Empirical bayes gene screening tool for time-course or dose-response microarray data.
Empirical bayes gene screening tool for time-course or dose-response microarray data.
复制标题
DOI：
10.1081/bip-200025656
复制
发表时间：
2004-08-01
影响因子：
1.1
通讯作者：
Zacharewski, T R
中科院分区：
文献类型：
--
作者：
Eckel, J E;Gennings, C;Zacharewski, T R
为了简化从微阵列实验中产生的大量数据，一种有效的方法将成千上万个cDNA克隆的基因表达数据降维到最差异表达的cDNA克隆子集是必不可少的。对Efron等人方法的扩展[Efron， B., Tibshirani， R., Storey， J., Tusher， V.]。微阵列实验的经验贝叶斯分析。j。中央集权。[协会96:1151-1160]应用于微分时间过程实验，以确定在一组不等间隔时间点的处理条件下具有最大差异表达概率的cdna子集。提议的扩展，提倡作为一种筛选工具，除了纳入更复杂的实验设计和允许多个设计重复之外，还允许跨连续变量进行推理。根据目前的数据，重点是时间过程实验；然而，所提出的方法可以很容易地在剂量反应实验中实施，或任何其他包含感兴趣的连续变量的微阵列实验。提出的经验性贝叶斯基因筛选工具与Efron等人（2001）的方法进行了比较，此外还使用时间过程数据集对基于模型的t值进行了调整，其中正在研究特定化学物质混合物的毒理学效应。
An efficient method to reduce the dimensionality of microarray gene expression data from thousands or tens of thousands of cDNA clones down to a subset of the most differentially expressed cDNA clones is essential in order to simplify the massive amount of data generated from microarray experiments. An extension to the methods of Efron et al. [Efron, B., Tibshirani, R., Storey, J., Tusher, V. (2001). Empirical Bayes analysis of a microarray experiment. J. Am. Statist. Assoc. 96:1151-1160] is applied to a differential time-course experiment to determine a subset of cDNAs that have the largest probability of being differentially expressed with respect to treatment conditions across a set of unequally spaced time points. The proposed extension, which is advocated to be a screening tool, allows for inference across a continuous variable in addition to incorporating a more complex experimental design and allowing for multiple design replications. With the current data the focus is on a time-course experiment; however, the proposed methods can easily be implemented on a dose-response experiment, or any other microarray experiment that contains a continuous variable of interest. The proposed empirical Bayes gene-screening tool is compared with the Efron et al. (2001) method in addition to an adjusted model-based t-value using a time-course data set where the toxicological effect of a specific mixture of chemicals is being studied.