免疫学成分数据的统计分析指南
A Guideline for the Statistical Analysis of Compositional Data in Immunology
摘要：
免疫细胞组成的研究由于产生多个大规模数据而在免疫学中具有极大的科学兴趣。从统计学的角度来看，这种免疫细胞数据应被视为组成。在组成数据中，每个元素都是正的，所有元素的总和是一个常数，通常可以设置为1。标准的统计方法不能直接应用于成分数据的分析，因为它们不能适当地处理成分元素之间的相关性。在本文中，我们回顾了成分数据分析的统计方法，并在免疫学的背景下说明它们。具体而言，我们专注于回归分析使用对数比变换和广义线性模型与Dirichlet分布，讨论其理论基础，并说明其应用与免疫细胞分数数据产生的结直肠癌患者。
英文摘要：
The study of immune cellular composition has been of great scientific interest in immunology because of the generation of multiple large-scale data. From the statistical point of view, such immune cellular data should be treated as compositional. In compositional data, each element is positive, and all the elements sum to a constant, which can be set to one in general. Standard statistical methods are not directly applicable for the analysis of compositional data because they do not appropriately handle correlations between the compositional elements. In this paper, we review statistical methods for compositional data analysis and illustrate them in the context of immunology. Specifically, we focus on regression analyses using log-ratio transformations and the generalized linear model with Dirichlet distribution, discuss their theoretical foundations, and illustrate their applications with immune cellular fraction data generated from colorectal cancer patients.
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