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

Artuur Couckuyt; Benjamin Rombaut; Yvan Saeys; Sofie Van Gassen

doi:10.1093/bioinformatics/btae179

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

Bioinformatics. 2024 Mar 29;40(4):btae179. doi: 10.1093/bioinformatics/btae179.

Authors

Artuur Couckuyt^{1

2}, Benjamin Rombaut^{1

2}, Yvan Saeys^{1

2}, Sofie Van Gassen^{1

2}

Affiliations

¹ Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium.
² Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, 9052 Ghent, Belgium.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Cluster Analysis
Computational Biology / methods
Flow Cytometry* / methods
Humans
Single-Cell Analysis* / methods
Software*

Abstract

Publication types

MeSH terms

Grants and funding