基于元分析的差异表达基因识别

针对传统差异表达基因分析方法使用单一数据集,不能处理异质性特性数据集、分析结果偏差大的问题,提出了联合驱动排除的概念,设计实现一种使用多研究数据集的元分析算法。应用公共数据库GEO中GDS2490和GDS2491数据集对设计的算法与微芯片显著性分析方法进行了对比。实验结果表明:设计的算法可以对数据集实行联合驱动排除,与微芯片显著性分析方法相比,可以有效分析异质性特性的数据集,准确找到差异表达的基因,验证了算法的有效性。该方法为差异表达基因识别提供了新思路。

Identification of differentially expressed genes based on meta-analysis

Traditional methods of differentially expressed genes analysis used only one single dataset of a study, so they couldn't handle the heterogeneity between studies and lead to inconsistency of analysis results. To overcome the above shortcoming, we proposed the concept of integration-driven exclusion, designed and implemented an algorithm to use several datasets of different studies. In this algorithm we presented a meta-analysis tool to identify differentially expressed genes. Using datasets GDS2490 and GDS2491 from GEO, we compared this algorithm with the method of significance analysis of microarrays. Experiment results show that the designed algorithm can identify differentially expressed genes accurately by integration-driven excluding, handle heterogeneity between studies effectively. This algorithm provides a new approach to identify differentially expressed genes.