POWER_SAGE: comparing statistical tests for SAGE experiments
POWER_SAGE: comparing statistical tests for SAGE experiments
复制标题
DOI：
10.1093/bioinformatics/16.11.953
复制
发表时间：
2000-11-01
期刊：
影响因子：
5.8
通讯作者：
Wang, YX
中科院分区：
文献类型：
--
作者：
Man, MZ;Wang, XN;Wang, YX
动机：基因表达系列分析（SAGE）技术通过测量衍生自相应mRNA转录物的序列标签的频率来确定基因的表达水平。已经开发了几种统计测试来检测两个样本之间的标签频率的显著差异。然而，其中一个这些测试具有最大的权力，以检测真实的变化仍然undertaked.Results：本文比较了三个统计检验检测SAGE实验中的基因表达的显着变化。比较利用蒙特卡罗模拟，在本质上，产生“虚拟”SAGE实验。我们的分析表明，卡方检验具有最好的功效和鲁棒性。由于POWER-SAGE程序可以很容易地运行“虚拟”SAGE研究与样本量和标签频率的不同组合，并确定每个组合的功率，它可以作为一个有用的工具，规划SAGE实验。
Motivation: The Serial Analysis of Gene Expression (SAGE) technology determines the expression level of a gene by measuring the frequency of a sequence tag derived from the corresponding mRNA transcript. Several statistical tests have been developed to detect significant differences in tag frequency between two samples. However, which one of these tests has the greatest power to detect real changes remains undetermined.Results: This paper compares three statistical tests for detecting significant changes of gene expression in SAGE experiments. The comparison makes use of Monte Carlo simulation that, in essence, generates 'virtual' SAGE experiments. Our analysis shows that the Chi-square test has the best power and robustness. Since the POWER-SAGE program can easily run 'virtual' SAGE studies with different combinations of sample size and tag frequency and determine the power for each combination, it can serve as a useful tool for planning SAGE experiments.