Toyoshiba Hiroyoshi, Yamanaka Takeharu, Sone Hideko, Parham Frederick M, Walker Nigel J, Martinez Jeanelle, Portier Christopher J
Laboratory of Computational Biology and Risk Analysis, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709, USA.
Environ Health Perspect. 2004 Aug;112(12):1217-24. doi: 10.1289/txg.7020.
Gene expression arrays (gene chips) have enabled researchers to roughly quantify the level of mRNA expression for a large number of genes in a single sample. Several methods have been developed for the analysis of gene array data including clustering, outlier detection, and correlation studies. Most of these analyses are aimed at a qualitative identification of what is different between two samples and/or the relationship between two genes. We propose a quantitative, statistically sound methodology for the analysis of gene regulatory networks using gene expression data sets. The method is based on Bayesian networks for direct quantification of gene expression networks. Using the gene expression changes in HPL1A lung airway epithelial cells after exposure to 2,3,7,8-tetrachlorodibenzo-(Italic)p(/Italic)-dioxin at levels of 0.1, 1.0, and 10.0 nM for 24 hr, a gene expression network was hypothesized and analyzed. The method clearly demonstrates support for the assumed network and the hypothesis linking the usual dioxin expression changes to the retinoic acid receptor system. Simulation studies demonstrated the method works well, even for small samples.
基因表达阵列（基因芯片）使研究人员能够在单个样本中大致定量大量基因的mRNA表达水平。已经开发了几种用于分析基因阵列数据的方法，包括聚类、异常值检测和相关性研究。这些分析大多旨在定性识别两个样本之间的差异和/或两个基因之间的关系。我们提出了一种定量的、统计学上合理的方法，用于使用基因表达数据集分析基因调控网络。该方法基于贝叶斯网络，用于直接定量基因表达网络。利用人肺气道上皮细胞HPL1A在暴露于0.1、1.0和10.0 nM的2,3,7,8-四氯二苯并-对-二恶英24小时后的基因表达变化，对一个基因表达网络进行了假设和分析。该方法清楚地证明了对假定网络以及将通常的二恶英表达变化与视黄酸受体系统联系起来的假设的支持。模拟研究表明，该方法即使对于小样本也能很好地发挥作用。
