Proportionality-based association metrics in count compositional data

doi:10.1093/nargab/lqag102

2026-09-04

DOI：10.1093/nargab/lqag102

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组成数据由描述整体构成部分的向量组成。源自各种组学平台（如16S和RNA测序）的数据具有组成性质。在这类数据中，特征在原始计数上的相关性没有实际意义。比例性度量被提出以解决此问题。然而，由于读深度存在变异性，当这些度量基于计数数据进行经验计算时，会产生固有偏差。我们量化了在计数数据中经验计算比例性相关度量所引入的偏差。此外，我们提出了一种在logit-正态多项式模型内估计这些度量的方法，以追求更准确的估计。模型估计值在模拟数据中优于经验估计值，并被应用于小鼠胚胎干细胞单细胞测序数据集以及儿童发病型多发性硬化的元基因组数据集。

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