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 sequence count data suffer from a scale limitation: 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 that rely on normalization make strong, implicit assumptions about the unmeasured system scale. We show that even small errors in these scale assumptions can lead to positive predictive values as low as 9%. To address this problem, we take three novel approaches. First, we introduce a sensitivity analysis framework to identify when modeling results are rob

SUBMITTER: McGovern KC 

PROVIDER: S-EPMC10695402 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature