Bootstrapping and Empirical Bayes Methods Improve Rhythm Detection in Sparsely Sampled Data.

ABSTRACT: 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 v

SUBMITTER: Hutchison AL 

PROVIDER: S-EPMC6347739 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature