Biological systems are extremely dynamic and many aspects of cellular processes show rhythmic circadian patterns. Extracting such information from large expression data is challenging. In this work, we present a modified application of the Empirical Bayes periodicity test to identify genes with diurnal rhythmic behavior in two brain regions. The hypothalamus and amygdala gene expression data were generated from 100 BXD recombinant inbred mice during the day hours. Brain samples were collected over the course of two days. We first filtered the transcripts based on rank correlation at matched time points between day-1 and day-2. We then applied the proposed test of periodicity to identify diurnal rhythm genes in the full cohort and gender-specific sub-cohorts. In hypothalamus, at a Benjamini-Hochberg false discovery rate (BH-FDR) of 0.01, we identified 15 transcripts with cyclic behavior in the full cohort, none, and 53 transcripts in the female and male cohort, respectively. Similarly, in amygdala, we identified 58 diurnal rhythm genes in the full cohort, and 1 and 28 in the female and male cohorts, respectively. In conclusion, we present a modified version of the empirical Bayes periodicity test to detect periodic expression patterns. Our results demonstrate that this approach can capture cyclic patterns from relatively noisy expression data sets.

MOTIVATION: 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. AVAILABILITY AND IMPLEMENTATION: 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. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.