associated with inflammation in the brain even without clinical evidence of dementia.
Journal ArticleDOI
Positively correlated miRNA-mRNA regulatory networks in mouse frontal cortex during early stages of alcohol dependence
Yury O. Nunez,Jay M. Truitt,Giorgio Gorini,Olga Ponomareva,Yuri A. Blednov,R. Adron Harris,R. Dayne Mayfield +6 more
TL;DR: This study tested the hypotheses that ethanol consumption induces changes in miRNA-mRNA interaction networks in the mouse frontal cortex and discovered a conserved signature of changing miRNAs between ethanol-treated mice and human alcoholics, which provides a valuable tool for future biomarker/diagnostic studies in humans.
Journal ArticleDOI
The organization of the transcriptional network in specific neuronal classes.
Kellen D. Winden,Michael C. Oldham,Karoly Mirnics,Philip J. Ebert,Christo H. Swan,Pat Levitt,John L.R. Rubenstein,Steve Horvath,Daniel H. Geschwind +8 more
TL;DR: This work performs the first systems‐level analysis of microarray data from single neuronal populations using weighted gene co‐expression network analysis to examine how neuronal transcriptome organization relates to neuronal function and diversity.
Journal ArticleDOI
Global Cross-Talk of Genes of the Mosquito Aedes aegypti in Response to Dengue Virus Infection
Susanta K. Behura,Consuelo Gomez-Machorro,Brent W. Harker,Becky deBruyn,Diane D. Lovin,Ryan R. Hemme,Akio Mori,Jeanne Romero-Severson,David W. Severson +8 more
TL;DR: The data revealed extensive transcriptional networks of mosquito genes that are expressed in modular manners in response to DENV infection, and indicated that successfully defending against viral infection requires more elaborate gene networks than hosting the virus.
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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