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

• Published in: Cytometry. Part A Vol. 87; no. 7; pp. 636 - 645
• Main Authors: Van Gassen, Sofie, Callebaut, Britt, Van Helden, Mary J., Lambrecht, Bart N., Demeester, Piet, Dhaene, Tom, Saeys, Yvan
• Format: Journal Article
• Language: English
• Published: United States 01.07.2015
• Subjects: Algorithms; bioinformatics; Biomarkers - analysis; Cluster Analysis; Computational Biology - methods; exploratory data analysis; Flow Cytometry - methods; Graft vs Host Disease - diagnosis; Hematopoietic Stem Cell Transplantation; Humans; Key terms: polychromatic flow cytometry; Lymphoma, B-Cell - diagnosis; mass cytometry; self‐organizing map; visualization method; West Nile Fever - diagnosis; self-organizing map; visualization method; exploratory data analysis; mass cytometry; bioinformatics; Key terms: polychromatic flow cytometry
• ISSN: 1552-4922; 1552-4930; 1552-4930
• DOI: 10.1002/cyto.a.22625

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