Efficient cytometry analysis with FlowSOM in Python boosts interoperability with other single-cell tools.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 38632080.
- Also identified by DOI 10.1093/bioinformatics/btae179 and PMC identifier 11052654.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
We describe a new Python implementation of FlowSOM, a clustering method for cytometry data. 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. The FlowSOM Python implementation is freely available on GitHub: https://github.com/saeyslab/FlowSOM_Python.
Medical subject headings
- Single-Cell Analysis
- Software
- Flow Cytometry