flowmix

A statistical approach to model cell populations measured by flow cytometry with covariates using sparse mixture of regressions.

In this project led by Dr. Sangwon Hyun, we apply a sparse mixture of multivariate regressions model for flow cytometry data. The main motivating application is continuous-time flow cytometry data collected in the ocean, over space and time. we propose a novel sparse mixture of multivariate regressions model to estimate the time-varying phytoplankton subpopulations while simultaneously identifying the specific environmental covariates that are predictive of the observed changes to these subpopulations.

A short vignette here shows how to use the package.

Description of the model is available here: https://doi.org/10.1214/22-AOAS1631

Code is available here: https://github.com/seaflow-uw/flowmix