Dr. Laurent Briollais

Samuel Lunenfeld Research Institute

Title: " Sequential design for microarray studies "

A critical aspect in the design of microarray studies is the determination
of the sample size necessary to declare genes differentially expressed
across different experimental conditions. Here, we propose a sequential
approach where the decision to stop the experiment depends on the
accumulated microarray data. The study could stop whenever sufficient
data have been accumulated to identify gene expression changes across
several experimental conditions. The gene expression response is modeled
by a robust linear regression model. We then construct a sequential
confidence interval for the intercept of this model, which represents the
median gene expression at a given experimental condition. We derive the
stopping rule of the experiment for both continuous and discrete sequential
approaches and give the asymptotic properties of the stopping variable. In
our application to a study of hormone responsive breast cancer cell lines,
we estimated the stopping variable for the sample size determination to be
smaller than the actual sample size available to conduct the experiment.
This means that we can obtain an accurate assessment of differential gene
expression without compromising the cost and size of the study. Altogether,
we anticipate that this approach could have an important contribution to
microarray studies by improving the usual experimental designs and methods
of analysis.