Elementary methods provide more replicable results in microbial differential abundance analysis
arxiv(2024)
Abstract
Differential abundance analysis is a key component of microbiome studies. It
focuses on the task of assessing the magnitude and statistical significance of
differences in microbial abundances between conditions. While dozens of methods
for differential abundance analysis exist, they have been reported to produce
remarkably discordant results. Currently, there is no consensus on the
preferred methods. While correctness of results in differential abundance
analysis is an ambiguous concept that cannot be evaluated without employing
simulated data, we argue that consistency of results across datasets should be
considered as an essential quality of a well-performing method. We compared the
performance of 13 differential abundance analysis methods employing datasets
from multiple (N = 54) taxonomic profiling studies based on 16S rRNA gene or
shotgun sequencing. For each method, we examined how the results replicated
between random partitions of each dataset and between datasets from independent
studies. While certain methods showed good consistency, some widely used
methods were observed to make a substantial number of conflicting findings.
Overall, the highest consistency without unnecessary reduction in sensitivity
was attained by analyzing total sum scaling (TSS) normalized counts with a
non-parametric method (Wilcoxon test or ordinal regression model) or linear
regression (MaAsLin2). Comparable performance was also attained by analyzing
presence/absence of taxa with logistic regression. In conclusion, while
numerous sophisticated methods for differential abundance analysis have been
developed, elementary methods seem to provide more consistent results without
unnecessarily compromising sensitivity. We therefore suggest that the
elementary methods should be preferred in microbial differential abundance
analysis when replicability needs to be emphasized.
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