随机集方法识别基因集分析中富集信号的不同方面
Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis
摘要：
对于相对于细胞的两种或更多种状态具有改变的表达水平的基因，可以在不同程度上富集预先指定的基因组。了解功能类别定义的基因集的富集，如基因本体（GO）注释，对于分析微阵列表达数据中的生物信号是有价值的。测量富集的一种常见方法是根据功能类别中的成员资格和选定的显着改变的基因列表中的成员资格对基因进行交叉分类。例如，在该$2\times2$表中，小的Fisher精确检验$p$值指示富集。其他类别分析方法保留定量基因水平分数，并通过将类别水平统计量引用到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的不同组分的随机集评分方法。这门课包括基于选定基因的Fisher检验，也包括对整个类别的平均基因水平证据的检验。平均和选择方法进行了比较经验上使用的Affyoung数据在鼻咽癌组织中的表达，并在理论上使用的差异表达的定位模型。我们发现，每种方法都有一个域的优势，在状态空间的丰富问题，这两种方法在实践中有好处。我们的分析还解决了两个问题相关的多类推理，即，同样丰富的类别没有检测到相同的概率，如果它们是不同的大小，也有依赖性的类别统计由于共享的基因。随机集富集计算不需要蒙特卡罗来实现。它们在R包allez中提供。
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.
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