THIS SEMINAR IS CANCELLED. WE APOLOGISED FOR ANY INCONVENIENCE.
THANK YOU FOR YOUR KIND UNDERSTANDING.
It is increasingly recognized that human host-microbiome interactions play important roles in human health, from metabolism to immune system. However, analysis of human microbiome data poses great challenges in robust quantification and results interpretation, due to the unique characteristics of the data, including high-dimensional compositional data, stochastic noise in composition, and a tree structure among the variables. In this talk, I will discuss the analysis strategies of such noisy tree-structure compositional data and share some of our recent efforts to ease such challenges from an optimal transport perspective. Specifically, I will present a new minimax optimal estimator for the Wasserstein distance on tree and introduce a novel interpretable two-sample test by leveraging the tree structure. The practical merit of the proposed methods is demonstrated by an application to a human intestinal biopsy microbiome data set for patients with inflammatory bowel disease.