Thursday, February 15, 2018 - 4:15pm to 5:15pm
Fred Hutchinson Cancer Research Institute
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.
Yale Institute for Network Science, 17 Hillhouse Avenue, 3rd Floor