Open AccessReassessing design and analysis of two‐colour microarray experiments using mixed effects models
Author(s) -
Rosa Guilherme J. M.,
Steibel Juan P.,
Tempelman Robert J.
Publication year - 2005
Publication title -
comparative and functional genomics
Language(s) - English
Resource type - Journals
eISSN - 1532-6268
pISSN - 1531-6912
DOI - 10.1002/cfg.464
Subject(s) - replication (statistics) , microarray analysis techniques , microarray , computer science , design of experiments , data mining , mixed model , variance (accounting) , gene chip analysis , linear model , statistical power , expression (computer science) , statistics , machine learning , biology , mathematics , gene expression , gene , genetics , accounting , business , programming language
Abstract Gene expression microarray studies have led to interesting experimental design and statistical analysis challenges. The comparison of expression profiles across populations is one of the most common objectives of microarray experiments. In this manuscript we review some issues regarding design and statistical analysis for two‐colour microarray platforms using mixed linear models, with special attention directed towards the different hierarchical levels of replication and the consequent effect on the use of appropriate error terms for comparing experimental groups. We examine the traditional analysis of variance (ANOVA) models proposed for microarray data and their extensions to hierarchically replicated experiments. In addition, we discuss a mixed model methodology for power and efficiency calculations of different microarray experimental designs. Copyright © 2005 John Wiley & Sons, Ltd.