This is an exciting and influential time for the field of Statistics in science. Technological advances in genetic, genomic, and the other 'omic sciences are providing large amounts of complex data that are presenting a number of challenges for the biological community. Many of these challenges are deeply rooted statistical issues that involve experimental design. Although there are many different computational tools for processing these data, there are a limited number of statistical methods for analyzing them, and even fewer that acknowledge the unique nature of individual gene transcription. After a discussion about experimental design for next-generation sequencing experiments, a simple and powerful statistical approach based on a two-stage Poisson model for modeling RNA sequencing data will be presented for the purpose of testing biologically important changes in gene expression. The advantages of this approach are demonstrated through simulations and real data applications.
<a href="http://www.stat.purdue.edu/~doerge/">Purdue University</a>
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