The PowerAtlas: A Power and Sample Size Atlas for Microarray Experimental Design and Research

Grier P. Page
Jode W. Edwards
Gary L. Gadbury, Missouri University of Science and Technology
Prashanth Yelisetti
Jelai Wang
Prinal Trivedi
David B. Allison

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Microarrays permit biologists to simultaneously measure the mRNA abundance of thousands of genes. An important issue facing investigators planning microarray experiments is how to estimate the sample size required for good statistical power. What is the projected sample size or number of replicate chips needed to address the multiple hypotheses with acceptable accuracy? Statistical methods exist for calculating power based upon a single hypothesis, using estimates of the variability in data from pilot studies. There is, however, a need for methods to estimate power and/or required sample sizes in situations where multiple hypotheses are being tested, such as in microarray experiments. In addition, investigators frequently do not have pilot data to estimate the sample sizes required for microarray studies.