Abstract

The target of inference in microbiome analyses is usually relative abundance (RA) because RA in a sample (e.g., stool) can be considered as an approximation of RA in an entire ecosystem (e.g., gut). However, inference on RA suffers from the fact that RA are calculated by dividing absolute abundances (AA) over the common denominator (CD), the summation of all AA (i.e., library size). Because of that, perturbation in one taxon will result in a change in the CD and thus cause false changes in RA of all other taxa, and those false changes could lead to false positive/negative findings. We propose a novel analysis approach (IFAA) to make robust inference on AA of an ecosystem that can circumvent the issues induced by the CD problem and compositional structure of RA. IFAA can also address the issues of overdispersion and handle zero-inflated data structures. IFAA identifies microbial taxa associated with the covariates in Phase one and estimates the association parameters by employing an independent reference taxon in Phase two. Two real data applications are presented and extensive simulations show that IFAA outperforms other established existing approaches by a big margin in the presence of unbalanced library size.

摘要

微生物组分析的推断目标通常是相对丰度（RA），因为样品（例如粪便）中的RA可以被视为整个生态系统（例如肠道）中RA的近似值。然而，对RA的推论的事实是RA是通过将绝对丰度（AA）除以公共分母（CD）来计算的，即所有AA的总和（即库大小）。因此，对一个分类单元的扰动将导致CD发生变化，从而导致所有其他分类单元的RA发生错误的变化，而这些错误的变化可能导致错误的阳性/阴性结果。我们提出了一种新颖的分析方法（IFAA），可以对生态系统的AA做出可靠的推断，从而可以规避CD问题和RA的组成结构引起的问题。IFAA还可以解决过度分散的问题，并处理零膨胀数据结构。IFAA在第一阶段识别与协变量相关的微生物分类单元，并在第二阶段通过采用独立的参考分类单元来估计关联参数。提出了两个实际的数据应用程序，并且大量的模拟显示，在不平衡的库大小存在的情况下，IFAA大大优于其他已建立的现有方法。