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75
A Bayesian mixture model for differential gene expression
 Journal of the Royal Statistical Society C
, 2005
"... We propose modelbased inference for differential gene expression, using a nonparametric Bayesian probability model for the distribution of gene intensities under different conditions. The probability model is essentially a mixture of normals. The resulting inference is similar to the empirical Bay ..."
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Cited by 57 (5 self)
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We propose modelbased inference for differential gene expression, using a nonparametric Bayesian probability model for the distribution of gene intensities under different conditions. The probability model is essentially a mixture of normals. The resulting inference is similar to the empirical Bayes approach proposed in Efron et al. (2001). The use of fully modelbased inference mitigates some of the necessary limitations of the empirical Bayes method. However, the increased generality of our method comes at a price. Computation is not as straightforward as in the empirical Bayes scheme. But we argue that inference is no more difficult than posterior simulation in traditional nonparametric mixture of normal models. We illustrate the proposed method in two examples, including a simulation study and a microarray experiment to screen for genes with differential expression in colon cancer versus normal tissue (Alon et al., 1999).
Sample size for fdrcontrol in microarray data analysis
 Bioinformatics
, 2005
"... We consider identifying differentially expressing genes between two patient groups using microarray experiment. We propose a sample size calculation method for a specified number of true rejections while controlling the false discovery rate at a desired level. Input parameters for the sample size c ..."
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Cited by 34 (2 self)
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We consider identifying differentially expressing genes between two patient groups using microarray experiment. We propose a sample size calculation method for a specified number of true rejections while controlling the false discovery rate at a desired level. Input parameters for the sample size calculation include the allocation proportion in each group, the number of genes in each array, the number of differentially expressing genes, and the effect sizes among the differentially expressing genes. We have a closedform sample size formula if the projected effect sizes are equal among differentially expressing genes. Otherwise, our method requires a numerical method to solve an equation. Simulation studies are conducted to show that the calculated sample sizes are accurate in practical settings. The proposed method is demonstrated with a real study. Key words: Block compound symmetry, Familywise error rate, Prognostic gene, True rejection, Twosample ttest.
Hybrid Dirichlet mixture models for functional data
"... Summary. In functional data analysis, curves or surfaces are observed, up to measurement error, at a finite set of locations, for, say, a sample of n individuals. Often, the curves are homogeneous, except perhaps for individualspecific regions that provide heterogeneous behaviour (e.g. ‘damaged ’ a ..."
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Cited by 17 (0 self)
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Summary. In functional data analysis, curves or surfaces are observed, up to measurement error, at a finite set of locations, for, say, a sample of n individuals. Often, the curves are homogeneous, except perhaps for individualspecific regions that provide heterogeneous behaviour (e.g. ‘damaged ’ areas of irregular shape on an otherwise smooth surface). Motivated by applications with functional data of this nature, we propose a Bayesian mixture model, with the aim of dimension reduction, by representing the sample of n curves through a smaller set of canonical curves. We propose a novel prior on the space of probability measures for a random curve which extends the popular Dirichlet priors by allowing local clustering: nonhomogeneous portions of a curve can be allocated to different clusters and the n individual curves can be represented as recombinations (hybrids) of a few canonical curves. More precisely, the prior proposed envisions a conceptual hidden factor with klevels that acts locally on each curve. We discuss several models incorporating this prior and illustrate its performance with simulated and real data sets. We examine theoretical properties of the proposed finite hybrid Dirichlet mixtures, specifically, their behaviour as the number of the mixture components goes to 1 and their connection with Dirichlet process mixtures.
Supplement to “Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomics data
, 2010
"... Image data are increasingly encountered and are of growing importance in many areas of science. Much of these data are quantitative image data, which are characterized by intensities that represent some measurement of interest in the scanned images. The data typically consist of multiple images on t ..."
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Cited by 8 (3 self)
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Image data are increasingly encountered and are of growing importance in many areas of science. Much of these data are quantitative image data, which are characterized by intensities that represent some measurement of interest in the scanned images. The data typically consist of multiple images on the same domain and the goal of the research is to combine the quantitative information across images to make inference about populations or interventions. In this paper we present a unified analysis framework for the analysis of quantitative image data using a Bayesian functional mixed model approach. This framework is flexible enough to handle complex, irregular images with many local features, and can model the simultaneous effects of multiple factors on the image intensities and account for the correlation between images induced by the design. We introduce a general isomorphic modeling approach to fitting the functional mixed model, of which the waveletbased functional mixed model is one special case. With suitable modeling choices, this approach leads to efficient calculations and can result in flexible
PowerEnhanced Multiple Decision Functions Controlling FamilyWise Error and False Discovery Rates
, 2009
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Sample size for gene expression microarray experiments
 Bioinformatics
, 2005
"... doi:10.1093/bioinformatics/bti162 ..."
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Microarray analysis of gene expression: considerations in data mining and statistical treatment. Physiol Genomics
"... Invited ReviewR1 DNA microarray represents a powerful tool in biomedical discoveries. Harnessing the potential of this technology depends on the development and appropriate use of data mining and statistical tools. Significant current advances have made microarray data mining more versatile. Resear ..."
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Cited by 7 (0 self)
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Invited ReviewR1 DNA microarray represents a powerful tool in biomedical discoveries. Harnessing the potential of this technology depends on the development and appropriate use of data mining and statistical tools. Significant current advances have made microarray data mining more versatile. Researchers are no longer limited to default choices that generate suboptimal results. Conflicting results in repeated experiments can be resolved through attention to the statistical details. In the current dynamic environment, there are many choices and potential pitfalls for researchers who intend to incorporate microarrays as a research tool. This review is intended to provide a simple framework to understand the choices and identify the pitfalls. Specifically, this review article discusses the choice of microarray platform, preprocessing raw data, differential expression and validation, clustering, annotation and functional characterization of genes, and pathway construction in light of emergent concepts and tools.
Semiparametric Differential Expression Analysis via Partial Mixture Estimation
, 2007
"... We develop an approach for microarray differential expression analysis, i.e. identifying genes whose expression levels differ between two or more groups. Current approaches to inference rely either on full parametric assumptions or on permutationbased techniques for sampling under the null distribu ..."
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Cited by 6 (1 self)
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We develop an approach for microarray differential expression analysis, i.e. identifying genes whose expression levels differ between two or more groups. Current approaches to inference rely either on full parametric assumptions or on permutationbased techniques for sampling under the null distribution. In some situations, however, a full parametric model cannot be justified, or the sample size per group is too small for permutation methods to be valid. We propose a semiparametric framework based on partial mixture estimation which only requires a parametric assumption for the null (equally expressed) distribution and can handle small sample sizes where permutation methods break down. We develop two novel improvements of Scott’s minimum integrated square error criterion for partial mixture estimation [Scott, 2004a,b]. As a side benefit, we obtain interpretable and closedform estimates for the proportion of EE genes. PseudoBayesian and frequentist procedures for controlling the false discovery rate are given. Results from simulations and real datasets indicate that our approach can provide substantial 1 1