{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T15:58:19Z","timestamp":1770479899042,"version":"3.49.0"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: 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\u2013self 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.<\/jats:p>\n               <jats:p>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.<\/jats:p>\n               <jats:p>Availability: The calculations are implemented in WinBugs. The codes are available on request from the authors.<\/jats:p>\n               <jats:p>Contact: \u00a0m.blangiardo@imperial.ac.uk<\/jats:p>","DOI":"10.1093\/bioinformatics\/bti750","type":"journal-article","created":{"date-parts":[[2005,11,3]],"date-time":"2005-11-03T01:13:48Z","timestamp":1130980428000},"page":"50-57","source":"Crossref","is-referenced-by-count":5,"title":["Using a calibration experiment to assess gene-specific information: full Bayesian and empirical Bayesian models for two-channel microarray data"],"prefix":"10.1093","volume":"22","author":[{"given":"Marta","family":"Blangiardo","sequence":"first","affiliation":[{"name":"Department of Statistics \u2018G.Parenti\u2019, University of Florence & Biostatistic Unit 1 \u00a0 1 \u00a0 \u00a0 CSPO, Florence"}]},{"given":"Simona","family":"Toti","sequence":"additional","affiliation":[{"name":"Department of Statistics \u2018G.Parenti\u2019, University of Florence & Biostatistic Unit 1 \u00a0 1 \u00a0 \u00a0 CSPO, Florence"}]},{"given":"Betti","family":"Giusti","sequence":"additional","affiliation":[{"name":"Department \u2018Area Critica Medico Chirurgica\u2019 University of Florence, Careggi Hospital, AOC 2 \u00a0 2 \u00a0 \u00a0 Florence, Italy"}]},{"given":"Rosanna","family":"Abbate","sequence":"additional","affiliation":[{"name":"Department \u2018Area Critica Medico Chirurgica\u2019 University of Florence, Careggi Hospital, AOC 2 \u00a0 2 \u00a0 \u00a0 Florence, Italy"}]},{"given":"Alberto","family":"Magi","sequence":"additional","affiliation":[{"name":"Department \u2018Area Critica Medico Chirurgica\u2019 University of Florence, Careggi Hospital, AOC 2 \u00a0 2 \u00a0 \u00a0 Florence, Italy"},{"name":"Cytogenetic and Genetic Unit, Careggi Hospital, AOC 3 \u00a0 3 \u00a0 \u00a0 Florence, Italy"}]},{"given":"Filippo","family":"Poggi","sequence":"additional","affiliation":[{"name":"Department \u2018Area Critica Medico Chirurgica\u2019 University of Florence, Careggi Hospital, AOC 2 \u00a0 2 \u00a0 \u00a0 Florence, Italy"}]},{"given":"Luciana","family":"Rossi","sequence":"additional","affiliation":[{"name":"Department \u2018Area Critica Medico Chirurgica\u2019 University of Florence, Careggi Hospital, AOC 2 \u00a0 2 \u00a0 \u00a0 Florence, Italy"}]},{"given":"Francesca","family":"Torricelli","sequence":"additional","affiliation":[{"name":"Cytogenetic and Genetic Unit, Careggi Hospital, AOC 3 \u00a0 3 \u00a0 \u00a0 Florence, Italy"}]},{"given":"Annibale","family":"Biggeri","sequence":"additional","affiliation":[{"name":"Department of Statistics \u2018G.Parenti\u2019, University of Florence & Biostatistic Unit 1 \u00a0 1 \u00a0 \u00a0 CSPO, Florence"}]}],"member":"286","published-online":{"date-parts":[[2005,11,2]]},"reference":[{"key":"2023012408322652100_b1","doi-asserted-by":"crossref","first-page":"5009","DOI":"10.1093\/bioinformatics\/17.6.509","article-title":"A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes","volume":"17","author":"Baldi","year":"2001","journal-title":"Bioinformatics"},{"key":"2023012408322652100_b2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2164-5-17","article-title":"Improving the statistical detection of regulated genes from microarray data using intensity-based variance estimation","volume":"5","author":"Comander","year":"2004","journal-title":"BMC Genomics"},{"key":"2023012408322652100_b3","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1093\/bioinformatics\/bti023","article-title":"Efficient variance modelling for differential analysis of replicated gene expression data","volume":"21","author":"Delmar","year":"2005","journal-title":"Bioinformatics"},{"key":"2023012408322652100_b4","doi-asserted-by":"crossref","first-page":"1438","DOI":"10.1093\/bioinformatics\/18.11.1438","article-title":"Comparison of microarray designs for class comparison and class discovery","volume":"18","author":"Dobbin","year":"2002","journal-title":"Bioinformatics"},{"key":"2023012408322652100_b5","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1093\/biomet\/59.2.335","article-title":"Empirical Bayes on vector observations: an extension of Stein's method","volume":"59","author":"Efron","year":"1972","journal-title":"Biometrika"},{"key":"2023012408322652100_b6","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1198\/016214501753382129","article-title":"Empirical Bayes analysis of a microarray experiment","volume":"96","author":"Efron","year":"2001","journal-title":"J. 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