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

Abstract

MotivationThere is much interest in using genome-wide expression time series to identify circadian genes. However, the cost and effort of such measurements often limits data collection. Consequently, it is difficult to assess the experimental uncertainty in the measurements and, in turn, to detect periodic patterns with statistical confidence.\n\nResultsWe 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 dataset with high time resolution to show that BooteJTK provides more consistent rhythm detection thanexisting 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.\n\nAvailabilityBootstrap eJTK (BooteJTK) is implemented in Python and is freely available on GitHub at https://github.com/alanlhutchison/BooteJTK.