Optimal timepoint sampling in high-throughput gene expression experiments
Optimal timepoint sampling in high-throughput gene expression experiments
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DOI：
10.1093/bioinformatics/bts511
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发表时间：
2012-11-01
期刊：
影响因子：
5.8
通讯作者：
Chen, Jin
中科院分区：
文献类型：
--
作者：
Rosa, Bruce A.;Zhang, Ji;Chen, Jin
动机：确定时间序列高通量基因表达实验的最佳采样率（最大化信息产量和最小化成本）是一个具有挑战性的优化问题。虽然现有的方法提供了深入了解最佳采样率的设计，我们利用现有的差异基因表达数据，发现最佳的时间点是引人注目的。结果：我们提出了一个新的数据整合模型，最佳时间点选择（OTS），以解决采样率的问题。在两个不同的数据集上进行了三个实验，以测试OTS的性能，包括迭代在线和一个top-up采样方法。在所有实验中，OTS的表现优于现有的最佳时间点选择方法，这表明它可以优化有限数量的时间点的分布，从而可能导致对所得基因表达模式的更好的生物学见解。
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.