FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data - PubMed (original) (raw)
doi: 10.1002/cyto.a.22625. Epub 2015 Jan 8.
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- PMID: 25573116
- DOI: 10.1002/cyto.a.22625
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FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data
Sofie Van Gassen et al. Cytometry A. 2015 Jul.
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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.
Keywords: Key terms: polychromatic flow cytometry; bioinformatics; exploratory data analysis; mass cytometry; self-organizing map; visualization method.
© 2015 International Society for Advancement of Cytometry.
Comment in
- Algorithmic Clustering Of Single-Cell Cytometry Data-How Unsupervised Are These Analyses Really?
Pedersen CB, Olsen LR. Pedersen CB, et al. Cytometry A. 2020 Mar;97(3):219-221. doi: 10.1002/cyto.a.23917. Epub 2019 Nov 5. Cytometry A. 2020. PMID: 31688998 No abstract available.
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