Microbiome data science in the SummarizedExperiment universe
Felix G.M. Ernst,Tuomas Borman,Sudarshan Shetty,Ruizhu Huang,Domenick James Braccia,Hector Bravo,Leo M Lahti University of Turku
Abstract
Recent developments in Bioconductor classes and methods have opened up new opportunities for microbiome data science. Contemporary microbiome research needs support for hierarchies in the sample and phylogenetic feature spaces, for integrating multiple data types, for analysing spatio-temporal variation, and for general performance optimization. Whereas many promising techniques for such purposes are readily available through the Bioconductor SummarizedExperiment universe, they have been largely underutilized in microbiome data science. The specific properties of microbiome taxonomic and functional profiling data need to be carefully considered in order to take full advantage of these opportunities, and coordinated effort between multiple research teams is needed. By linking microbiome data more tightly to other already established Bioconductor classes, we hope to improve the interoperability of available tools and ensure the long-term sustainability of the ecosystem. We demonstrate the potential of SummarizedExperiment and its derivatives in microbiome research and discuss the latest developments in the R/Bioconductor microbiome data science ecosystem.
Keywords: microbiome,data integration,hierarchical data,phylogenetic trees,SummarizedExperiment