flowPhyto: Enabling automated analysis of microscopic algae from continuous flow cytometric data

Abstract

Motivation: Flow cytometry is a widely used technique among biologists to study the abundances of populations of microscopic algae living in aquatic environments. A new generation of high-frequency flow cytometers collects up to several hundred samples per day and can run continuously for several weeks. Automated computational methods are needed to analyze the different phytoplankton populations present in each sample. Software packages in the programming environment R provide powerful tools for conducting such analyses. Results: We introduce flowPhyto, an R package that performs aggregate statistics on virtually unlimited collections of raw flow cytometry files and provides a memory efficient, parallelized solution for analyzing high-throughput flow cytometric data.

Francois Ribalet

Research Associate Professor

Our work combines high-resolution ocean observations and statistical modeling to reveal how environmental changes affect the growth of marine microbial communities, helping us understand their role in marine ecosystems and global carbon cycling.