Methodological Considerations in Longitudinal Analyses of Microbiome Data: A Comprehensive Review

Area of Science:

• Microbiome research
• Computational biology
• Data science

Background:

• Biological processes are dynamic and best understood over time.
• Longitudinal microbiome data analysis presents unique challenges.
• Understanding host-microbiome dynamics is key to health and disease.

Purpose of the Study:

• To review and compare methods for time-series microbiome data analysis.
• To address challenges in handling longitudinal microbiome data.
• To highlight the importance of computational tools in microbiome research.

Main Methods:

• Comprehensive review of existing literature on microbiome time-series analysis.
• Comparison of preprocessing and downstream analysis methods.
• Discussion of differential analysis, clustering, network inference, and trait classification.

Main Results:

• Identified challenges in time-series microbiome data analysis.
• Compared various available computational approaches and methods.
• Emphasized the utility of appropriate analytical tools.

Conclusions:

• Appropriate handling of longitudinal microbiome data provides insights into microbial roles and interactions.
• Computational tools are vital for understanding dynamic host-microbiome relationships.
• This analysis aids in understanding health, disease, and developmental phases through microbiome dynamics.