基于最近邻的非参数检验在异质单细胞蛋白质组数据中的病毒重构
A Nearest-Neighbor Based Nonparametric Test for Viral Remodeling in Heterogeneous Single-Cell Proteomic Data
摘要：
基于单细胞蛋白质表达数据的当代免疫学研究中的一个重要问题是确定细胞表达是否在病原体感染后重塑。检测这种变化的一种自然方法是使用非参数双样本统计检验。然而，在单细胞研究中，直接应用这些测试通常是不够的，因为来自未感染群体的单细胞水平表达数据通常包含具有高度异质性特征的几个潜伏亚群的属性。因此，病毒通常以不同的速率感染这些不同的亚群，在这种情况下，用于检查分布相似性的传统非参数双样本检验不再保守。我们提出了一种新的非参数方法，用于测试在异质性下的重塑（TRUH），可以准确地检测到受感染的样品相比，可能异质未感染的样品的变化。我们的测试框架是基于复合空值，旨在允许空值模型包含受感染样本（尽管未被病毒改变）可能主要来自基线数据中代表性不足的亚群的可能性。TRUH统计，它使用最近邻的感染样本到基线未感染的人口的预测，校准使用一种新的bootstrap算法。通过仿真实验证明了该检验的非渐近性，并推导出了检验统计量的大样本限，为检验的一致渐近校准提供了理论支持.我们使用TRUH统计量来研究不同类型的HIV感染下扁桃体T细胞的重塑，发现与传统的测试不同，基于TRUH的统计推断符合生物学验证的HIV感染免疫学理论。
英文摘要：
An important problem in contemporary immunology studies based on single-cell protein expression data is to determine whether cellular expressions are remodeled post infection by a pathogen. One natural approach for detecting such changes is to use non-parametric two-sample statistical tests. However, in single-cell studies, direct application of these tests is often inadequate because single-cell level expression data from uninfected populations often contains attributes of several latent sub-populations with highly heterogeneous characteristics. As a result, viruses often infect these different sub-populations at different rates in which case the traditional nonparametric two-sample tests for checking similarity in distributions are no longer conservative. We propose a new nonparametric method for Testing Remodeling Under Heterogeneity (TRUH) that can accurately detect changes in the infected samples compared to possibly heterogeneous uninfected samples. Our testing framework is based on composite nulls and is designed to allow the null model to encompass the possibility that the infected samples, though unaltered by the virus, might be dominantly arising from under-represented sub-populations in the baseline data. The TRUH statistic, which uses nearest neighbor projections of the infected samples into the baseline uninfected population, is calibrated using a novel bootstrap algorithm. We demonstrate the non-asymptotic performance of the test via simulation experiments and derive the large sample limit of the test statistic, which provides theoretical support towards consistent asymptotic calibration of the test. We use the TRUH statistic for studying remodeling in tonsillar T cells under different types of HIV infection and find that unlike traditional tests, TRUH based statistical inference conforms to the biologically validated immunological theories on HIV infection.
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