Benchmarking differential abundance analysis methods for correlated microbiome sequencing data
Benchmarking differential abundance analysis methods for correlated microbiome sequencing data
复制标题
相关微生物组测序数据差异丰度分析方法的基准测试
DOI：
10.1093/bib/bbac607
复制
发表时间：
2023
影响因子：
9.5
通讯作者：
Chen, Jun
中科院分区：
文献类型：
--
作者：
Yang, Lu;Chen, Jun
差异丰度分析（DAA）是微生物组数据分析中的一项中心统计任务。一个强大而强大的DAA工具可以帮助识别高度可靠的微生物候选物，以进行进一步的生物学验证。目前的微生物组研究经常从不同的微生物组采样方案（如空间和时间采样）生成相关样本。在过去的十年中，已经提出了许多用于相关微生物组数据（DAA-c）的DAA工具。令人不安的是，不同的DAA-c工具有时会产生非常不一致的结果。为了向现场推荐最佳实践，我们使用基于真实的数据的模拟对现有DAA-c工具进行了首次全面评估。总体而言，基于线性模型的方法琳达，MaAsLin 2和LDM比基于广义线性模型的方法更稳健。琳达方法是在存在强成分效应的情况下保持合理性能的唯一方法。
Differential abundance analysis (DAA) is one central statistical task in microbiome data analysis. A robust and powerful DAA tool can help identify highly confident microbial candidates for further biological validation. Current microbiome studies frequently generate correlated samples from different microbiome sampling schemes such as spatial and temporal sampling. In the past decade, a number of DAA tools for correlated microbiome data (DAA-c) have been proposed. Disturbingly, different DAA-c tools could sometimes produce quite discordant results. To recommend the best practice to the field, we performed the first comprehensive evaluation of existing DAA-c tools using real data-based simulations. Overall, the linear model-based methods LinDA, MaAsLin2 and LDM are more robust than methods based on generalized linear models. The LinDA method is the only method that maintains reasonable performance in the presence of strong compositional effects.