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Empirical Bayes Estimation of a Sparse Vector of Gene Expression Changes
-
Stephen Erickson
Veröffentlicht/Copyright:
6. September 2005
Gene microarray technology is often used to compare the expression of thousand of genes in two different cell lines. Typically, one does not expect measurable changes in transcription amounts for a large number of genes; furthermore, the noise level of array experiments is rather high in relation to the available number of replicates. For the purpose of statistical analysis, inference on the ``population'' difference in expression for genes across the two cell lines is often cast in the framework of hypothesis testing, with the null hypothesis being no change in expression. Given that thousands of genes are investigated at the same time, this requires some multiple comparison correction procedure to be in place. We argue that hypothesis testing, with its emphasis on type I error and family analogues, may not address the exploratory nature of most microarray experiments. We instead propose viewing the problem as one of estimation of a vector known to have a large number of zero components. In a Bayesian framework, we describe the prior knowledge on expression changes using mixture priors that incorporate a mass at zero, and we choose a loss function that favors the selection of sparse solutions. We consider two different models applicable to the microarray problem, depending on the nature of replicates available, and show how to explore the posterior distributions of the parameters using MCMC. Simulations show an interesting connection between this Bayesian estimation framework and false discovery rate (FDR) control. Finally, two empirical examples illustrate the practical advantages of this Bayesian estimation paradigm.
Published Online: 2005-9-6
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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- Hierarchical Inverse Gaussian Models and Multiple Testing: Application to Gene Expression Data
- FADO: A Statistical Method to Detect Favored or Avoided Distances between Occurrences of Motifs using the Hawkes' Model
- Prediction of Genomewide Conserved Epitope Profiles of HIV-1: Classifier Choice and Peptide Representation
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- Test on the Structure of Biological Sequences via Chaos Game Representation
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- Empirical Bayes and Resampling Based Multiple Testing Procedure Controlling Tail Probability of the Proportion of False Positives.
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- A Probabilistic Approach to Large-Scale Association Scans: A Semi-Bayesian Method to Detect Disease-Predisposing Alleles
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Artikel in diesem Heft
- Article
- Estimating Motifs Under Order Restrictions
- Reproducible Research: A Bioinformatics Case Study
- Generalized Rank Tests for Replicated Microarray Data
- Stepwise Normalization of Two-Channel Spotted Microarrays
- Comparing Automatic and Manual Image Processing in FLARE Assay Analysis for Colon Carcinogenesis
- Pixel-level Signal Modelling with Spatial Correlation for Two-Colour Microarrays
- Empirical Bayes Microarray ANOVA and Grouping Cell Lines by Equal Expression Levels
- Multiple Testing and Data Adaptive Regression: An Application to HIV-1 Sequence Data.
- Early Diagnostic Marker Panel Determination for Microarray Based Clinical Studies
- Prediction of Missing Values in Microarray and Use of Mixed Models to Evaluate the Predictors
- Combined Association and Linkage Analysis for General Pedigrees and Genetic Models
- Incorporating Biological Information as a Prior in an Empirical Bayes Approach to Analyzing Microarray Data
- The Relative Inefficiency of Sequence Weights Approaches in Determining a Nucleotide Position Weight Matrix
- A Simple Loglinear Model for Haplotype Effects in a Case-Control Study Involving Two Unphased Genotypes
- Extension of the SIMLA Package for Generating Pedigrees with Complex Inheritance Patterns: Environmental Covariates, Gene-Gene and Gene-Environment Interaction
- Error Distribution for Gene Expression Data
- A General Framework for Weighted Gene Co-Expression Network Analysis
- Statistical Inference in Evolutionary Models of DNA Sequences via the EM Algorithm
- Comparing Bacterial DNA Microarray Fingerprints
- Continuous Covariates in Genetic Association Studies of Case-Parent Triads: Gene and Gene-Environment Interaction Effects, Population Stratification, and Power Analysis
- Robust Remote Homology Detection by Feature Based Profile Hidden Markov Models
- Empirical Bayes Estimation of a Sparse Vector of Gene Expression Changes
- Hierarchical Inverse Gaussian Models and Multiple Testing: Application to Gene Expression Data
- FADO: A Statistical Method to Detect Favored or Avoided Distances between Occurrences of Motifs using the Hawkes' Model
- Prediction of Genomewide Conserved Epitope Profiles of HIV-1: Classifier Choice and Peptide Representation
- Fold-Change Estimation of Differentially Expressed Genes using Mixture Mixed-Model
- Test on the Structure of Biological Sequences via Chaos Game Representation
- Reverse Engineering Galactose Regulation in Yeast through Model Selection
- Empirical Bayes and Resampling Based Multiple Testing Procedure Controlling Tail Probability of the Proportion of False Positives.
- Weighted Analysis of Paired Microarray Experiments
- A Probabilistic Approach to Large-Scale Association Scans: A Semi-Bayesian Method to Detect Disease-Predisposing Alleles
- A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics
- Structured Antedependence Models for Functional Mapping of Multiple Longitudinal Traits
- Correlation Between Gene Expression Levels and Limitations of the Empirical Bayes Methodology for Finding Differentially Expressed Genes
- Bayesian Statistical Studies of the Ramachandran Distribution
- On Reference Designs For Microarray Experiments
- Computing Asymptotic Power and Sample Size for Case-Control Genetic Association Studies in the Presence of Phenotype and/or Genotype Misclassification Errors
