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

25 Scopus citations

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 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.

Original languageEnglish (US)
Pages (from-to)339-349
Number of pages11
JournalJournal of biological rhythms
Volume33
Issue number4
DOIs
StatePublished - Aug 1 2018

Funding

We would like to thank Matthew Stephens for many discussions regarding bootstrapping and John Hogenesch for sharing data from Zhang et al. (2014). This work was completed in part with resources provided by the University of Chicago Research Computing Center. This work was supported by the Defense Advanced Research Projects Agency (D12AP00023) www.darpa.mil/. ALH is a trainee of the National Institutes of Health Medical Scientist Training program at the University of Chicago (grant NIGMS T32GM07281; www.nigms.nih.gov) and was supported in part by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health (award T32EB009412; www.nibib.nih.gov). The content is solely the responsibility of the authors and does not necessarily reflect the position or the policy of the government, and no official endorsement should be inferred. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Keywords

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

ASJC Scopus subject areas

  • Physiology
  • Physiology (medical)

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