Dimensionality Reduction with viSNE

High dimensional flow cytometry data (both fluorescent or mass cytometry) require new visualization and analysis tools such as viSNE/t-SNE. On the Cytobank Platform viSNE uses the Barnes-Hut implementation of the t-SNE algorithm 1. viSNE is a dimensionality-reduction algorithm that works well for flow cytometry data, as well as many other data types 2. It allows users to analyze many cell events to detect rare populations.

viSNE parameters may require adjustment to resolve major and minor cell populations in large datasets. The viSNE implementation available on the Cytobank platform provides the necessary flexibility to define and optimize viSNE parameters, such as iterations and perplexity. Learn more about the impact of the settings and how to run viSNE on Cytobank in the tutorial below. Combining data analysis tools, for example viSNE and FlowSOM, creates an analysis pipeline aimed at extracting maximum information from high parameter data.

Dimensionality Reduction with viSNE

Listen to two scientists sharing how and why they use viSNE and other tools to accelerate their research.

For more details and instructions also visit our Cytobank Support pages.

References

  1. Kotecha, N., Krutzik, P. O., & Irish, J. M. (2010). Web–Based Analysis and Publication of Flow Cytometry Experiments. Current Protocols in Cytometry, 53(1), 10.17.1–10.17.24. https://doi. org/10.1002/0471142956.cy1017s53

  2. Amir ED, Davis KL, Tadmor MD, et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nature Biotechnology. 2013;31(6):545-552. doi:10.1038/nbt.2594