Recent evidence suggests that analyzing the presence/absence of taxonomic features can offer a compelling alternative to differential abundance analysis in microbiome studies. However, standard approaches face challenges with boundary cases and multiple testing. To address these challenges, we developed DiPPER (Differential Prevalence via Probabilistic Estimation in R), a method based on Bayesian hierarchical modeling. We benchmarked our method against existing differential prevalence and abundance methods using data from 67 publicly available human gut microbiome studies. We observed considerable variation in performance across methods, with DiPPER outperforming alternatives by combining high sensitivity with effective error control. DiPPER also demonstrated superior replication of findings across independent studies. Furthermore, DiPPER provides differential prevalence estimates and uncertainty intervals that are inherently adjusted for multiple testing.

翻译：近期研究表明，在微生物组研究中分析分类学特征的存在/缺失状态可为差异丰度分析提供一种具有吸引力的替代方案。然而，标准方法在处理边界案例和多重检验时面临挑战。为解决这些问题，我们开发了DiPPER（基于R语言概率估计的差异流行度分析），这是一种基于贝叶斯层次建模的方法。我们利用67项公开的人类肠道微生物组研究数据，将本方法与现有的差异流行度及差异丰度分析方法进行了基准测试。我们观察到不同方法的性能存在显著差异，其中DiPPER通过将高灵敏度与有效的误差控制相结合，表现优于其他方法。DiPPER在独立研究间也展现出更优的结果可重复性。此外，DiPPER提供的差异流行度估计值及其不确定性区间，本身已针对多重检验进行了校正。