The complete FlowSOM package known from R, now available in Python!

Please refer to the documentation. In particular, the following resources are available:

• Example FlowSOM notebook
• API documentation
• FlowSOM Python Cheatsheet

You need to have Python 3.9 or newer installed on your system. If you don't have Python installed, we recommend installing Mambaforge.

There are several alternative options to install FlowSOM:

1. Install the latest development version:

pip install git+https://github.com/saeyslab/FlowSOM_Python

Starting from an FCS file that is properly transformed, compensated and checked for quality, the following code can be used to run the FlowSOM algorithm:

# Import the FlowSOM package
import flowsom as fs

# Load the FCS file
ff = fs.io.read_FCS("./tests/data/ff.fcs")

# Run the FlowSOM algorithm
fsom = fs.FlowSOM(
    ff, cols_to_use=[8, 11, 13, 14, 15, 16, 17], xdim=10, ydim=10, n_clusters=10, seed=42
)

# Plot the FlowSOM results
p = fs.pl.plot_stars(fsom, background_values=fsom.get_cluster_data().obs.metaclustering)
p.show()

See the changelog.

For questions and help requests or if you found a bug, please use the issue tracker.

If you use FlowSOM in your work, please cite the following papers:

A. Couckuyt, B. Rombaut, Y. Saeys, and S. Van Gassen, “Efficient cytometry analysis with FlowSOM in Python boosts interoperability with other single-cell tools,” Bioinformatics, vol. 40, no. 4, p. btae179, Apr. 2024, doi: 10.1093/bioinformatics/btae179.

S. Van Gassen et al., “FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data,” Cytometry Part A, vol. 87, no. 7, pp. 636–645, 2015, doi: 10.1002/cyto.a.22625.