About this Research Topic
One of the primary limitations to leveraging this large body of ‘big data’ is computational and statistical. Among these are the technical nature of the data associated: high-dimensionality, count and compositional data structure, sparsity (zero-inflation), over-dispersion, and hierarchical, spatial, and temporal dependence, among others. To combat these challenges, specialized methods and software are needed to accurately characterize microbial communities within and across large studies, while maintaining both statistical rigor and biological relevance.
This Research Topic thus focuses on studies (e.g. original research, perspectives, reviews, commentaries, and opinion papers) that investigate and discuss novel experimental design and downstream biostatistical considerations for integrated analysis of microbial community multi-omics profiles (16S amplicon, metagenomics, metatranscriptomics, metaproteomics, metabolomics, and other culture-independent molecular data). We believe this topic is both timely and fundamental for improving our current understanding of the microbiome. The diverse collection of articles on this topic will (i) provide a useful reference for both current and future investigators in translational and clinical microbiome research, and (ii) establish best practice guidelines for analyzing and integrating microbial multi-omics data, including but not limited to:
• biologically informed strain- or species-level ecological interaction discovery
• meta-analysis for batch effect correction and population structure discovery
• integrative analysis for precision medicine
• longitudinal and time-series analyses
• machine learning methods for predictive analyses
Keywords: Microbial Ecology, Metagenomics, Metabolomics, Metatranscriptomics, Metaproteomics
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.