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

By S. van Gassen, Britt Callebaut, M. V. van Helden, B. Lambrecht, Piet Demeester, Tom D'haene, Yvan Saeys. 2015

1,724 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Evan W. Newell
2. Yvan Saeys
3. B. Lambrecht
4. S. van Gassen
5. Garry P. Nolan
6. Sean Bendall
7. Dmitry Kobak

In Molecular Biology

1. Yvan Saeys
2. Evan W. Newell
3. B. Lambrecht
4. S. van Gassen
5. Garry P. Nolan
6. Sean Bendall
7. Jinmiao Chen
8. Michael Poidinger

Citations in the corpus

• SOM-based embedding improves efficiency of high-dimensional cytometry data analysis (2019, methodology): “SOMs have been successfully used for classifying this space into clusters — for example, FlowSOM [25] uses 45 SOM vertices as cluster centers to classify the cells into clusters that form a basis for further analysis.”
• High‐Dimensional Single‐Cell Mapping of Central Nervous System Immune Cells Reveals Distinct Myeloid Subsets in Health, Aging, and Disease (2018, neutral): “…al., 2013 https://github.com/nolanlab/bead- normalization/releases t-SNE Van Der Maaten and Hinton, 2008 https://github.com/jkrijthe/Rtsne FlowSOM Van Gassen et al., 2015 https://github.com/SofieVG/FlowSOM Scaffold Spitzer et al., 2015 https://github.com/nolanlab/scaffold One-SENSE Cheng et al.,…”
• Computational flow cytometry: helping to make sense of high-dimensional immunology data (2016, methodology): “Another clustering based technique to visualize cytometry data is the FlowSOM (flow cytometry data analysis using self-organizing maps) algorithm 52 .”
• Comparison Between UMAP and t-SNE for Multiplex-Immunofluorescence Derived Single-Cell Data from Tissue Sections (2019, background): “Among the 119 biopsies included and with a median follow-up of 73 months (4-160), 5-year overall survival was 39.”
• MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure (2019, methodology): “To systematically identify tumor phenotypes, we performed clustering analysis (44) of tumor cells across all regions.”
• Unsupervised High-Dimensional Analysis Aligns Dendritic Cells across Tissues and Species (2016, methodology): “More importantly, both FlowSOM (Figures S2D and S2E) and tSNE (Figure S2F) readily identified cDC1s regardless of their lower CD11c expression because the remaining XCR1hi CD172aloCD64loF4/80loMHCIIhiCD26hi profile was sufficient to identify these cells as cDC1s in a fully unsupervised way.”