Tzeng I-Shiang
Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei 231, Taiwan.
Department of Statistics, National Taipei University, Taipei 237, Taiwan.
J Pers Med. 2021 Jan 20;11(2):62. doi: 10.3390/jpm11020062.
Significance analysis of microarrays (SAM) provides researchers with a non-parametric score for each gene based on repeated measurements. However, it may lose certain power in general statistical tests to correctly detect differentially expressed genes (DEGs) which violate homogeneity. Monte Carlo simulation shows that the "half SAM score" can maintain type I error rates of about 0.05 based on assumptions of normal and non-normal distributions. The author found 265 DEGs using the half SAM scoring, more than the 119 DEGs detected by SAM, with the false discovery rate controlled at 0.05. In conclusion, the author recommends the half SAM scoring method to detect DEGs in data that show heterogeneity.
微阵列显著性分析（SAM）基于重复测量为每个基因提供了一个非参数得分。然而，在一般统计检验中，它可能会在正确检测违反同质性的差异表达基因（DEG）方面失去一定的功效。蒙特卡罗模拟表明，基于正态和非正态分布的假设，“半SAM得分”可以维持约0.05的I型错误率。作者使用半SAM评分发现了265个DEG，比SAM检测到的119个DEG更多，且错误发现率控制在0.05。总之，作者推荐使用半SAM评分方法来检测显示异质性的数据中的DEG。
