Using t-SNE Transformations in FCS Express
High-dimensional single-cell technologies, such as multicolor flow cytometry, mass cytometry, and image cytometry, can measure dozens of parameters at the single-cell level. FCS Express integrates t-Distributed Stochastic Neighbor Embedding, otherwise known as t-SNE, which is a tool that allows you to map high-dimensional cytometry data onto a two dimension plot while conserving the original high-dimensional structure to help you visualize and analyze high-dimensional data.
The final result of the algorithm in FCS Express is a 2D plot in which the positions of cells reflect their proximity in their original high-dimensional space. Plots can further be colored with density or heat mapping of each parameter, allowing for easy visualization of populations.
Given that t-SNE is an highly demanding algorithm, the De Novo Software 's team made a great effort to improve its speed within FCS Express.
To give you an idea of how t-SNE is performing within FCS Express, we have run some speed tests to show how the two methods that are used to calculate t-SNE compare with each other in version 6.
The table below shows the elapsed time (in seconds) for t-SNE calculation using the Barnes-Hut Approximation (Amount of Approximation = 0.5)with a different combination of number of considered events and number of considered parameters. In these tests, t-SNE was not estimated for unsampled events. Please refer to the Defining a t-SNE Transformation section in the software reference manual for more information about the methods and options that are available in FCS Express.
We then repeated the previous tests by enabling the Estimate t-SNE for Unsampled Events option. By enabling this option, the events that did not participate in the t-SNE calculation will be mapped to the nearest point that did participate in the t-SNE mapping. With the file used for this test, the estimation had been performed on almost half a million of events. The result of these tests can be seen in the table and scatter plots below.
The chart below shows how t-SNE in FCS Express compares to tSNE as performed in R:
t-SNE and vSNE Frequently Asked Questions
Learn more about tSNE in FCS Express via a recorded webinar.