Bayesian detection of non-sinusoidal periodic patterns in circadian expression data

MOTIVATION Cyclical biological processes such as cell division and circadian regulation produce coordinated periodic expression of thousands of genes. Identification of such genes and their expression patterns is a crucial step in discovering underlying regulatory mechanisms. Existing computational methods are biased toward discovering genes that follow sine-wave patterns. RESULTS We present an analysis of variance (ANOVA) periodicity detector and its Bayesian extension that can be used to discover periodic transcripts of arbitrary shapes from replicated gene expression profiles. The models are applicable when the profiles are collected at comparable time points for at least two cycles. We provide an empirical Bayes procedure for estimating parameters of the prior distributions and derive closed-form expressions for the posterior probability of periodicity, enabling efficient computation. The model is applied to two datasets profiling circadian regulation in murine liver and skeletal muscle, revealing a substantial number of previously undetected non-sinusoidal periodic transcripts in each. We also apply quantitative real-time PCR to several highly ranked non-sinusoidal transcripts in liver tissue found by the model, providing independent evidence of circadian regulation of these genes. AVAILABILITY Matlab software for estimating prior distributions and performing inference is available for download from http://www.datalab.uci.edu/resources/periodicity/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

[1]  John K Kruschke,et al.  Bayesian data analysis. , 2010, Wiley interdisciplinary reviews. Cognitive science.

[2]  Kevin P. Keegan,et al.  Meta-Analysis of Drosophila Circadian Microarray Studies Identifies a Novel Set of Rhythmically Expressed Genes , 2007, PLoS Comput. Biol..

[3]  Erin L. McDearmon,et al.  Identification of the circadian transcriptome in adult mouse skeletal muscle. , 2007, Physiological genomics.

[4]  Erin L. McDearmon,et al.  Circadian and CLOCK-controlled regulation of the mouse transcriptome and cell proliferation , 2007, Proceedings of the National Academy of Sciences.

[5]  T. Speed,et al.  A multivariate empirical Bayes statistic for replicated microarray time course data , 2006, math/0702685.

[6]  Andrey A. Ptitsyn,et al.  Circadian Clocks Are Resounding in Peripheral Tissues , 2006, PLoS Comput. Biol..

[7]  Mats G. Gustafsson,et al.  Bayesian detection of periodic mRNA time profiles without use of training examples , 2006, BMC Bioinformatics.

[8]  Adam Claridge‐Chang,et al.  Control of Daily Transcript Oscillations in Drosophila by Light and the Circadian Clock , 2006, PLoS genetics.

[9]  Tom S. Price,et al.  Bioinformatic Analysis of Circadian Gene Oscillation in Mouse Aorta , 2005, Circulation.

[10]  P. Müller,et al.  A Bayesian mixture model for differential gene expression , 2005 .

[11]  Padhraic Smyth,et al.  Identification of hair cycle-associated genes from time-course gene expression profile data by using replicate variance , 2004, Proc. Natl. Acad. Sci. USA.

[12]  R. Gentleman,et al.  A Model-Based Background Adjustment for Oligonucleotide Expression Arrays , 2004 .

[13]  Deepayan Sarkar,et al.  Detecting differential gene expression with a semiparametric hierarchical mixture method. , 2004, Biostatistics.

[14]  Hongzhe Li,et al.  Model-based methods for identifying periodically expressed genes based on time course microarray gene expression data , 2004, Bioinform..

[15]  K. Kadota,et al.  Genome-wide Expression Analysis of Mouse Liver Reveals CLOCK-regulated Circadian Output Genes* , 2003, Journal of Biological Chemistry.

[16]  M. C. Rudolph,et al.  Functional Development of the Mammary Gland: Use of Expression Profiling and Trajectory Clustering to Reveal Changes in Gene Expression During Pregnancy, Lactation, and Involution , 2003, Journal of Mammary Gland Biology and Neoplasia.

[17]  Kai-Florian Storch,et al.  Extensive and divergent circadian gene expression in liver and heart , 2002, Nature.

[18]  F. Conquet,et al.  Circadian Expression of the Steroid 15 α-Hydroxylase (Cyp2a4) and Coumarin 7-Hydroxylase (Cyp2a5) Genes in Mouse Liver Is Regulated by the PAR Leucine Zipper Transcription Factor DBP , 1999, Molecular and Cellular Biology.

[19]  Gordon K Smyth,et al.  Statistical Applications in Genetics and Molecular Biology Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2011 .

[20]  Sylvia Richardson,et al.  Statistical Applications in Genetics and Molecular Biology Fully Bayesian Mixture Model for Differential Gene Expression : Simulations and Model Checks , 2011 .

[21]  T. Speed,et al.  On the gene ranking of replicated microarray time course data , 2007 .

[22]  Martin Straume,et al.  DNA Microarray Time Series Analysis: Automated Statistical Assessment of Circadian Rhythms in Gene Expression Patterning , 2004, Numerical Computer Methods, Part D.