Mathematical-based microbiome analytics for clinical translation

Traditionally, human microbiology has been strongly built on the laboratory focused culture of microbes isolated from human specimens in patients with acute or chronic infection. These approaches primarily view human disease through the lens of a single species and its relevant clinical setting however such approaches fail to account for the surrounding environment and wide microbial diversity that exists in vivo. Given the emergence of next generation sequencing technologies and advancing bioinformatic pipelines, researchers now have unprecedented capabilities to characterise the human microbiome in terms of its taxonomy, function, antibiotic resistance and even bacteriophages. Despite this, an analysis of microbial communities has largely been restricted to ordination, ecological measures, and discriminant taxa analysis. This is predominantly due to a lack of suitable computational tools to facilitate microbiome analytics. In this review, we first evaluate the key concerns related to the inherent structure of microbiome datasets which include its compositionality and batch effects. We describe the available and emerging analytical techniques including integrative analysis, machine learning, microbial association networks, topological data analysis (TDA) and mathematical modelling. We also present how these methods may translate to clinical settings including tools for implementation. Mathematical based analytics for microbiome analysis represents a promising avenue for clinical translation across a range of acute and chronic disease states.Ministry of Health (MOH)Nanyang Technological UniversityNational Medical Research Council (NMRC)Published versionThis research is supported by the Singapore Ministry of Health’s National Medical Research Council under its Clinician-Scientist Individual Research Grant (CS-IRG) (MOH-000141) (S.H.C) and Clinician Scientist Award (CSA) (MOH-000710) (S.H.C). It is also supported by the NTU Integrated Medical, Biological and Environmental Life Sciences (NIMBELS), Nanyang Technological University, Singapore [NIM/03/2018] (S.H.C). KTA gratefully acknowledges the financial support of the EPSRC via grant EP/T017856/1