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

Alan L. Hutchison*, Ravi Allada, Aaron R. Dinner

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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

Original languageEnglish (US)
Pages (from-to)339-349
Number of pages11
JournalJournal of biological rhythms
Issue number4
StatePublished - Aug 1 2018


  • bioinformatics
  • circadian
  • empirical Bayes
  • gene expression analysis
  • rhythm detection

ASJC Scopus subject areas

  • Physiology
  • Physiology (medical)

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