title: Improved statistical methods enable greater sensitivity in rhythm detection for genome-wide data.
abstract: 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.
authors: Medical Scientist Training Program, University of Chicago, Chicago, Illinois, United States of America; Graduate Program in the Biophysical Sciences, University of Chicago, Chicago, Illinois, United States of America; James Franck Institute, University of Chicago, Chicago, Illinois, United States of America.
authors: Alan L
authors: AL
authors: Hutchison
authors: Center for Research Informatics, University of Illinois at Chicago, Chicago, Illinois, United States of America.
authors: Mark
authors: M
authors: Maienschein-Cline
authors: James Franck Institute, University of Chicago, Chicago, Illinois, United States of America.
authors: Andrew H
authors: AH
authors: Chiang
authors: James Franck Institute, University of Chicago, Chicago, Illinois, United States of America.
authors: S M Ali
authors: SM
authors: Tabei
authors: Graduate Program in the Biophysical Sciences, University of Chicago, Chicago, Illinois, United States of America; James Franck Institute, University of Chicago, Chicago, Illinois, United States of America.
authors: Herman
authors: H
authors: Gudjonson
authors: Center for Research Informatics, University of Illinois at Chicago, Chicago, Illinois, United States of America.
authors: Neil
authors: N
authors: Bahroos
authors: Department of Neurobiology, Northwestern University, Evanston, Illinois, United States of America.
authors: Ravi
authors: R
authors: Allada
authors: Graduate Program in the Biophysical Sciences, University of Chicago, Chicago, Illinois, United States of America; James Franck Institute, University of Chicago, Chicago, Illinois, United States of America; Department of Chemistry, University of Chicago, Chicago, Illinois, United States of America.
authors: Aaron R
authors: AR
authors: Dinner
mesh_terms: D000818:Animals; D002940:Circadian Rhythm / Q000235:genetics*; D003198:Computer Simulation; D004331:Drosophila melanogaster / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D049750:Genome, Insect / Q000235:genetics; D023281:Genomics / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical