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Microbial ecology serves as a foundation for a wide range of scientific andbiomedical studies. Rapidly-evolving high-throughput sequencing technologyenables the comprehensive search for microbial biomarkers using longitudinalexperiments. Such experiments consist of repeated biological observations fromeach subject over time and are essential in accounting for the highbetween-subject and within-subject variability.  Unfortunately, many of the statistical tests based on parametric models relyon correctly specifying temporal dependence structure which is unavailable inmost microbiome data.  In this paper, we propose an extension of the nonparametric bootstrap methodthat enables inference on these types longitudinal data. The proposed movingblock bootstrap (MBB) method accounts for within-subject dependency by usingoverlapping blocks of repeated observations within each subject to draw validinferences based on approximately pivotal statistics. Our simulation studiesshow an increase in power compared to merge-by-subject (MBS) strategies. Wealso show that compared to tests that presume independent samples (PIS), ourproposed method reduces false microbial biomarker discovery rates.  In this paper, we illustrated the MBB method using three different pregnancydata and an oral microbiome data. We provide an open-source R packagehttps://github.com/PratheepaJ/bootLong to make our method accessible and thestudy in this paper reproducible.

The Block Bootstrap Method for Longitudinal Microbiome Data