<p>This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p> <p>Microarray experiments are multi-step processes. At each step—the growth of cultures, extraction of mRNA, reverse transcription, labelling, hybridization, scanning,
and image analysis—variation and error cannot be completely avoided. Estimating
the amount of such noise and variation is essential, not only to test for differential
expression but also to suggest at which level replication is most effective.</p> <p>Replication and averaging are the key to the estimation as well as the reduction of
variability. Here I discuss the use of ANOVA mixed models and of analysis of variance
components as a rigorous way to calculate the number of replicates necessary to
detect a given target fold-change in expression levels. Procedures are available in the
package YASMA (http://www.cryst.bbk.ac.uk/wernisch/yasma.html) for the statistical
data analysis system R (http://www.R-project.org).</p>
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Comp Funct Genomics. 2002 Aug; 3(4):372-374