Bootstrapping and Empirical Bayes Methods Improve Rhythm Detection in Sparsely Sampled Data
Bootstrapping and Empirical Bayes Methods Improve Rhythm Detection in Sparsely Sampled Data
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DOI：
10.1177/0748730418789536
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发表时间：
2018-08-01
影响因子：
3.5
通讯作者：
Dinner, Aaron R.
中科院分区：
文献类型：
--
作者：
Hutchison, Alan L.;Allada, Ravi;Dinner, Aaron R.
利用全基因组表达时间序列来识别昼夜节律基因有很大的兴趣。然而，这种测量的成本和努力往往限制了数据收集。因此，很难评估测量中的实验不确定性，进而难以检测具有统计置信度的周期性图案。我们表明，参数自举和经验贝叶斯方差收缩方法可以提高全基因组表达时间序列的节奏检测。我们证明了这些方法的经验JTK_CYCLE方法（eJTK）的基础上制定的方法，我们术语BooteJTK。我们的程序快速，准确地检测循环时间序列相结合的测量不确定性的信息与信息的时间序列值的排序。我们利用一个公开的全基因组数据集与高时间分辨率，以表明BooteJTK提供更一致的节奏检测比现有的方法在典型的采样频率。然后，我们将BooteJTK应用于来自多个组织的全基因组表达时间序列，并表明它揭示了eJTK错过的生物学上敏感的组织关系。BooteJTK是用Python实现的，可以在GitHub上免费获得，网址是https://github.com/alanlhutchison/BooteJTK。
There is much interest in using genome-wide expression time series to identify circadian genes. However, the cost and effort of such measurements often limit data collection. Consequently, it is difficult to assess the experimental uncertainty in the measurements and, in turn, to detect periodic patterns with statistical confidence. We show that parametric bootstrapping and empirical Bayes methods for variance shrinkage can improve rhythm detection in genome-wide expression time series. We demonstrate these approaches by building on the empirical JTK_CYCLE method (eJTK) to formulate a method that we term BooteJTK. Our procedure rapidly and accurately detects cycling time series by combining information about measurement uncertainty with information about the rank order of the time series values. We exploit a publicly available genome-wide data set with high time resolution to show that BooteJTK provides more consistent rhythm detection than existing methods at typical sampling frequencies. Then, we apply BooteJTK to genome-wide expression time series from multiple tissues and show that it reveals biologically sensible tissue relationships that eJTK misses. BooteJTK is implemented in Python and is freely available on GitHub at https://github.com/alanlhutchison/BooteJTK.