title: Models for microarray gene expression data.
abstract: This paper describes a general methodology for the analysis of differential gene expression based on microarray data. First, we characterize the data by a linear statistical model that accounts for relevant sources of variation in the data and then we consider estimation of the model parameters. Because microarray studies typically involve thousands of genes, we propose a two-stage method for parameter estimation. The interaction terms for genes and experimental conditions in this model capture all relevant information about differential gene expression in the microarray data. We propose a mixture distribution model for a summary statistic of differential expression that consists of null and alternative component distributions. The mixture model suggests two methods for identifying genes exhibiting differential expression. One is a frequentist method that identifies distinguished genes and the other an empirical Bayes procedure that yields estimated posterior probabilities of differential expression, conditional on observed microarray readings.
authors.affiliation: Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115-5804, USA. stmei@channing.harvard.edu
authors.forename: Mei-Ling Ting
authors.initials: ML
authors.lastname: Lee
authors.forename: Weining
authors.initials: W
authors.lastname: Lu
authors.forename: G A
authors.initials: GA
authors.lastname: Whitmore
authors.forename: David
authors.initials: D
authors.lastname: Beier
mesh_terms: D000818:Animals; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data; D051379:Mice; D008817:Mice, Mutant Strains; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000706:statistics & numerical data; D007690:Polycystic Kidney Diseases / Q000235:genetics