FlowSOM offers a new way to look at your cytometry data. Instead of visualizing all the cells in 2D-scatter plots, the cells are first clustered. By grouping very similar cells together, it is easier to create a full overview, in which each group of cells is visualized as a 'node'. These nodes are organized into a tree, in which similar nodes are connected, creating 'branches' corresponding to the different cell types. Pie charts indicate the MFI's for different markers and node size corresponds to the number of cells in each population.
Additionaly, different groups of samples (e.g. different mouse strains or different treatments) can be easily compared. When mapping both groups onto the tree, statistical tests can indicate which nodes differ significantly. Because all cells are visualized in the tree, you might detect differences in cell subsets you wouldn't even have analyzed otherwise.
Sofie Van Gassen, Britt Callebaut, Mary J. Van Helden, Bart N. Lambrecht, Piet Demeester, Tom Dhaene and Yvan Saeys. FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A. DOI: 10.1002/cyto.a.22625