Addressing Erroneous Scale Assumptions in Microbe and Gene Set Enrichment Analysis

Abstract

By applying Differential Set Analysis (DSA) to sequence count data, researchers can determine whether groups of microbes or genes are differentially enriched. Yet these data lack information about the scale (i.e., size) of the biological system under study, leading some authors to call these data compositional (i.e., proportional). In this article we show that commonly used DSA methods make strong, implicit assumptions about the unmeasured system scale. We show that even small errors in these assumptions can lead to false positive rates as high as 70%. To mitigate this problem, we introduce a sensitivity analysis framework to identify when modeling results are robust to such errors and when they are suspect. Unlike standard benchmarking studies, our methods do not require ground-truth knowledge and can therefore be applied to both simulated and real data.