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Models bacterial compositions derived from 16S rRNA sequencing. GPMicrobiome is a Stan implementation of the Temporal Gaussian Process Model for Compositional Data Analysis (TGP-CODA). The model integrates temporal, over dispersion, and zero-inflation components for analyzing longitudinal 16S rRNA sequencing data. It can incorporate different experimental designs, such as non-equidistant sampling over time, missing time points, and variable sequencing depth and quantifies the uncertainty of the final estimates, which is an important property in integrated microbiome studies.


A framework for PERMANOVA power estimation tailored to marker-gene microbiome studies that will be analyzed by pairwise distances, which includes: (i) a method for distance matrix simulation that permits modeling of within-group pairwise distances according to pre-specified population parameters; (ii) a method to incorporate effects of different sizes within the simulated distance matrix; (iii) a simulation-based method for estimating PERMANOVA power from simulated distance matrices; and (iv) an R statistical software package that implements the above. Matrices of pairwise distances can be efficiently simulated to satisfy the triangle inequality and incorporate group-level effects, which are quantified by the adjusted coefficient of determination, omega-squared. From simulated distance matrices, available PERMANOVA power or necessary sample size can be estimated for a planned microbiome study.