title: An empirical Bayes approach for analysis of diverse periodic trends in time-course gene expression data.
abstract: BACKGROUND
  There is a substantial body of works in the biology literature that seeks to characterize the cyclic behavior of genes during cell division. Gene expression microarrays made it possible to measure the expression profiles of thousands of genes simultaneously in time-course experiments to assess changes in the expression levels of genes over time. In this context, the commonly used procedures for testing include the permutation test by de Lichtenberg et al. and the Fisher's G-test, both of which are designed to evaluate periodicity against noise. However, it is possible that a gene of interest may have expression that is neither cyclic nor just noise. Thus, there is a need for a new test for periodicity that can identify cyclic patterns against not only noise but also other non-cyclic patterns such as linear, quadratic or higher order polynomial patterns.
  
  
  RESULTS
  To address this weakness, we have introduced an empirical Bayes approach to test for periodicity and compare its performance in terms of sensitivity and specificity with that of the permutation test and Fisher's G-test through extensive simulations and by application to a set of time-course experiments on the Schizosaccharomyces pombe cell-cycle gene expression. We use 'conserved' and 'cycling' genes by Lu et al. to assess the sensitivity and CESR genes by Chenet al. to assess the specificity of our new empirical Bayes method.
  
  
  METHODS
  The SAS Macro for our empirical Bayes test for periodicity is included in the supplementary materials along with a sample run of the MACRO program.
  
  
  BACKGROUND
  mkocak1@uthsc.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Preventive Medicine, University of Tennessee Health Sciences Center, Memphis, TN 38105, USA. mkocak1@uthsc.edu
authors.forename: Mehmet
authors.initials: M
authors.lastname: Kocak
authors.forename: E Olusegun
authors.initials: EO
authors.lastname: George
authors.forename: Saumyadipta
authors.initials: S
authors.lastname: Pyne
authors.forename: Stanley
authors.initials: S
authors.lastname: Pounds
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002453:Cell Cycle / Q000235:genetics; D015870:Gene Expression; D020869:Gene Expression Profiling; D020411:Oligonucleotide Array Sequence Analysis; D010507:Periodicity; D012568:Schizosaccharomyces / Q000235:genetics; D012680:Sensitivity and Specificity