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Improved Statistical Methods Enable Greater Sensitivity in Rhythm Detection for Genome-Wide Data
PLOS Computational Biology (2014)
  • Alan H. Hutchison, The University of Illinois at Chicago
  • Mark Maienschein-Cline, The University of Illinois at Chicago
  • Andres H. Chiang, The University of Illinois at Chicago
  • S.M. Ali Tabei, University of Northern Iowa
  • Herman Gudjonson, The University of Illinois at Chicago
  • Neil Bahroos, University of Chicago
  • Ravi Allada, The University of Illinois at Chicago
  • Aaron R. Dinner, The University of Illinois at Chicago
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.
Disciplines
Publication Date
2014
DOI
10.1371/journal.pcbi.1004094
Citation Information
Alan H. Hutchison, Mark Maienschein-Cline, Andres H. Chiang, S.M. Ali Tabei, et al.. "Improved Statistical Methods Enable Greater Sensitivity in Rhythm Detection for Genome-Wide Data" PLOS Computational Biology Vol. 11 Iss. 3 (2014)
Available at: http://works.bepress.com/sm-tabei/1/