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Background: Microarray experiments are often performed with a small number of biological replicates,
resulting in low statistical power for detecting differentially expressed genes and concomitant high false
positive rates. While increasing sample size can increase statistical power and decrease error rates, with
too many samples, valuable resources are not used efficiently. The issue of how many replicates are required
in a typical experimental system needs to be addressed. Of particular interest is the difference in required
sample sizes for similar experiments in inbred vs. outbred populations (e.g. mouse and rat vs. human).
Results: We hypothesize that if all other factors (assay protocol, microarray platform, data pre-processing)
were equal, fewer individuals would be needed for the same statistical power using inbred animals as opposed to
unrelated human subjects, as genetic effects on gene expression will be removed in the inbred populations.
We apply the same normalization algorithm and estimate the variance of gene expression for a variety of cDNA
data sets (humans, inbred mice and rats) comparing two conditions. Using one sample, paired sample or two
independent sample t-tests, we calculate the sample sizes required to detect a 1.5-, 2-, and 4-fold changes
in expression level as a function of false positive rate, power and percentage of genes that have a standard
deviation below a given percentile.
Conclusions: Factors that affect power and sample size calculations include variability of the
population, the desired detectable differences, the power to detect the differences, and an acceptable error
rate. In addition, experimental design, technical variability and data pre-processing play a role in the power
of the statistical tests in microarrays. We show that the number of samples required for detecting a 2-fold change
with 90% probability and a p-value of 0.01 in humans is much larger than the number of samples commonly used in
present day studies, and that far fewer individuals are needed for the same statistical power when using inbred
animals rather than unrelated human subjects.
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