B1310
Title: Learning the ocean's microbial ecology using statistical mixture models
Authors: Sangwon Hyun - University of California, Santa Cruz (United States) [presenting]
Jacob Bien - University of Southern California (United States)
Francois Ribalet - University of Washington (United States)
Abstract: Microscopic phytoplankton in the ocean are extremely important to all of life and are responsible for as much photosynthesis as all plants on land combined. Oceanographers now routinely collect single-cell data in real-time while onboard a moving ship, which yields high-resolution information about the distribution of phytoplankton across thousands of kilometers. New statistical mixture models are presented, designed to estimate time-varying phytoplankton sub-populations from flow cytometry data. Combining techniques like trend filtering and censoring with mixture models, effective analysis of this complex biological data is achieved. The models are applied to data from numerous oceanographic ships deployed in the North Pacific Ocean to improve plankton classification in ocean flow cytometry data and learn new insights about the relationship between marine microbial populations and environmental factors.
