摘要翻译：
对于相对于细胞的两种或两种以上状态改变了表达水平的基因，可以在不同程度上富集预先指定的一组基因。了解基因本体（gene ontology,GO）注释等功能类别定义的基因集的丰富程度，对于分析微阵列表达数据中的生物信号具有重要意义。衡量浓缩的一种常见方法是根据基因在功能类别中的成员资格和在选定的显著改变基因列表中的成员资格对基因进行交叉分类。例如，在这个$2\times2$表中，一个小的Fisher精确测试$p$-value表示富集。其他类别分析方法保留基因水平的定量得分，并通过将类别水平的统计量引用与原始差异表达问题相关的排列分布来测量显著性。我们描述了一类测量富集信号不同分量的随机集评分方法。该类别包括基于选定基因的Fisher测试，以及在整个类别中平均基因水平证据的测试。利用Affymetrix在鼻咽癌组织中表达的数据进行经验比较，并从理论上使用差异表达的定位模型对平均和选择方法进行比较。我们发现每一种方法在富集问题的状态空间中都有各自的优势，并且这两种方法在实践中都有好处。我们的分析还解决了与多类别推理有关的两个问题，即相同丰富的类别在大小不同的情况下不能以相同的概率被检测到，以及类别统计量之间由于基因共享而存在依赖性。随机集浓缩计算不需要蒙特卡罗来实现。它们在R包Allez中提供。
---
英文标题：
《Random-set methods identify distinct aspects of the enrichment signal in
  gene-set analysis》
---
作者：
Michael A. Newton, Fernando A. Quintana, Johan A. den Boon, Srikumar
  Sengupta, Paul Ahlquist
---
最新提交年份：
2007
---
分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
--

---
英文摘要：
  A prespecified set of genes may be enriched, to varying degrees, for genes that have altered expression levels relative to two or more states of a cell. Knowing the enrichment of gene sets defined by functional categories, such as gene ontology (GO) annotations, is valuable for analyzing the biological signals in microarray expression data. A common approach to measuring enrichment is by cross-classifying genes according to membership in a functional category and membership on a selected list of significantly altered genes. A small Fisher's exact test $p$-value, for example, in this $2\times2$ table is indicative of enrichment. Other category analysis methods retain the quantitative gene-level scores and measure significance by referring a category-level statistic to a permutation distribution associated with the original differential expression problem. We describe a class of random-set scoring methods that measure distinct components of the enrichment signal. The class includes Fisher's test based on selected genes and also tests that average gene-level evidence across the category. Averaging and selection methods are compared empirically using Affymetrix data on expression in nasopharyngeal cancer tissue, and theoretically using a location model of differential expression. We find that each method has a domain of superiority in the state space of enrichment problems, and that both methods have benefits in practice. Our analysis also addresses two problems related to multiple-category inference, namely, that equally enriched categories are not detected with equal probability if they are of different sizes, and also that there is dependence among category statistics owing to shared genes. Random-set enrichment calculations do not require Monte Carlo for implementation. They are made available in the R package allez.
---
PDF链接：
https://arxiv.org/pdf/708.435