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• W3091240408 abstract "Abstract 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." @default.
• W3091240408 created "2020-10-08" @default.
• W3091240408 creator A5049973896 @default.
• W3091240408 creator A5049981216 @default.
• W3091240408 date "2020-10-02" @default.
• W3091240408 modified "2023-10-09" @default.
• W3091240408 title "An application of compositional data analysis to multiomic time-series data" @default.
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• W3091240408 doi "https://doi.org/10.1093/nargab/lqaa079" @default.
• W3091240408 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7671389" @default.
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• W3091240408 hasPublicationYear "2020" @default.
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