viSNE is an algorithm that reduces high-parameter data down to two dimensions for easy visualization and rapid exploratory data analysis of any data type. viSNE in Cytobank uses the Barnes-Hut implementation of the t-SNE algorithm (van der Maaten, 2014).
Here are some resources to learn more about viSNE:
- Events in our webinar series that feature viSNE:
- “viSNE Applications and Insights from the Experts” featuring Dr. Anna Belkina
- “Bench to Bytes - Translating Bench Research” where Drs. Shahram Kordasti and Richard Ellis show how they used viSNE to make new discoveries in a large clinical dataset
- “Pipeline Setup & Analysis” where Dr. Vinko Tosevski illustrates how to combine multiple tools in Cytobank to analyze high parameter data
- Examples using viSNE from our blog
- The publication from the Pe’er lab at Columbia University that originally demonstrated the use of the tSNE algorithm in cytometry data: viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia
- A PDF guide for viSNE analysis of the Healthy Human PBMC 26 Surface Markers demo dataset
A viSNE analysis can be run on a data set by logging into Premium or Enterprise Cytobank and using the Experiment Navigation bar to open the viSNE menu and create a new analysis. Read more about How to Configure and Run a viSNE Analysis.