biostudies-literatureUnknown9Astrand M<h4>Background</h4>When analyzing microarray data a primary objective is often to find differentially expressed genes. With empirical Bayes and penalized t-tests the sample variances are adjusted towards a global estimate, producing more stable results compared to ordinary t-tests. However, for Affymetrix type data a clear dependency between variability and intensity-level generally exists, even for logged intensities, most clearly for data at the probe level but also for probe-set summarizes such as the MAS5 expression index. As a consequence, adjustment towards a global estimate results in an intensity-level dependent false positive rate.<h4>Results</h4>We propose two new methods for finding differentially expressed genes, Probe level Locally moderated Weighted median-t (PLW) and LocallyBMC bioinformatics156https://www.ebi.ac.uk/biostudies/studies/S-EPMC2358895biostudies-literatureEmpirical Bayes models for multiple probe type microarrays at the probe level.PMC2358895Rudemo MAstrand MMostad PfalseEmpirical Bayes models for multiple probe type microarrays at the probe level.<h4>Background</h4>When analyzing microarray data a primary objective is often to find differentially expressed genes. With empirical Bayes and penalized t-tests the sample variances are adjusted towards a global estimate, producing more stable results compared to ordinary t-tests. However, for Affymetrix type data a clear dependency between variability and intensity-level generally exists, even for logged intensities, most clearly for data at the probe level but also for probe-set summarizes such as the MAS5 expression index. As a consequence, adjustment towards a global estimate results in an intensity-level dependent false positive rate.<h4>Results</h4>We propose two new methods for finding differentially expressed genes, Probe level Locally moderated Weighted median-t (PLW) and Locally2008-01-01T00:00:00Z2008 Mar2025-04-04T10:42:14.165Z2019-03-27T02:44:54ZS-EPMC23588951836669410.1186/1471-2105-9-156