SplinectomeR enables group comparisons in longitudinal microbiome studies

Robin R. Shields-Cutler, Gabe A. Al-Ghalith, Moran Yassour, Dan Knights

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Longitudinal, prospective studies often rely on multi-omics approaches, wherein various specimens are analyzed for genomic, metabolomic, and/or transcriptomic profiles. In practice, longitudinal studies in humans and other animals routinely suffer from subject dropout, irregular sampling, and biological variation that may not be normally distributed. As a result, testing hypotheses about observations over time can be statistically challenging without performing transformations and dramatic simplifications to the dataset, causing a loss of longitudinal power in the process. Here, we introduce splinectomeR, an R package that uses smoothing splines to summarize data for straightforward hypothesis testing in longitudinal studies. The package is open-source, and can be used interactively within R or run from the command line as a standalone tool. We present a novel in-depth analysis of a published large-scale microbiome study as an example of its utility in straightforward testing of key hypotheses. We expect that splinectomeR will be a useful tool for hypothesis testing in longitudinal microbiome studies.

Original languageEnglish (US)
Article number785
JournalFrontiers in Microbiology
Volume9
Issue numberAPR
DOIs
StatePublished - Apr 23 2018

Keywords

  • Bioinformatics
  • Computational biology methods
  • Longitudinal data analysis
  • Microbiome analysis
  • Permutation tests
  • R packages

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