The Block Bootstrap Method for Longitudinal Microbiome Data

Microbial ecology serves as a foundation for a wide range of scientific and biomedical studies. Rapidly-evolving high-throughput sequencing technology enables the comprehensive search for microbial biomarkers using longitudinal experiments. Such experiments consist of repeated biological observations from each subject over time and are essential in accounting for the high between-subject and within-subject variability. Unfortunately, many of the statistical tests based on parametric models rely on correctly specifying temporal dependence structure which is unavailable in most microbiome data. In this paper, we propose an extension of the nonparametric bootstrap method that enables inference on these types longitudinal data. The proposed moving block bootstrap (MBB) method accounts for within-subject dependency by using overlapping blocks of repeated observations within each subject to draw valid inferences based on approximately pivotal statistics. Our simulation studies show an increase in power compared to merge-by-subject (MBS) strategies. We also show that compared to tests that presume independent samples (PIS), our proposed method reduces false microbial biomarker discovery rates. In this paper, we illustrated the MBB method using three different pregnancy data and an oral microbiome data. We provide an open-source R package https://github.com/PratheepaJ/bootLong to make our method accessible and the study in this paper reproducible.