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An adaptive empirical Bayesian thresholding procedure for analysing microarray experiments with replication
Author(s) -
Walls Rebecca E.,
Barber Stuart,
Kent John T.,
Gilthorpe Mark S.
Publication year - 2007
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2007.00577.x
Subject(s) - bayesian probability , replication (statistics) , thresholding , computer science , robustness (evolution) , dna microarray , artificial intelligence , data mining , pattern recognition (psychology) , mathematics , biology , statistics , gene , genetics , gene expression , image (mathematics)
Summary. A typical microarray experiment attempts to ascertain which genes display differential expression in different samples. We model the data by using a two‐component mixture model and develop an empirical Bayesian thresholding procedure, which was originally introduced for thresholding wavelet coefficients, as an alternative to the existing methods for determining differential expression across thousands of genes. The method is built on sound theoretical properties and has easy computer implementation in the R statistical package. Furthermore, we consider improvements to the standard empirical Bayesian procedure when replication is present, to increase the robustness and reliability of the method. We provide an introduction to microarrays for those who are unfamilar with the field and the proposed procedure is demonstrated with applications to two‐channel complementary DNA microarray experiments.