IFAA: Robust association identification and Inference For Absolute Abundance in microbiome analyses

TL;DR

This paper introduces IFAA, a new method for robustly inferring absolute abundances in microbiome data, overcoming issues caused by relative abundance calculations and library size effects, with demonstrated superior performance.

Contribution

The paper presents IFAA, a novel approach that accurately infers absolute abundances in microbiome studies, addressing compositional and library size confounding issues.

Findings

IFAA outperforms existing methods in simulations.

It effectively handles zero-inflated data.

Demonstrated robustness to library size confounding.

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 confounding effect of library size 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 confounding effect of library size.