Studies of the microbiome, the complex communities of bacteria that live in and around us, present interesting statistical problems. In particular, bacteria are best understood as the result of a continuous evolutionary process and methods to analyze data from microbiome studies should use the evolutionary history. Motivated by this example, I describe adaptive gPCA, a method for dimensionality reduction that uses the evolutionary structure as a regularizer and to improve interpretability of the low-dimensional space. I also discuss implications for interpretable supervised learning incorporating both the phylogeny and variable selection.
Julia Fukuyama is currently a postdoctoral research fellow in Computational Biology at the Fred Hutchinson Cancer Research Center. She obtained her PhD in Statistics at Stanford University, where she developed a set of multivariate methods for integrative analysis of abundance and phylogenetic data for the microbiome. Her postdoctoral work has been in computational immunology, focusing in particular on B cell repertoire sequencing. She also holds a BS in Biology from Yale University, which informs her interest in methods that help us make sense of complex, high-dimensional biological data.