title: Using a calibration experiment to assess gene-specific information: full Bayesian and empirical Bayesian models for two-channel microarray data.
abstract: BACKGROUND
  Microarray studies permit to quantify expression levels on a global scale by measuring transcript abundance of thousands of genes simultaneously. A difficulty when analysing expression measures is how to model variability for the whole set of genes. It is usually unrealistic to assume a common variance for each gene. Several approaches to model gene-specific variances are proposed. We take advantage of calibration experiments, in which the probes hybridized on the two channels come from the same population (self-self experiment). In this case it is possible to estimate the gene-specific variance, to be incorporated in comparative experiments on the same tissue, cellular line or species.
  
  
  RESULTS
  We present two approaches to introduce prior information on gene-specific variability from a calibration experiment: an empirical Bayes model and a full Bayesian hierarchical model. We apply the methods in the analysis of human lipopolysaccharide-stimulated leukocyte experiments.
  
  
  BACKGROUND
  The calculations are implemented in WinBugs. The codes are available on request from the authors.
authors.affiliation: Department of Statistics G. Parenti, University of Florence & Biostatistic Unit CSPO, Florence, Italy. m.blangiardo@imperial.ac.uk
authors.forename: Marta
authors.initials: M
authors.lastname: Blangiardo
authors.forename: Simona
authors.initials: S
authors.lastname: Toti
authors.forename: Betti
authors.initials: B
authors.lastname: Giusti
authors.forename: Rosanna
authors.initials: R
authors.lastname: Abbate
authors.forename: Alberto
authors.initials: A
authors.lastname: Magi
authors.forename: Filippo
authors.initials: F
authors.lastname: Poggi
authors.forename: Luciana
authors.initials: L
authors.lastname: Rossi
authors.forename: Francesca
authors.initials: F
authors.lastname: Torricelli
authors.forename: Annibale
authors.initials: A
authors.lastname: Biggeri
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D001499:Bayes Theorem; D002138:Calibration; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D015536:Down-Regulation; D005069:Evaluation Studies as Topic; D005786:Gene Expression Regulation; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007962:Leukocytes / Q000378:metabolism; D007963:Leukocytes, Mononuclear / Q000166:cytology; D008070:Lipopolysaccharides / Q000737:chemistry / Q000378:metabolism; D015233:Models, Statistical; D009693:Nucleic Acid Hybridization; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012107:Research Design; D012680:Sensitivity and Specificity; D014894:Weights and Measures
chemical_list: D008070:Lipopolysaccharides