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High throughput technologies have become the practice of choice forcomparative studies in biomedical applications. Limited number of sample pointsdue to sequencing cost or access to organisms of interest necessitates thedevelopment of efficient sample collections to maximize the power of downstreamstatistical analyses. We propose a method for sequentially choosing trainingsamples under the Optimal Bayesian Classification framework. Specificallydesigned for RNA sequencing count data, the proposed method takes advantage ofefficient Gibbs sampling procedure with closed-form updates. Our results showsenhanced classification accuracy, when compared to random sampling.

Sequential Sampling for Optimal Bayesian Classification of Sequencing Count Data