Parafac4microbiome: exploratory analysis of longitudinal microbiome data using parallel factor analysis.

ABSTRACT: Studies investigating microbial temporal dynamics are increasingly common, leveraging longitudinal designs that collect microbial abundance data across multiple time points from the same subjects. Traditional exploratory approaches like principal component analysis fail to fully utilize this structure. By organizing data as a three-way array-subjects as rows, microbial abundances as columns, and time points as the third dimension-multi-way methods such as parallel factor analysis (PARAFAC) can better capture temporal and structural patterns. This study demonstrates PARAFAC as a method to explore longitudinal microbiome data using three exemplary studies. In the first example, a long-time series of in vitro microbiomes, PARAFAC identifies primary time-resolved variations. The second

SUBMITTER: van der Ploeg GR 

PROVIDER: S-EPMC12172464 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature