<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>DNA microarray analysis is a biological technology which permits the whole genome
to be monitored simultaneously on a single slide. Microarray technology not only
opens an exciting research area for biologists, but also provides significant new
challenges to statisticians. Two very common questions in the analysis of microarray
data are, first, should we normalize arrays to remove potential systematic biases,
and if so, what normalization method should we use? Second, how should we then
implement tests of statistical significance? Straightforward and uniform answers
to these questions remain elusive. In this paper, we use a real data example to
illustrate a practical approach to addressing these questions. Our data is taken from a
DNA–protein binding microarray experiment aimed at furthering our understanding
of transcription regulation mechanisms, one of the most important issues in biology.
For the purpose of preprocessing data, we suggest looking at descriptive plots first
to decide whether we need preliminary normalization and, if so, how this should
be accomplished. For subsequent comparative inference, we recommend use of
an empirical Bayes method (the <italic>B</italic> statistic), since it performs much better than
traditional methods, such as the sample mean (<italic>M</italic> statistic) and Student's <italic>t</italic> statistic,
and it is also relatively easy to compute and explain compared to the others. The false
discovery rate (FDR) is used to evaluate the different methods, and our comparative
results lend support to our above suggestions.</p>
[]
Comp Funct Genomics. 2004 Jul; 5(5):432-444