Abstract
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.
Highlights
Analysis of the microbiome aims to characterize the composition and functional potential of microbes in a particular ecosystem
Recent studies have shown the gut microbiome plays an important roles in various diseases, from the efficacy of cancer immunotherapy to the pathogenesis of inflammatory bowel disease (IBD)[1,2,3,4]
To better understand bacterial population dynamics, many studies are expanding to longitudinal sampling and foregoing cross-sectional or single time-point explorations
Summary
Analysis of the microbiome aims to characterize the composition and functional potential of microbes in a particular ecosystem. Recent studies have shown the gut microbiome plays an important roles in various diseases, from the efficacy of cancer immunotherapy to the pathogenesis of inflammatory bowel disease (IBD)[1,2,3,4]. While many studies profile static community “snapshots”, microbial communities do not exist within an equilibrium[5]. To better understand bacterial population dynamics, many studies are expanding to longitudinal sampling and foregoing cross-sectional or single time-point explorations. With a decrease in sequencing costs, more longitudinal data will be generated for varying communities of interest. While data generation will present fewer difficulties, there remain several statistical challenges involved in analyzing these datasets
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