  High throughput technologies have become the practice of choice for
comparative studies in biomedical applications. Limited number of sample points
due to sequencing cost or access to organisms of interest necessitates the
development of efficient sample collections to maximize the power of downstream
statistical analyses. We propose a method for sequentially choosing training
samples under the Optimal Bayesian Classification framework. Specifically
designed for RNA sequencing count data, the proposed method takes advantage of
efficient Gibbs sampling procedure with closed-form updates. Our results shows
enhanced classification accuracy, when compared to random sampling.
