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

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

Author information

1
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.
2
Center for Research Informatics, University of Illinois at Chicago, Chicago, Illinois, United States of America.
3
James Franck Institute, University of Chicago, Chicago, Illinois, United States of America.
4
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.
5
Department of Neurobiology, Northwestern University, Evanston, Illinois, United States of America.
6
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.

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.

PMID:
25793520
PMCID:
PMC4368642
DOI:
10.1371/journal.pcbi.1004094
[Indexed for MEDLINE]
Free PMC Article

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