Kleyman Michael, Sefer Emre, Nicola Teodora, Espinoza Celia, Chhabra Divya, Hagood James S, Kaminski Naftali, Ambalavanan Namasivayam, Bar-Joseph Ziv
Machine Learning and Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States.
Division of Neonatology, Department of Pediatrics, University of Alabama at Birmingham, Birmingham, United States.
Elife. 2017 Jan 26;6:e18541. doi: 10.7554/eLife.18541.
Biological systems are increasingly being studied by high throughput profiling of molecular data over time. Determining the set of time points to sample in studies that profile several different types of molecular data is still challenging. Here we present the Time Point Selection () method that solves this combinatorial problem in a principled and practical way. utilizes expression data from a small set of genes sampled at a high rate. As we show by applying to study mouse lung development, the points selected by can be used to reconstruct an accurate representation for the expression values of the non selected points. Further, even though the selection is only based on gene expression, these points are also appropriate for representing a much larger set of protein, miRNA and DNA methylation changes over time. TPS can thus serve as a key design strategy for high throughput time series experiments. Supporting Website: www.sb.cs.cmu.edu/TPS.
随着时间的推移，生物系统越来越多地通过分子数据的高通量分析来进行研究。在对几种不同类型的分子数据进行分析的研究中，确定采样的时间点集仍然具有挑战性。在这里，我们提出了时间点选择（TPS）方法，该方法以一种有原则且实用的方式解决了这个组合问题。TPS利用从一小部分以高频率采样的基因中获得的表达数据。正如我们通过应用TPS来研究小鼠肺部发育所表明的那样，TPS选择的时间点可用于重建未选择时间点的表达值的准确表示。此外，即使选择仅基于基因表达，这些时间点也同样适用于表示随着时间推移而发生的大量蛋白质、miRNA和DNA甲基化变化。因此，TPS可以作为高通量时间序列实验的关键设计策略。支持网站：www.sb.cs.cmu.edu/TPS 。
