Efficient cytometry analysis with FlowSOM in Python boosts interoperability with other single-cell tools
Artuur Couckuyt, Benjamin Rombaut, Yvan Saeys, Sofie Van Gassen
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.
Topics & Concepts
Python (programming language)Computer scienceInteroperabilityCluster analysisProgramming languageWorld Wide WebArtificial intelligenceSingle-cell and spatial transcriptomicsCell Image Analysis TechniquesEnvironmental Monitoring and Data Management