ShinySOM: graphical SOM-based analysis of single-cell cytometry data.
Miroslav Kratochvíl1,2, David Bednárek2, Tomáš Sieger3
1Institute of Organic Chemistry and Biochemistry AS CR, 166 10 Praha 6, Czech Republic.
Bioinformatics (Oxford, England)
|February 13, 2020
Summary
ShinySOM provides a user-friendly platform for analyzing high-dimensional cytometry data using self-organizing maps. This tool enhances performance and data dissection for reproducible, high-throughput analysis.
Area of Science:
- Computational Biology
- Bioinformatics
- Data Science
Background:
- High-dimensional cytometry data (flow and mass cytometry) presents significant analysis challenges.
- Reproducible and high-throughput analysis is crucial for extracting meaningful biological insights.
Purpose of the Study:
- To introduce ShinySOM, a software tool for user-friendly, high-throughput analysis of high-dimensional cytometry data.
- To improve upon existing FlowSOM workflows with enhanced performance, visualization, and data dissection capabilities.
Main Methods:
- Utilizes self-organizing maps (SOMs) for data clustering and visualization.
- Implements a FlowSOM-style workflow with performance optimizations.
- Provides R-compatible metadata for batch processing.
Main Results:
- ShinySOM offers a user-friendly interface for complex data analysis.
- Improved performance and enhanced data dissection possibilities compared to standard workflows.
- Generates precise statistical information and R-compatible metadata for downstream analysis.
Conclusions:
- ShinySOM facilitates reproducible, high-throughput analysis of high-dimensional cytometry data.
- The software empowers researchers with advanced visualization and data dissection tools.
- ShinySOM is a valuable, free, and open-source resource for the cytometry community.
