In a variety of application areas, there is interest in assessing evidence of differences in the intensity of event realizations between groups. For example, in cancer genomic studies collecting data on rare variants, the focus is on assessing whether and how the variant profile changes with the disease subtype. Motivated by this application, we develop multiresolution nonparametric Bayes tests for differential mutation rates across groups. The multiresolution approach yields fast and accurate detection of spatial clusters of rare variants, and our nonparametric Bayes framework provides great flexibility for modeling the intensities of rare variants. Some theoretical properties are also assessed, including weak consistency of our Dirichlet Process-Poisson-Gamma mixture over multiple resolutions. Simulation studies illustrate excellent small sample properties relative to competitors, and we apply the method to detect rare variants related to common variable immunodeficiency from whole exome sequencing data on 215 patients and over 60,027 control subjects.

翻译：摘要：在多种应用领域中，评估事件实现强度在不同组间差异的证据备受关注。例如，在收集罕见变异数据的癌症基因组研究中，重点在于评估变异谱是否以及如何随疾病亚型而变化。受此应用启发，我们开发了用于跨组差异突变率的多分辨率无参数贝叶斯检验。多分辨率方法能够快速准确地检测罕见变异的空间聚类，而无参数贝叶斯框架为罕见变异强度的建模提供了极大的灵活性。此外，我们还评估了一些理论性质，包括狄利克雷过程-泊松-伽马混合模型在多个分辨率下的弱一致性。模拟研究表明，与竞争方法相比，该方法在小样本条件下具有优越的性能，并将其应用于从215名患者和超过60,027名对照受试者的全外显子测序数据中检测与常见变异型免疫缺陷相关的罕见变异。