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

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

Artuur Couckuyt  1   2 , Benjamin Rombaut  1   2 , Yvan Saeys  1   2 , Sofie Van Gassen  1   2
Affiliations

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

Artuur Couckuyt et al. Bioinformatics. .

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.

PubMed Disclaimer

Conflict of interest statement

None declared.

Figures

Figure 1.
Figure 1.
Overview of FlowSOM functionalities in Python. (A) A star plot of a FlowSOM object made in matplotlib. Each node is a cluster and is scaled according to the percentage of cells in this cluster. In each cluster, the marker median values are represented by the star charts. The metaclusters are displayed as the background color of each node. (B) The computational runtime of the Python implementation is lower than in R. The average computational runtime was tested on a single core of a compute cluster with 32 GB of RAM and we oversampled one file to the necessary amount of events. The error bars are the standard deviation over five runs. (C) FlowSOM in Python is more memory efficient than R. The memory usage was averaged over five runs on a single core of a compute cluster with 32 GB of RAM. We oversampled one file to the necessary amount of events. The error bars are the standard deviation over five runs. (D) The performance of the Python implementation of FlowSOM is equal to R. F1 scores are calculated based on the manually assigned cell labels and the values predicted from FlowSOM in both Python and R and are shown over 10 runs. Using a Wilcoxon rank-sum test, no significant differences were found (P > .05).
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