An important data analysis task in statistical genomics involves the integration of genome-wide gene-level measurements with preexisting data on the same genes. A wide variety of statistical methodologies and computational tools have been developed for this general task. We emphasize one particular distinction among methodologies, namely whether they process gene sets one at a time (uniset) or simultaneously via some multiset technique. Owing to the complexity of collections of gene sets, the multiset approach offers some advantages, as it naturally accommodates set-size variations and among-set overlaps. However, this approach presents both computational and inferential challenges. After reviewing some statistical issues that arise in uniset analysis, we examine two model-based multiset methods for gene list data.

中文翻译：

---

基因组分析的多集统计。

统计基因组学中的一项重要数据分析任务涉及将全基因组基因水平的测量结果与相同基因的现有数据进行整合。针对这一一般任务，已经开发了各种各样的统计方法和计算工具。我们强调方法学之间的一个特殊区别，即它们是一次（单一集）还是通过某种多集技术同时处理一个基因集。由于基因集集合的复杂性，多集方法具有一些优势，因为它自然地适应了集大小的变化和集之间的重叠。但是，这种方法既带来了计算上的挑战，也带来了推论上的挑战。在回顾了在单集分析中出现的一些统计问题之后，我们研究了两种基于模型的基因列表数据的多集方法。