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.