Chen Zhongxue, McGee Monnie, Liu Qingzhong, Kong Y Megan, Huang Xudong, Yang Jack Y, Scheuermann Richard H
Biostatistics/Epidemiology/Research Design Core, Center for Clinical and Translational Sciences, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Int J Comput Biol Drug Des. 2010;3(3):237-57. doi: 10.1504/IJCBDD.2010.038028. Epub 2011 Jan 11.
We propose a new method based on probe-level data (PLIDEG) to filter differentially expressed genes from non-differentially expressed genes. We compare this new method with others based on expression values by using two spikein data sets. With the extra information provided by probe level data, PLIDEG not only controls type I error, but also increases the power of detecting DEGs, simultaneously. Therefore, PLIDEG can efficiently separate DEGs and non-DEGs without requiring the estimation of the number of non-DEGs. Based on theoretical analysis and results from application to real microarray data, we confirm these good features of this new method.
我们提出了一种基于探针水平数据的新方法(PLIDEG),用于从非差异表达基因中筛选差异表达基因。我们使用两个掺入数据组,将这种新方法与基于表达值的其他方法进行比较。借助探针水平数据提供的额外信息,PLIDEG不仅能控制I型错误,还能同时提高检测差异表达基因的功效。因此,PLIDEG无需估计非差异表达基因的数量,就能有效地分离差异表达基因和非差异表达基因。基于理论分析以及应用于实际微阵列数据的结果,我们证实了这种新方法的这些良好特性。