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Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
Published on: July 12, 2018
flowPhyto: enabling automated analysis of microscopic algae from continuous flow cytometric data.
Francois Ribalet1, David M Schruth, E Virginia Armbrust
1School of Oceanography, University of Washington, Seattle, WA 98195, USA.
Bioinformatics (Oxford, England)
|January 7, 2011
Summary
flowPhyto is a new R package for analyzing high-throughput flow cytometry data from aquatic environments. It efficiently processes large datasets to identify phytoplankton populations.
Area of Science:
- Marine biology
- Limnology
- Computational biology
Background:
- Flow cytometry is crucial for analyzing microscopic algae in aquatic ecosystems.
- High-frequency flow cytometers generate massive datasets requiring automated analysis.
- Phytoplankton population dynamics are essential for understanding aquatic environments.
Purpose of the Study:
- To introduce flowPhyto, an R package for analyzing high-throughput flow cytometry data.
- To provide a memory-efficient and parallelized solution for large-scale flow cytometry data.
- To facilitate the analysis of phytoplankton populations in aquatic samples.
Main Methods:
- Development of the flowPhyto R package.
- Implementation of aggregate statistics for raw flow cytometry files.
- Utilizing parallel processing for efficient data analysis.
Main Results:
- flowPhyto enables processing of virtually unlimited raw flow cytometry files.
- The package offers a memory-efficient solution for high-throughput data.
- Parallelization ensures rapid analysis of large datasets.
Conclusions:
- flowPhyto provides a robust computational tool for phytoplankton analysis.
- The R package addresses the need for automated analysis of high-frequency flow cytometry data.
- flowPhyto is freely accessible, promoting wider adoption in aquatic research.
