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.

中文翻译：

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贝叶斯周期性测试在识别大脑昼夜节律基因中的应用。

生物系统极为活跃，细胞过程的许多方面都显示出节律性的昼夜节律模式。从大表达数据中提取此类信息具有挑战性。在这项工作中，我们提出了经验贝叶斯周期性测试的改进应用，以识别两个大脑区域具有昼夜节律行为的基因。下丘脑和杏仁核基因表达数据是在白天从100只BXD重组近交小鼠产生的。在两天内收集脑样本。我们首先在第1天和第2天之间的匹配时间点基于排名相关性过滤了成绩单。然后，我们应用提出的周期性测试来确定整个队列和性别特定子队列的昼夜节律基因。在下丘脑中，Benjamini-Hochberg的错误发现率（BH-FDR）为0.01，我们在整个队列中确定了15个具有循环行为的转录本，而在女性和男性队列中分别确定了53个转录本。同样，在杏仁核中，我们在整个队列中分别确定了58个昼夜节律基因，在女性和男性队列中分别确定了1个和28个昼夜节律基因。总之，我们提出了经验贝叶斯周期性测试的修改版本，以检测周期表达模式。我们的结果表明，这种方法可以从相对嘈杂的表达数据集中捕获循环模式。我们提出了经验贝叶斯周期性测试的修改版本，以检测周期表达模式。我们的结果表明，这种方法可以从相对嘈杂的表达数据集中捕获循环模式。我们提出了经验贝叶斯周期性测试的修改版本，以检测周期表达模式。我们的结果表明，这种方法可以从相对嘈杂的表达数据集中捕获循环模式。