Improved statistical methods enable greater sensitivity in rhythm detection for genome-wide data

PLoS Comput Biol. 2015 Mar 20;11(3):e1004094. doi: 10.1371/journal.pcbi.1004094. eCollection 2015 Mar.


Robust methods for identifying patterns of expression in genome-wide data are important for generating hypotheses regarding gene function. To this end, several analytic methods have been developed for detecting periodic patterns. We improve one such method, JTK_CYCLE, by explicitly calculating the null distribution such that it accounts for multiple hypothesis testing and by including non-sinusoidal reference waveforms. We term this method empirical JTK_CYCLE with asymmetry search, and we compare its performance to JTK_CYCLE with Bonferroni and Benjamini-Hochberg multiple hypothesis testing correction, as well as to five other methods: cyclohedron test, address reduction, stable persistence, ANOVA, and F24. We find that ANOVA, F24, and JTK_CYCLE consistently outperform the other three methods when data are limited and noisy; empirical JTK_CYCLE with asymmetry search gives the greatest sensitivity while controlling for the false discovery rate. Our analysis also provides insight into experimental design and we find that, for a fixed number of samples, better sensitivity and specificity are achieved with higher numbers of replicates than with higher sampling density. Application of the methods to detecting circadian rhythms in a metadataset of microarrays that quantify time-dependent gene expression in whole heads of Drosophila melanogaster reveals annotations that are enriched among genes with highly asymmetric waveforms. These include a wide range of oxidation reduction and metabolic genes, as well as genes with transcripts that have multiple splice forms.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Circadian Rhythm / genetics*
  • Computer Simulation
  • Drosophila melanogaster / genetics
  • Gene Expression Profiling / methods*
  • Genome, Insect / genetics
  • Genomics / methods*
  • Models, Genetic*
  • Models, Statistical*