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

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          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.

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          Author and article information

          Journal
          Cytometry A
          Cytometry. Part A : the journal of the International Society for Analytical Cytology
          Wiley-Blackwell
          1552-4930
          1552-4922
          Jul 2015
          : 87
          : 7
          Affiliations
          [1 ] Department of Information Technology, Ghent University, iMinds, Ghent, Belgium.
          [2 ] Inflammation Research Center, VIB, Ghent, Belgium.
          [3 ] Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium.
          Article
          10.1002/cyto.a.22625
          25573116

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