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FlowSOM: Using self‐organizing maps for visualization and interpretation of cytometry data
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
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“Coupled with modern dimensionality-reduction and clustering algorithms (e.g., t-SNE [51], FlowSOM [52]), these platforms resolve continuous phenotypic gradients and novel subpopulations [41].”
Dissecting the cell of origin of aberrant SALL4 expression in myelodysplastic syndrome
Tatetsu, H., Watanabe, M., Liu, J., Tokunaga, K., Iwanaga, E., Komohara, Y., Thrash, E., Bassal, M. A., Matsuoka, M., Tenen, D. G., et al. (2023) · Clinical and Translational Medicine · doi:10.1002/ctm2.1327 · Cited by 1
Metadata
- Authors
- Van Gassen, S., Callebaut, B., Van Helden, M. J., Lambrecht, B. N., Demeester, P., Dhaene, T., & Saeys, Y.
- Venue
- Cytometry Part A
- Publisher
- Wiley
- Published
- 2015-01-08
- Volume
- 87
- Issue
- 7
- Pages
- 636-645
- ISSN
- 1552-4922 (ISSN-L)
- 1552-4930
- OpenAlex
- W1993835819
- Cited by
- 1,919
- References
- 3
- FWCI
- 12.09
- Type
- journal article
- Language
- en
- Open access
- closed
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