Acquiring highly multi-parametric flow cytometry data sets is becoming more routine with the advent of new instrumentation and reagents but challenges remain to distill the information into visualizations that can be easily assessed and reported. Data transformation algorithms in the form of tSNE, UMAP,...
tSNE is an unsupervised nonlinear dimensionality reduction algorithm useful for visualizing high dimensional flow or mass cytometry data sets in a dimension-reduced data space. The tSNE platform computes two new derived parameters from a user defined selection of cytometric parameters. The tSNE-generated...