%A Shields-Cutler,Robin R. %A Al-Ghalith,Gabe A. %A Yassour,Moran %A Knights,Dan %D 2018 %J Frontiers in Microbiology %C %F %G English %K bioinformatics,Microbiome analysis,permutation tests,longitudinal data analysis,R packages,computational biology methods %Q %R 10.3389/fmicb.2018.00785 %W %L %M %P %7 %8 2018-April-23 %9 Technology Report %# %! Longitudinal microbiome analysis with splinectomeR %* %< %T SplinectomeR Enables Group Comparisons in Longitudinal Microbiome Studies %U https://www.frontiersin.org/articles/10.3389/fmicb.2018.00785 %V 9 %0 JOURNAL ARTICLE %@ 1664-302X %X 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.