Wang Mukai, Fontaine Simon, Jiang Hui, Li Gen
Department of Biostatistics, University of Michigan, Ann Arbor, 48109, MI, USA.
Department of Statistics, University of Michigan, Ann Arbor, 48109, MI, USA.
bioRxiv. 2024 May 17:2024.05.14.594186. doi: 10.1101/2024.05.14.594186.
Microbiome differential abundance analysis remains a challenging problem despite multiple methods proposed in the literature. The excessive zeros and compositionality of metagenomics data are two main challenges for differential abundance analysis. We propose a novel method called "analysis of differential abundance by pooling Tobit models" (ADAPT) to overcome these two challenges. ADAPT uniquely treats zero counts as left-censored observations to facilitate computation and enhance interpretation. ADAPT also encompasses a theoretically justified way of selecting non-differentially abundant microbiome taxa as a reference for hypothesis testing. We generate synthetic data using independent simulation frameworks to show that ADAPT has more consistent false discovery rate control and higher statistical power than competitors. We use ADAPT to analyze 16S rRNA sequencing of saliva samples and shotgun metagenomics sequencing of plaque samples collected from infants in the COHRA2 study. The results provide novel insights into the association between the oral microbiome and early childhood dental caries.
尽管文献中提出了多种方法，但微生物组差异丰度分析仍然是一个具有挑战性的问题。宏基因组学数据中过多的零值和组成性是差异丰度分析的两个主要挑战。我们提出了一种名为“通过合并托比特模型进行差异丰度分析”（ADAPT）的新方法来克服这两个挑战。ADAPT独特地将零计数视为左删失观测值，以方便计算并增强解释力。ADAPT还包含一种从理论上合理的方法，用于选择非差异丰富的微生物组分类群作为假设检验的参考。我们使用独立的模拟框架生成合成数据，以表明ADAPT在控制错误发现率方面比竞争对手更一致，并且具有更高的统计功效。我们使用ADAPT分析了COHRA2研究中收集的婴儿唾液样本的16S rRNA测序和菌斑样本的鸟枪法宏基因组学测序。结果为口腔微生物组与幼儿龋齿之间的关联提供了新的见解。
