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Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
Published on: July 12, 2018
Multivariate data analysis methods for the interpretation of microbial flow cytometric data
Hazel M Davey1, Christopher L Davey
1Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Penglais, Aberystwyth, SY23 3DD, UK, hlr@aber.ac.uk.
Advances in Biochemical Engineering/Biotechnology
|November 12, 2010
Summary
Flow cytometry hardware advancements enable microbial analysis. New software and advanced methods like genetic programming improve data analysis, overcoming limitations of manual processing for microbial flow cytometry data.
Area of Science:
- Microbiology
- Immunology
- Cell Biology
Background:
- Flow cytometry hardware is now sensitive enough for routine microbial analysis.
- Software tools for analyzing, displaying, and disseminating flow cytometry data have lagged behind hardware advancements.
- Manual analysis of individual samples remains a bottleneck in microbial flow cytometry.
Purpose of the Study:
- To present new datasets illustrating common flow cytometry applications in microbiology.
- To demonstrate advanced data analysis techniques for microbial flow cytometry.
- To introduce novel software for automated data visualization in flow cytometry.
Main Methods:
- Application of manual data analysis.
- Utilizing automated visualization with newly developed software.
- Employing genetic programming, principal components analysis, and artificial neural networks.
Main Results:
- Demonstrated the effectiveness of advanced computational methods for microbial flow cytometry data.
- Illustrated the application of these methods on two new microbiological datasets.
- Showcased a new software tool for automated data visualization.
Conclusions:
- Advanced computational methods can significantly enhance the analysis of microbial flow cytometry data.
- The presented techniques overcome limitations associated with manual data analysis.
- The described data analysis approaches are transferable to other cell types in flow cytometry applications.
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