ShinySOM: graphical SOM-based analysis of single-cell cytometry data
Language English Country England, Great Britain Media print
Document type Journal Article, Research Support, Non-U.S. Gov't
PubMed
32049322
PubMed Central
PMC7214046
DOI
10.1093/bioinformatics/btaa091
PII: 5734646
Knihovny.cz E-resources
- MeSH
- Algorithms * MeSH
- Metadata MeSH
- Workflow MeSH
- Software * MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
SUMMARY: ShinySOM offers a user-friendly interface for reproducible, high-throughput analysis of high-dimensional flow and mass cytometry data guided by self-organizing maps. The software implements a FlowSOM-style workflow, with improvements in performance, visualizations and data dissection possibilities. The outputs of the analysis include precise statistical information about the dissected samples, and R-compatible metadata useful for the batch processing of large sample volumes. AVAILABILITY AND IMPLEMENTATION: ShinySOM is free and open-source, available online at gitlab.com/exaexa/ShinySOM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Department of Software Engineering MFF Charles University 118 00 Prague Czech Republic
Institute of Organic Chemistry and Biochemistry AS CR 166 10 Praha 6 Czech Republic
See more in PubMed
Chevrier S. et al. (2018) Compensation of signal spillover in suspension and imaging mass cytometry. Cell Syst., 6, 612–620.e5. PubMed PMC
Fišer K. et al. (2012) Detection and monitoring of normal and leukemic cell populations with hierarchical clustering of flow cytometry data. Cytometry A, 81, 25–34. PubMed
Kratochvíl M. et al. (2019) Generalized EmbedSOM on quadtree-structured self-organizing maps. F1000Res., 8, 2120. PubMed PMC
Monaco G. et al. (2016) flowAI: automatic and interactive anomaly discerning tools for flow cytometry data. Bioinformatics, 32, 2473–2480. PubMed
Sieger T. et al. (2017) Interactive dendrograms: the R packages idendro and idendr0. J. Stat. Softw., 76, 1–22.
van Gassen S. et al. (2015) FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A, 87, 636–645. PubMed
Weber L.M., Robinson M.D. (2016) Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data. Cytometry A, 89, 1084–1096. PubMed