Abstract
Concerns have been raised about the use of relative abundance data derived from next generation sequencing as a proxy for absolute abundances. For example, in the differential abundance setting, compositional effects in relative abundance data may give rise to spurious differences (false positives) when considered from the absolute perspective. In practice however, relative abundances are often transformed by renormalization strategies intended to compensate for these effects and the scope of the practical problem remains unclear. We used simulated data to explore the consistency of differential abundance calling on renormalized relative abundances versus absolute abundances and find that, while overall consistency is high, with median sensitivities (true positive rates) and specificities (1 - false positive rates) each of around 0.90, consistency can be much lower where there is widespread change in the abundance of features across conditions. We confirm these findings on a large number of real data sets drawn from 16S metabarcoding, expression array, bulk RNA-seq, and single-cell RNA-seq experiments, where data sets with the greatest change between experimental conditions are also those with the highest false positive rates. Finally, we evaluate the predictive utility of summary features of relative abundance data themselves. Estimates of sparsity and the prevalence of feature-level change in relative abundance data give accurate predictions of discrepancy in differential abundance calling in simulated data and provide useful bounds for worst-case outcomes in real data.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Results and conclusions updated to reflect a significant change in the analysis. The previous version of this manuscript reported on the consistency of differential abundance calls made by methods of interest on relative versus absolute count data (i.e. a method-versus-self comparison). This version compares calls made by methods of interest on relative count data to a pseudo-ground truth (i.e. a method-versus-oracle comparison).