Sample Size Choice for Microarray Experiments

We review Bayesian sample size arguments for microarray experiments, focusing on a decision theoretic approach. We start by introducing a choice based on minimizing expected loss as theoretical ideal. Practical limitations of this approach quickly lead us to consider a compromise solution that combines this idealized solution with a sensitivity argument. The finally proposed approach relies on conditional expected loss, conditional on an assumed true level of differential expression to be discovered. The expression for expected loss can be interpreted as a version of power, thus providing for ease of interpretation and communication.