Optimal timepoint sampling in high-throughput gene expression experiments
Motivation: Determining the best sampling rates (which maximize information yield and minimize cost) for time-series high-throughput gene expression experiments is a challenging optimization problem. Although existing approaches provide insight into the design of optimal sampling rates, our ability to utilize existing differential gene expression data to discover optimal timepoints is compelling.Results: We present a new data-integrative model, Optimal Timepoint Selection (OTS), to address the sampling rate problem. Three experiments were run on two different datasets in order to test the performance of OTS, including iterative-online and a top-up sampling approaches. In all of the experiments, OTS outperformed the best existing timepoint selection approaches, suggesting that it can optimize the distribution of a limited number of timepoints, potentially leading to better biological insights about the resulting gene expression patterns.Availability: OTS is available at www.msu.edu/∼jinchen/OTS.Contact: firstname.lastname@example.org; email@example.comSupplementary information: Supplementary data are available at Bioinformatics online.