Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods

Published 2021

Summary

The diet plays a major role in shaping gut microbiome composition and function in both
humans and animals, and dietary intervention trials are often used to investigate and understand
these effects. A plethora of statistical methods for analysing the differential abundance
of microbial taxa exists, and new methods are constantly being developed, but there
is a lack of benchmarking studies and clear consensus on the best multivariate statistical
practices. This makes it hard for a biologist to decide which method to use. We compared
the outcomes of generic multivariate ANOVA (ASCA and FFMANOVA) against statistical
methods commonly used for community analyses (PERMANOVA and SIMPER) and methods
designed for analysis of count data from high-throughput sequencing experiments
(ALDEx2, ANCOM and DESeq2). The comparison is based on both simulated data and five
published dietary intervention trials representing different subjects and study designs. We
found that the methods testing differences at the community level were in agreement
regarding both effect size and statistical significance. However, the methods that provided
ranking and identification of differentially abundant operational taxonomic units (OTUs)
gave incongruent results, implying that the choice of method is likely to influence the biological
interpretations. The generic multivariate ANOVA tools have the flexibility needed for
analysing multifactorial experiments and provide outputs at both the community and OTU
levels; good performance in the simulation studies suggests that these statistical tools are
also suitable for microbiome data sets.