Sequential analysis of large scale data screening problem

2015-01-26High-throughput screening (HTS) for drug discovery is a large-scale hierarchical process in which a large number of chemicals are tested in multiple stages. Conventional statistical analyses of HTS studies often suffer from high test error rates and soaring costs in large-scale settings. This dissertation develops new methodologies for false discovery rate control and optimal design in HTS studies. The dissertation proposes a two-stage procedure that determines the optimal numbers of replicates at different screening stages while simultaneously controlling the false discovery rate in the confirmatory stage subject to a constraint on the total budget. The merits of the proposed methods are illustrated using both simulated and real data. It is shown that the proposed screening procedure effectively controls the error rate and the design leads to improved detection power and great savings in study costs