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Activity Number:
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587
- Ocean Statistical Methodology and Application
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 6, 2020 : 3:00 PM to 4:50 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #313491
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Title:
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Joint Modeling of Continuous Flow Cytometry Data with Environmental Covariates
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Author(s):
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Sangwon Hyun* and Jacob Bien and Mattias Rolf Cape and Francois Ribalet
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Companies:
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University of Southern California and University of Southern California and School of Oceanography at the University of Washington and School of Oceanography at the University of Washington
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Keywords:
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Ocean;
Gaussian mixture model;
EM Algorithm;
Flow Cytometry;
Mixture of regressions
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Abstract:
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Flow cytometry data collected in the ocean can give valuable insight into the composition and dynamics of phytoplankton populations. We present a novel method for modeling time-varying flow cytometry data conditional on a large number of environmental covariates. We develop a novel mixture of multivariate sparse regressions model that can simultaneously estimate and identify the important covariates for each phytoplankton population. The method ties covariates to both the flow cytometry population centers as well as the relative abundances of these populations. The approach involves a lasso-penalized expectation-maximization procedure with additional convex constraints to facilitate interpretation of the estimated model. We apply the method to continuous-time flow cytometry data measured from the ocean, on a ship near Honolulu traveling from warmer, nutrient-sparse subtropical waters to cooler, more productive waters. The method provides a powerful framework for developing a fine-grained understanding of the environmental drivers of phytoplankton populations in the ocean.
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Authors who are presenting talks have a * after their name.
