Fisher-Pitman Permutation Tests Based on Nonparametric Poisson Mixtures with Application to Single Cell Genomics

• 成果类型: Article
• 署名作者: Miao, Zhen; Kong, Weihao; Vinayak, Ramya Korlakai; Sun, Wei; Han, Fang
• 署名单位: University of Washington; University of Washington Seattle; Alphabet Inc.; Google Incorporated; University of Wisconsin System; University of Wisconsin Madison; Fred Hutchinson Cancer Center
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2022.2120401
• 发表日期: 2024
• 页码: 394-406
• 关键词: maximum likelihood estimation; large-sample power; mixing densities; F-TEST; Minimax Estimation; autism; Consistency; algorithm; CONVERGENCE; estimators

摘要

This article investigates the theoretical and empirical performance of Fisher-Pitman-type permutation tests for assessing the equality of unknown Poisson mixture distributions. Building on nonparametric maximum likelihood estimators (NPMLEs) of the mixing distribution, these tests are theoretically shown to be able to adapt to complicated unspecified structures of count data and also consistent against their corresponding ANOVA-type alternatives; the latter is a result in parallel to classic claims made by Robinson. The studied methods are then applied to a single-cell RNA-seq data obtained from different cell types from brain samples of autism subjects and healthy controls; empirically, they unveil genes that are differentially expressed between autism and control subjects yet are missed using common tests. For justifying their use, rate optimality of NPMLEs is also established in settings similar to nonparametric Gaussian (Wu and Yang) and binomial mixtures (Tian, Kong, and Valiant; Vinayak et al.). Supplementary materials for this article are available online.