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

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 mar...