Park Taesung, Yi Sung-Gon, Lee Seungmook, Lee Seung Yeoun, Yoo Dong-Hyun, Ahn Jun-Ik, Lee Yong-Sung
Department of Statistics, Seoul National University, Seoul, Korea.
Bioinformatics. 2003 Apr 12;19(6):694-703. doi: 10.1093/bioinformatics/btg068.
Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. In time-course experiments in which gene expression is monitored over time, we are interested in testing gene expression profiles for different experimental groups. However, no sophisticated analytic methods have yet been proposed to handle time-course experiment data.
We propose a statistical test procedure based on the ANOVA model to identify genes that have different gene expression profiles among experimental groups in time-course experiments. Especially, we propose a permutation test which does not require the normality assumption. For this test, we use residuals from the ANOVA model only with time-effects. Using this test, we detect genes that have different gene expression profiles among experimental groups. The proposed model is illustrated using cDNA microarrays of 3840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells.
微阵列技术能够同时监测数千个基因的表达水平。在随时间监测基因表达的时间进程实验中，我们关注的是测试不同实验组的基因表达谱。然而，目前尚未提出复杂的分析方法来处理时间进程实验数据。
我们提出了一种基于方差分析模型的统计检验程序，以识别在时间进程实验中不同实验组间具有不同基因表达谱的基因。特别是，我们提出了一种无需正态性假设的置换检验。对于此检验，我们仅使用来自仅含时间效应的方差分析模型的残差。通过使用此检验，我们检测出不同实验组间具有不同基因表达谱的基因。使用在一项寻找皮质干细胞神经元分化过程中基因表达谱变化的实验中获得的3840个基因的cDNA微阵列对所提出的模型进行了说明。
