  Bacterial community composition is measured using 16S rRNA (ribosomal
ribonucleic acid) gene sequencing, for which one of the defining
characteristics is the phylogenetic relationships that exist between variables.
Here, we demonstrate the utility of modelling these relationships in two
statistical tasks (the two sample test and host trait prediction) by employing
string kernels originally proposed in natural language processing. We show via
simulation studies that a kernel two-sample test using the proposed kernels,
which explicitly model phylogenetic relationships, is powerful while also being
sensitive to the phylogenetic scale of the difference between the two
populations. We also demonstrate how the proposed kernels can be used with
Gaussian processes to improve predictive performance in host trait prediction.
Our method is implemented in the Python package StringPhylo (available at
github.com/jonathanishhorowicz/stringphylo).
