While tSNE is a powerful visualization technique, running the algorithm is computationally expensive, and the output is non-deterministic, which means that: 1) you must limit the number of events fed into the algorithm for the calculation to complete in a reasonable period of time, and 2) if you run the algorithm more than once (on two separate samples, for example), the 2-dimensional data space created by tSNE will be different between those samples. Manually-gated populations of known phenotype were overlaid onto the tSNE space in the FlowJo Layout editor, revealing how distinct phenotypic subsets of events cluster together and are enriched in distinct areas of the continent-like structure. Example of a 15-color flow cytometry panel after tSNE has been used to reduce dimensionality into a 2-dimensional data space. These tSNE-generated parameters are optimized in such a way that data points that were close together in the raw high-dimensional data remain close together in the reduced data space. The tSNE algorithm computes two new derived parameters from a user-defined selection of cytometric parameters. One approach is to use a dimensionality reduction algorithm, which reduces an N-dimensional data space into two dimensions while still maintaining the structure of the data.įlowJo v10 now comes with a dimensionality reduction algorithm plugin called t-Distributed Stochastic Neighbor Embedding (tSNE). What do you do with all those markers? How do you start discovering the differences between a control and a treated sample in your disease model when you don’t know what phenotypes to look at? So you’ve got access to a new high-parameter cytometry instrument, and 15+ parameter panels are calling your name.
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