Shields-Cutler, Robin R.Al-Ghalith, Gabe A.Yassour, MoranKnights, Dan2018-06-272018Shields-Cutler, Robin R., Gabe A. Al-Ghalith, Moran Yassour, and Dan Knights. 2018. “SplinectomeR Enables Group Comparisons in Longitudinal Microbiome Studies.” Frontiers in Microbiology 9 (1): 785. doi:10.3389/fmicb.2018.00785. http://dx.doi.org/10.3389/fmicb.2018.00785.http://nrs.harvard.edu/urn-3:HUL.InstRepos:37160129Longitudinal, 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.en-USTechnology Reportbioinformaticsmicrobiome analysisR packagescomputational biology methodspermutation testslongitudinal data analysisJournal Article2018-06-2710.3389/fmicb.2018.00785