Sisk-Hackworth Laura, Kelley Scott T
Department of Biology, San Diego State University, San Diego, CA 92182, USA.
NAR Genom Bioinform. 2020 Oct 2;2(4):lqaa079. doi: 10.1093/nargab/lqaa079. eCollection 2020 Dec.
Compositional data analysis (CoDA) methods have increased in popularity as a new framework for analyzing next-generation sequencing (NGS) data. CoDA methods, such as the centered log-ratio (clr) transformation, adjust for the compositional nature of NGS counts, which is not addressed by traditional normalization methods. CoDA has only been sparsely applied to NGS data generated from microbial communities or to multiple 'omics' datasets. In this study, we applied CoDA methods to analyze NGS and untargeted metabolomic datasets obtained from bacterial and fungal communities. Specifically, we used clr transformation to reanalyze NGS amplicon and metabolomics data from a study investigating the effects of building material type, moisture and time on microbial and metabolomic diversity. Compared to analysis of untransformed data, analysis of clr-transformed data revealed novel relationships and stronger associations between sample conditions and microbial and metabolic community profiles.
作为一种分析下一代测序（NGS）数据的新框架，成分数据分析（CoDA）方法越来越受欢迎。CoDA方法，如中心对数比（clr）转换，可针对NGS计数的成分性质进行调整，而传统归一化方法并未涉及这一点。CoDA仅稀疏地应用于从微生物群落生成的NGS数据或多个“组学”数据集。在本研究中，我们应用CoDA方法分析从细菌和真菌群落获得的NGS和非靶向代谢组学数据集。具体而言，我们使用clr转换重新分析了一项研究中的NGS扩增子和代谢组学数据，该研究调查了建筑材料类型、湿度和时间对微生物和代谢组学多样性的影响。与未转换数据的分析相比，clr转换数据的分析揭示了样本条件与微生物和代谢群落概况之间的新关系和更强的关联。
