A broken promise: microbiome differential abundance methods do not control the false discovery rate
A broken promise: microbiome differential abundance methods do not control the false discovery rate
复制标题
DOI：
10.1093/bib/bbx104
复制
发表时间：
2019-01-01
影响因子：
9.5
通讯作者：
Thas, Olivier
中科院分区：
文献类型：
--
作者：
Hawinkel, Stijn;Mattiello, Federico;Thas, Olivier
高通量测序技术可以很容易地描述人类微生物组的特征，但分析微生物组数据的统计方法仍处于初级阶段。差异丰度法旨在检测细菌物种丰度与受试者分组因素之间的关联。这些方法的结果对于确定微生物组作为预后或诊断生物标志物或证明前药或抗生素药物的疗效是重要的。由于在微生物组领域缺乏标杆研究，对统计方法的性能没有达成共识。我们通过广泛的参数和非参数模拟以及真实的数据洗牌算法对大量流行的方法进行了比较。结果在不同的方法上是一致的，并且都表明错误发现的数量惊人地多。这引发了人们对过去研究中发现的可靠性的极大怀疑，并危及微生物组实验的重复性。为了进一步改进方法基准，我们引入了一个新的模拟工具，该工具允许在任何单变量计数分布之后生成相关计数数据；相关结构可以从实际数据中推断出来。大多数模拟研究放弃了物种之间的相关性，但我们的结果表明，这种相关性会对统计方法的性能产生负面影响。
High-throughput sequencing technologies allow easy characterization of the human microbiome, but the statistical methods to analyze microbiome data are still in their infancy. Differential abundance methods aim at detecting associations between the abundances of bacterial species and subject grouping factors. The results of such methods are important to identify the microbiome as a prognostic or diagnostic biomarker or to demonstrate efficacy of prodrug or antibiotic drugs. Because of a lack of benchmarking studies in the microbiome field, no consensus exists on the performance of the statistical methods. We have compared a large number of popular methods through extensive parametric and nonparametric simulation as well as real data shuffling algorithms. The results are consistent over the different approaches and all point to an alarming excess of false discoveries. This raises great doubts about the reliability of discoveries in past studies and imperils reproducibility of microbiome experiments. To further improve method benchmarking, we introduce a new simulation tool that allows to generate correlated count data following any univariate count distribution; the correlation structure may be inferred from real data. Most simulation studies discard the correlation between species, but our results indicate that this correlation can negatively affect the performance of statistical methods.