The correct identification of differentially abundant microbial taxa between experimental conditions is a methodological and computational challenge. Recent work has produced methods to deal with the high sparsity and compositionality characteristic of microbiome data, but independent benchmarks comparing these to alternatives developed for RNA-seq data analysis are lacking. Here, we compare methods developed for single cell, bulk RNA-seq, and microbiome data, in terms of suitability of distributional assumptions, ability to control false discoveries, concordance, and power. We benchmark these methods using 100 manually curated datasets from 16S and whole metagenome shotgun sequencing. The multivariate and compositional methods developed specifically for microbiome analysis did not outperform univariate methods developed for differential expression analysis of RNA-seq data. We recommend a careful exploratory data analysis prior to application of any inferential model and we present a framework to help scientists make an informed choice of analysis methods in a dataset-specific manner.

中文翻译：

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从单细胞，大量RNA-seq和宏基因组学应用于微生物组数据的统计方法评估

在实验条件之间正确识别差异丰富的微生物分类群是方法和计算上的挑战。最近的工作已经产生了解决微生物组数据的高稀疏性和组成特征的方法，但是缺乏将它们与为RNA-seq数据分析开发的替代方案进行比较的独立基准。在这里，我们根据分布假设的适用性，控制错误发现的能力，一致性和能力，比较为单细胞，大量RNA-seq和微生物组数据开发的方法。我们使用来自16S的100个手动策划的数据集和整个元基因组shot弹枪测序对这些方法进行基准测试。专为微生物组分析而开发的多元和组成方法并没有优于为RNA-seq数据的差异表达分析而开发的单变量方法。我们建议在应用任何推论模型之前进行仔细的探索性数据分析，并提出一个框架，以帮助科学家以特定于数据集的方式明智地选择分析方法。