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

Cytometry A. 2015 Jul;87(7):636-45. doi: 10.1002/cyto.a.22625. Epub 2015 Jan 8.


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.

Keywords: Key terms: polychromatic flow cytometry; bioinformatics; exploratory data analysis; mass cytometry; self-organizing map; visualization method.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Biomarkers / analysis
  • Cluster Analysis
  • Computational Biology / methods*
  • Flow Cytometry / methods*
  • Graft vs Host Disease / diagnosis
  • Hematopoietic Stem Cell Transplantation
  • Humans
  • Lymphoma, B-Cell / diagnosis
  • West Nile Fever / diagnosis


  • Biomarkers