F1000Research (Feb 2020)

microbiomeDASim: Simulating longitudinal differential abundance for microbiome data [version 2; peer review: 2 approved]

  • Justin Williams,
  • Hector Corrada Bravo,
  • Jennifer Tom,
  • Joseph Nathaniel Paulson

DOI
https://doi.org/10.12688/f1000research.20660.2
Journal volume & issue
Vol. 8

Abstract

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An increasing emphasis on understanding the dynamics of microbial communities in various settings has led to the proliferation of longitudinal metagenomic sampling studies. Data from whole metagenomic shotgun sequencing and marker-gene survey studies have characteristics that drive novel statistical methodological development for estimating time intervals of differential abundance. In designing a study and the frequency of collection prior to a study, one may wish to model the ability to detect an effect, e.g., there may be issues with respect to cost, ease of access, etc. Additionally, while every study is unique, it is possible that in certain scenarios one statistical framework may be more appropriate than another. Here, we present a simulation paradigm implemented in the R Bioconductor software package microbiomeDASim available at http://bioconductor.org/packages/microbiomeDASim microbiomeDASim. microbiomeDASim allows investigators to simulate longitudinal differential abundant microbiome features with a variety of known functional forms with flexible parameters to control desired signal-to-noise ratio. We present metrics of success results on one particular method called metaSplines.