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Limma: linear models for microarray data
 Bioinformatics and Computational Biology Solutions using R and Bioconductor
, 2005
"... This free opensource software implements academic research by the authors and coworkers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents ..."
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Cited by 758 (13 self)
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This free opensource software implements academic research by the authors and coworkers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents
NCBI GEO: archive for functional genomics data sets–10 years on
 Nucleic Acids Res
, 2011
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A Shrinkage Approach to LargeScale Covariance Matrix Estimation and Implications for Functional Genomics
, 2005
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Use of withinarray replicate spots for assessing differential expression in microarray experiments
 Bioinformatics
, 2005
"... Motivation. Spotted arrays are often printed with probes in duplicate or triplicate, but current methods for assessing differential expression are not able to make full use of the resulting information. Usual practice is to average the duplicate or triplicate results for each probe before assessing ..."
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Cited by 233 (8 self)
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Motivation. Spotted arrays are often printed with probes in duplicate or triplicate, but current methods for assessing differential expression are not able to make full use of the resulting information. Usual practice is to average the duplicate or triplicate results for each probe before assessing differential expression. This loses valuable information about genewise variability. Results. A method is proposed for extracting more information from withinarray replicate spots in microarray experiments by estimating the strength of the correlation between them. The method involves fitting separate linear models to the expression data for each gene but with a common value for the betweenreplicate correlation. The method greatly improves the precision with which the genewise variances are estimated and thereby improves inference methods designed to identify differentially expressed genes. The method may be combined with empirical Bayes methods for moderating the genewise variances between genes. The method is validated using data from a microarray experiment involving calibration and ratio control spots in conjunction with spikedin RNA. Comparing results for calibration and ratio control spots shows that the common correlation method results in substantially better discrimination of differentially expressed genes from those which are not. The spikein experiment also confirms that the results may be further improved by empirical Bayes smoothing of the variances when the sample size is small. Availability. The methodology is implemented in the limma software package for R, available from the CRAN repository
On testing the significance of sets of genes
 Annals of Applied Statistics
"... This paper discusses the problem of identifying differentially expressed groups of genes from a microarray experiment. The groups of genes are externally defined, for example, sets of gene pathways derived from biological databases. Our starting point is the interesting Gene Set Enrichment Analysis ..."
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Cited by 164 (3 self)
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This paper discusses the problem of identifying differentially expressed groups of genes from a microarray experiment. The groups of genes are externally defined, for example, sets of gene pathways derived from biological databases. Our starting point is the interesting Gene Set Enrichment Analysis (GSEA) procedure of Subramanian et al. (2005). We study the problem in some generality and propose two potential improvements to GSEA: the maxmean statistic for summarizing genesets, and restandardization for more accurate inferences. We discuss a variety of examples and extensions, including the use of geneset scores for class predictions. We also describe a new R language package GSA that implements our ideas. 1
Moderated Statistical Tests for Assessing Differences in Tag Abundance
"... Motivation: Digital gene expression (DGE) technologies measure gene expression by counting sequence tags. They are sensitive technologiesfor measuring gene expression on agenomic scale, without the need for prior knowledge of the genome sequence. As the cost of sequencing DNA decreases, the number o ..."
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Cited by 140 (7 self)
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Motivation: Digital gene expression (DGE) technologies measure gene expression by counting sequence tags. They are sensitive technologiesfor measuring gene expression on agenomic scale, without the need for prior knowledge of the genome sequence. As the cost of sequencing DNA decreases, the number of DGE datasets is expected to grow dramatically. Various tests of differential expression have been proposed for replicated DGE data using binomial, Poisson, negative binomial or pseudolikelihood (PL) models for the counts, but none of the these are usable when the number of replicates is very small. Results: We develop tests using the negative binomial distribution to model overdispersion relative to the Poisson, and use conditional weighted likelihood to moderate the level of overdispersion across genes. Not only is our strategy applicable even with the smallest number of libraries, but it also proves to be more powerful than previous strategies when more libraries are available. The methodology is equally applicable to other counting technologies, such as proteomic spectral counts. Availability: An R package and supplementary materials can be accessed from
A Hilbert space embedding for distributions
 In Algorithmic Learning Theory: 18th International Conference
, 2007
"... Abstract. We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a reproducing kernel Hilbert space. Applications of this technique can be found in twosample tests, which are used for ..."
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Cited by 110 (44 self)
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Abstract. We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a reproducing kernel Hilbert space. Applications of this technique can be found in twosample tests, which are used for determining whether two sets of observations arise from the same distribution, covariate shift correction, local learning, measures of independence, and density estimation. Kernel methods are widely used in supervised learning [1, 2, 3, 4], however they are much less established in the areas of testing, estimation, and analysis of probability distributions, where information theoretic approaches [5, 6] have long been dominant. Recent examples include [7] in the context of construction of graphical models, [8] in the context of feature extraction, and [9] in the context of independent component analysis. These methods have by and large a common issue: to compute quantities such as the mutual information, entropy, or KullbackLeibler divergence, we require sophisticated space partitioning and/or
Modelbased Variancestabilizing Transformation for Illumina Microarray Data’, Nucleic Acids Res
, 2008
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Microarrays, empirical Bayes and the twogroups model
 STATIST. SCI
, 2006
"... The classic frequentist theory of hypothesis testing developed by Neyman, Pearson, and Fisher has a claim to being the Twentieth Century’s most influential piece of applied mathematics. Something new is happening in the TwentyFirst Century: high throughput devices, such as microarrays, routinely re ..."
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Cited by 73 (10 self)
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The classic frequentist theory of hypothesis testing developed by Neyman, Pearson, and Fisher has a claim to being the Twentieth Century’s most influential piece of applied mathematics. Something new is happening in the TwentyFirst Century: high throughput devices, such as microarrays, routinely require simultaneous hypothesis tests for thousands of individual cases, not at all what the classical theory had in mind. In these situations empirical Bayes information begins to force itself upon frequentists and Bayesians alike. The twogroups model is a simple Bayesian construction that facilitates empirical Bayes analysis. This article concerns the interplay of Bayesian and frequentist ideas in the twogroups setting, with particular attention focussed on Benjamini and Hochberg’s False Discovery Rate method. Topics include the choice and meaning of the null hypothesis in largescale testing situations, power considerations, the limitations of permutation methods, significance testing for groups of cases (such as pathways in microarray studies), correlation effects, multiple confidence intervals, and Bayesian competitors to the twogroups model.
A multivariate empirical bayes statistic for replicated microarray time course data
 Annals of Statistics
, 2006
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