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FlowSOM: Using self‐organizing maps for visualization and interpretation of cytometry data
Author(s) -
Van Gassen Sofie,
Callebaut Britt,
Van Helden Mary J.,
Lambrecht Bart N.,
Demeester Piet,
Dhaene Tom,
Saeys Yvan
Publication year - 2015
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.22625
Subject(s) - mass cytometry , bioconductor , computer science , cluster analysis , visualization , cytometry , flow cytometry , data mining , data visualization , artificial intelligence , biology , biochemistry , genetics , gene , phenotype
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.