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
Citations
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Journal ArticleDOI
A global meta-analysis of microarray expression data to predict unknown gene functions and estimate the literature-data divide
TL;DR: A global meta-analysis of all publicly available GEO two-channel human microarray datasets was conducted to identify genes with recurrent, reproducible patterns of co-regulation across different conditions, finding genes co-expressed in parallel with the query gene were frequently associated with the same GO categories, whereas anti-parallel genes were not.
Journal ArticleDOI
Network analysis reveals seasonal variation of co-occurrence correlations between Cyanobacteria and other bacterioplankton.
TL;DR: High-throughput sequencing was employed to investigate the seasonal variations in the composition of bacterioplankton communities in six eutrophic urban lakes of Nanjing City, China and revealed that Cyanobacteria were dominant in summer which may result from strong co-occurrence patterns and suitable living conditions.
Journal ArticleDOI
Comprehensive transcriptome analysis of neocortical layers in humans, chimpanzees and macaques
Zhisong He,Dingding Han,Dingding Han,Olga Efimova,Patricia Guijarro,Qianhui Yu,Qianhui Yu,Anna Oleksiak,Shasha Jiang,Konstantin V. Anokhin,Boris M. Velichkovsky,Stefan Grünewald,Philipp Khaitovich +12 more
TL;DR: Despite apparent histological conservation, human neocortical organization has undergone substantial changes affecting more than 5% of its transcriptome, suggesting acceleration of cortical reorganization on the human evolutionary lineage.
Journal ArticleDOI
Computational prediction of cancer-gene function
TL;DR: How can modern computational and statistical methods be used to reliably deduce the function(s) of poorly characterized cancer genes from the newly available genomic and proteomic datasets?
Journal ArticleDOI
RNA Sequencing of Mouse Sinoatrial Node Reveals an Upstream Regulatory Role for Islet-1 in Cardiac Pacemaker Cells
TL;DR: The PC transcriptome diverges sharply from other cardiomyocytes and is a positive transcriptional regulator of the PC gene expression program, and RNA sequencing on sinoatrial node tissue lacking Islet-1 established that Is let-1 is an important transcriptional regulators within the developing sino atrial node.
References
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Journal ArticleDOI
Emergence of Scaling in Random Networks
TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Journal ArticleDOI
Statistical mechanics of complex networks
TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
Journal ArticleDOI
Cluster analysis and display of genome-wide expression patterns
TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
Book
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.
Journal ArticleDOI
R: A Language for Data Analysis and Graphics
Ross Ihaka,Robert Gentleman +1 more
TL;DR: In this article, the authors discuss their experience designing and implementing a statistical computing language, which combines what they felt were useful features from two existing computer languages, and they feel that the new language provides advantages in the areas of portability, computational efficiency, memory management, and scope.
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