Improved Statistical Methods Enable Greater Sensitivity in Rhythm Detection for Genome-Wide Data

• DOI: 10.1371/journal.pcbi.1004094
• Publication Date: 2015-03-20T19:12:39Z
• Authors: Alan L. Hutchison; Mark Maienschein-...; Andrew H. Chiang; S. M. Ali Tabei; Herman Gudjonson; Neil Bahroos; Ravi Allada; Aaron R. Dinner
• Keywords: 570; 0303 health sciences; Models, Statistical; Models, Genetic; QH301-705.5; Gene Expression Profiling; Genome, Insect; 610; Genomics; Circadian Rhythm; 03 medical and health sciences; Drosophila melanogaster; Animals; Computer Simulation; Biology (General); Research Article

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.