A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types

Area of Science:

• Microbiology
• Bioinformatics
• Systems Biology

Background:

• Simultaneous profiling of biospecimens generates diverse data types, including microbial communities, omics, and clinical variables.
• Reduced costs allow for longitudinal studies, aiming to understand host-microbiota interactions and molecular mechanisms.
• Existing analytical frameworks struggle to integrate longitudinal microbiome data with other biological and clinical data types.

Purpose of the Study:

• To develop a generic, data-driven framework for integrating longitudinal multi-omics and clinical data with microbial community data.
• To address challenges in analyzing sparse, compositional microbiome data and unevenly spaced time points.
• To identify key temporal features associated with host-microbiota interactions.

Main Methods:

• A data-driven framework involving filtering, modeling, and integration using smoothing splines.
• Application of multivariate dimension reduction methods for data analysis.
• Illustration of the framework on multi-omics case studies from bioreactor experiments and human studies.

Main Results:

• The proposed framework effectively integrates diverse longitudinal data types, including microbiome data.
• Key temporal features with strong associations within sample groups were identified.
• The framework demonstrated utility in analyzing complex biological systems.

Conclusions:

• The developed framework provides a robust approach for integrated analysis of longitudinal microbiome and multi-omics data.
• It offers a solution to the analytical challenges posed by microbiome-derived data.
• This approach facilitates a deeper understanding of host-microbiota interactions and their role in biological mechanisms.