An Application of the Bayesian Periodicity Test to Identify Diurnal Rhythm Genes in the Brain

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.