parafac4microbiome: exploratory analysis of longitudinal microbiome data using parallel factor analysis

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• Longitudinal microbiome studies generate complex, multi-dimensional data.
• Traditional methods like principal component analysis often fail to capture temporal dynamics effectively.
• Multi-way analysis methods offer a more suitable framework for structured microbiome data.

Purpose of the Study:

• To demonstrate the utility of parallel factor analysis (PARAFAC) for exploring longitudinal microbiome data.
• To showcase PARAFAC's ability to identify temporal patterns and structural variations.
• To provide an accessible R package for applying PARAFAC to microbiome research.

Main Methods:

• Application of parallel factor analysis (PARAFAC) to three distinct longitudinal microbiome datasets.
• Data organized as a three-way array: subjects, microbial abundances, and time points.
• Post-hoc clustering applied for microbial subcommunity identification.

Main Results:

• PARAFAC successfully identified primary time-resolved variations in an in vitro microbiome study.
• The method distinguished subject groups and improved comparative analysis in a longitudinal infant gut microbiome study, even with missing data.
• PARAFAC facilitated the identification of microbial subcommunities in a gingivitis intervention study of the oral microbiome.

Conclusions:

• PARALLEL FACTOR ANALYSIS (PARAFAC) is a powerful and versatile method for analyzing longitudinal microbiome data across diverse environments.
• The approach effectively captures temporal and structural patterns, outperforming traditional methods.
• The parafac4microbiome R package provides accessible tools for researchers to apply PARAFAC in their own studies.