Speaker: Julia Fukuyama, Postdoctoral Research Fellow, Department of Computational Biology, Fred Hutchinson Cancer Research Institute
Title: Dimension Reduction for Structured Variables
Abstract: 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 how adaptive gPCA applies to general variable structures, including variables structured according to a network, as well as implications for supervised learning and structure estimation.