Luan Y, Li H
Rowe Program in Human Genetics, School of Medicine, University of California, Davis, CA 95616, USA.
Bioinformatics. 2004 Feb 12;20(3):332-9. doi: 10.1093/bioinformatics/btg413.
The expressions of many genes associated with certain periodic biological and cell cycle processes such as circadian rhythm regulation are known to be rhythmic. Identification of the genes whose time course expressions are synchronized to certain periodic biological process may help to elucidate the molecular basis of many diseases, and these gene products may in turn represent drug targets relevant to those diseases.
We propose in this paper a statistical framework based on a shape-invariant model together with a false discovery rate (FDR) procedure for identifying periodically expressed genes based on microarray time-course gene expression data and a set of known periodically expressed guide genes. We applied the proposed methods to the alpha-factor, cdc15 and cdc28 synchronized yeast cell cycle data sets and identified a total of 1010 cell-cycle-regulated genes at a FDR of 0.5% in at least one of the three data sets analyzed, including 89 (86%) of 104 known periodic transcripts. We also identified 344 and 201 circadian rhythmic genes in vivo in mouse heart and liver tissues with FDR of 10 and 2.5%, respectively. Our results also indicate that the shape-invariant model fits the data well and provides estimate of the common shape function and the relative phases for these periodically regulated genes.
已知许多与某些周期性生物和细胞周期过程（如昼夜节律调节）相关的基因表达具有节律性。鉴定其时间进程表达与特定周期性生物过程同步的基因，可能有助于阐明许多疾病的分子基础，而这些基因产物反过来可能代表与这些疾病相关的药物靶点。
在本文中，我们提出了一个基于形状不变模型以及错误发现率（FDR）程序的统计框架，用于基于微阵列时间进程基因表达数据和一组已知的周期性表达的引导基因来鉴定周期性表达的基因。我们将所提出的方法应用于α因子、cdc15和cdc28同步化的酵母细胞周期数据集，并在分析的三个数据集中至少一个数据集上以0.5%的错误发现率共鉴定出1010个细胞周期调控基因，其中包括104个已知周期性转录本中的89个（86%）。我们还在小鼠心脏和肝脏组织中分别以10%和2.5%的错误发现率鉴定出344个和201个昼夜节律基因。我们的结果还表明，形状不变模型能很好地拟合数据，并为这些周期性调控基因提供共同形状函数和相对相位的估计。
