Integrative Analysis of Microbial 16S Gene and Shotgun Metagenomic Sequencing Data Improves Statistical Efficiency in Testing Differential Abundance
提出Com-2seq方法，整合16S和鸟枪法宏基因组测序数据，克服实验偏差和样本重叠问题，在属水平和群落水平上更有效地检测微生物差异丰度，并在真实数据中发现与糖尿病前期相关的菌属。
The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Surprisingly, many microbiome studies have performed both experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, suggesting that an integrative analysis of both datasets could enhance the testing power for these signatures. However, differential experimental biases, partially overlapping samples, and uneven library sizes pose tremendous challenges when combining the two datasets. In this article, we introduce the first method of this kind, named Com-2seq, that combines the two datasets for testing differential abundance at the genus level as well as the community level while overcoming these difficulties. Our simulation studies demonstrate that Com-2seq substantially enhances statistical efficiency over analysis of a single dataset and outperforms two ad hoc approaches to integrative analysis. In analysis of real microbiome data, Com-2seq uncovered scientifically plausible findings, namely, the association of Butyrivibrio, Gemella and Ignavigranum with prediabetes status, which would have been missed by analyzing a single dataset. Butyrivibrio failed to reach the significance level in the analysis of each dataset despite showing a consistent trend; Gemella and Ignavigranum failed to produce adequate data in the 16S experiment. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.