FlowSOM: Using self‐organizing maps for visualization and interpretation of cytometry data

TLDR

The rapid increase in markers in flow and mass cytometry generates high‑dimensional data that is impractical to analyze manually, and traditional 2D scatter plots become exponentially numerous, risking missed information. The study introduces FlowSOM, a visualization method that applies a Self‑Organizing Map to flow or mass cytometry data. FlowSOM employs two‑level clustering and star charts to provide a clear overview of marker behavior across cells and to uncover potentially missed subsets. The FlowSOM implementation is available as R code on GitHub and will be released via Bioconductor. © 2015 International Society for Advancement of Cytometry.

Abstract

Abstract The number of markers measured in both flow and mass cytometry keeps increasing steadily. Although this provides a wealth of information, it becomes infeasible to analyze these datasets manually. When using 2D scatter plots, the number of possible plots increases exponentially with the number of markers and therefore, relevant information that is present in the data might be missed. In this article, we introduce a new visualization technique, called FlowSOM, which analyzes Flow or mass cytometry data using a Self‐Organizing Map. Using a two‐level clustering and star charts, our algorithm helps to obtain a clear overview of how all markers are behaving on all cells, and to detect subsets that might be missed otherwise. R code is available at https://github.com/SofieVG/FlowSOM and will be made available at Bioconductor. © 2015 International Society for Advancement of Cytometry