NITPicker: selecting time points for follow-up experiments
NITPicker: selecting time points for follow-up experiments
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DOI：
10.1186/s12859-019-2717-5
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发表时间：
2019-04-02
影响因子：
3
通讯作者：
Keir, Joseph
中科院分区：
文献类型：
--
作者：
Ezer, Daphne;Keir, Joseph
背景实验的设计既影响研究人员可以测量的内容，也影响结果的可信度。因此，实验设计决策不会系统性地影响研究结果，这一点至关重要。同时，做出最佳设计决策可以产生统计上更有说服力的结论。决定采样地点和时间是许多实验设计中最关键的方面之一；例如，我们可能必须选择时间点来测量时间序列实验中的某些数量。选择相距太远的时间可能会导致错过短暂的活动爆发。另一方面，可能有些时间点提供的关于相关数量的整体行为的信息非常少。结果在这项研究中，我们开发了一种名为 NITPicker（下一次迭代时间点选择器）的工具，用于选择最佳时间点（或沿单个轴的空间点），消除了人类决策造成的一些偏差，同时最大化有关基础曲线形状的信息。 NITPicker 使用功能数据分析领域的思想。 NITPicker 可在综合 R 档案网络 (CRAN) 上使用，绘制图形的代码可在 Github (https://github.com/ezer/NITPicker) 上找到。结论 NITPicker 在与各种生物应用相关的各种现实世界数据集上表现良好，包括为纵向基因表达数据、天气模式随时间变化和生长曲线设计后续实验。
BackgroundThe design of an experiment influences both what a researcher can measure, as well as how much confidence can be placed in the results. As such, it is vitally important that experimental design decisions do not systematically bias research outcomes. At the same time, making optimal design decisions can produce results leading to statistically stronger conclusions. Deciding where and when to sample are among the most critical aspects of many experimental designs; for example, we might have to choose the time points at which to measure some quantity in a time series experiment. Choosing times which are too far apart could result in missing short bursts of activity. On the other hand, there may be time points which provide very little information regarding the overall behaviour of the quantity in question.ResultsIn this study, we develop a tool called NITPicker (Next Iteration Time-point Picker) for selecting optimal time points (or spatial points along a single axis), that eliminates some of the biases caused by human decision-making, while maximising information about the shape of the underlying curves. NITPicker uses ideas from the field of functional data analysis. NITPicker is available on the Comprehensive R Archive Network (CRAN) and code for drawing figures is available on Github (https://github.com/ezer/NITPicker).ConclusionsNITPicker performs well on diverse real-world datasets that would be relevant for varied biological applications, including designing follow-up experiments for longitudinal gene expression data, weather pattern changes over time, and growth curves.