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

S Van Gassen, B Callebaut, MJ Van Helden… - Cytometry Part …, 2015 - Wiley Online Library
S Van Gassen, B Callebaut, MJ Van Helden, BN Lambrecht, P Demeester, T Dhaene
Cytometry Part A, 2015Wiley Online Library
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 …
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
Wiley Online Library