Fisher-Pitman Permutation Tests Based on Nonparametric Poisson Mixtures with Application to Single Cell Genomics
Year: 2022
Type: dataset
Abstract: 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.
Source: Figshare
Institutions: University of Washington, Capital Medical University, China Medical University, Chinese Academy of Medical Sciences & Peking Union Medical College, Huazhong Agricultural University, Peking Union Medical College Hospital, Fred Hutch Cancer Center, Peking University Cancer Hospital, Shandong Provincial Hospital, Anyang Normal University, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, First Affiliated Hospital of Xi'an Jiaotong University, First Hospital of China Medical University, China Academy of Chinese Medical Sciences, Second Affiliated Hospital of Anhui Medical University, Jiangsu Province Hospital, University of North Carolina System, Huazhong University of Science and Technology, Nanjing Medical University, China Medical University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4394404073