Broberg Per
Molecular Sciences, AstraZeneca Research and Development Lund, S-221 87 Lund, Sweden.
Genome Biol. 2003;4(6):R41. doi: 10.1186/gb-2003-4-6-r41. Epub 2003 May 29.
In the analysis of microarray data the identification of differential expression is paramount. Here I outline a method for finding an optimal test statistic with which to rank genes with respect to differential expression. Tests of the method show that it allows generation of top gene lists that give few false positives and few false negatives. Estimation of the false-negative as well as the false-positive rate lies at the heart of the method.
在微阵列数据分析中,鉴别差异表达至关重要。在此,我概述一种方法,用于找到一个最优检验统计量,以便根据差异表达对基因进行排名。对该方法的测试表明,它能够生成假阳性和假阴性都很少的顶级基因列表。假阴性率和假阳性率的估计是该方法的核心所在。