Clinical microbiome data science with MultiAssayExperiment
Tuomas Borman,Paulina Salminen,Leo M Lahti
University of Turku
Abstract
Because of the complex and high dimensional nature of microbiome profiling data, machine learning and other computational approaches have become an instrumental part of the researcher’s toolkit in this field. There is an increasing need to develop robust and reproducible methods to integrate and analyze taxonomic, functional, and clinical data across multiple sources, such as microbial abundances in the gut with biomolecular profiling data from blood samples. This kind of integrative multi-omic approaches can support the analysis of microbiome dysbiosis and facilitate the discovery of novel biomarkers for health and disease. Efficient data containers, such as the MultiAssayExperiment in R/Bioconductor, can greatly support the development and application of new methods. We provide new, enhanced solutions for multi-assay microbiome data in terms of scalability and the ability to incorporate different types of complementary data sources in reproducible workflows. The merging analysis ecosystem called miaverse (Microbiome analysis universe) utilizes a common, standardized data container, which enables highly optimized integration of multiassay microbiome profiling data from clinical studies. We have developed open data science methods for data analysis and visualization, with a particularly designed support for multi-assay data integration and analysis. Stable versions are available via the Bioconductor network, together with comprehensive online documentation. The framework fulfills the need for open and reproducible analysis of multiassay data in microbiome studies. We anticipate that the framework has the potential to be widely adopted by microbiome researchers. This is further facilitated by the tight links to related application domains, such as single cell sequencing, where closely related open source techniques are now being developed.