Detecting differentially expressed genes in multiple tag sampling experiments: comparative evaluation of statistical tests
Detecting differentially expressed genes in multiple tag sampling experiments: comparative evaluation of statistical tests
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DOI：
10.1093/hmg/10.19.2133
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发表时间：
2001-09-15
影响因子：
3.5
通讯作者：
Danieli, GA
中科院分区：
文献类型：
--
作者：
Romualdi, C;Bortoluzzi, S;Danieli, GA
目前用于检测差异表达基因的几种统计方法的比较尝试通过模拟方法和通过分析从UniGene获得的人类表达序列标签的数据集。在模拟的混合情况下，模拟接近现实的情况，一般卡方检验出乎意料地在多标签抽样实验中是最有效的，特别是在处理影响弱表达基因的变异时。另一方面，Audic和Claverie的方法被证明是在处理成对比较时检测基因表达差异的最有效方法。通过将上述方法应用于与正常肾组织相比的关于两种人肾肿瘤的基于UniGene的数据集，鉴定了在这些肿瘤中过表达的三种新基因。关于统计方法、模拟方法和数据的软件和其他信息可在http://telethon.bio.unipd.it/bioinfo/IDEG6/上查阅。
The comparison of several statistical methods currently used for detection of differentially expressed genes was attempted both by a simulation approach and by the analysis of data sets of human expressed sequence tags, obtained from UniGene. In the simulated mixed case, mimicking a situation close to reality, the general chi (2) test was unexpectedly the most efficient in multiple tag sampling experiments, especially when dealing with variations affecting weakly expressed genes. On the other hand, Audic and Claverie's method proved the most efficient for detecting differences in gene expression when dealing with pairwise comparisons. By applying the above methods on UniGene-based data sets concerning two human kidney tumours compared with normal kidney tissue, three novel genes overexpressed in these tumours were identified. Software and additional information on statistical methodologies, simulation approach and data are available at http://telethon.bio.unipd.it/bioinfo/IDEG6/.