Efficient cytometry analysis with FlowSOM in Python boosts interoperability with other single-cell tools

Abstract Motivation We describe a new Python implementation of FlowSOM, a clustering method for cytometry data. Results This implementation is faster than the original version in R, better adapted to work with single-cell omics data including integration with current single-cell data structures and...

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) England), 2024-03, Vol.40 (4)
Main Authors: Couckuyt, Artuur, Rombaut, Benjamin, Saeys, Yvan, Van Gassen, Sofie
Format: Article
Language:English
Subjects:
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Clustering
Cytometry
Data structures
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Summary:Abstract Motivation We describe a new Python implementation of FlowSOM, a clustering method for cytometry data. Results This implementation is faster than the original version in R, better adapted to work with single-cell omics data including integration with current single-cell data structures and includes all the original visualizations, such as the star and pie plot. Availability and implementation The FlowSOM Python implementation is freely available on GitHub: https://github.com/saeyslab/FlowSOM_Python.
ISSN:1367-4811
1367-4803
1367-4811
DOI:10.1093/bioinformatics/btae179