However, the tSNE analysis, although powerful, is very slow and memory-intensive. From these single plots, further analysis can be performed using other analytical techniques. TSNE allows for the visualization of high-dimensional data on a single bivariate plot. You can read more about it in these articles: van der Maaten and Hinton (2008), van der Maaten (2014), and Amir et al (2013). One of the most popular algorithms in flow cytometry circles is the tSNE algorithm. This has led to the desire to find analytical methods that can reduce the complexity of the data in some way to make it more manageable to find populations of interest. With all these parameters, the data files become very large very quickly, and the ability to analyze such complex data becomes increasingly difficult. Spectral cytometry may push this limit to 50 parameters or more in the near future. It didn’t take long for fluorescence-based cytometers to begin pushing past the 18-fluorochrome limit, and now instruments that can do 24 or more fluorescent parameters at the same time are available.
The CyTOF threw the gauntlet down to start this new race by changing how the signal was detected. One of the trends in flow cytometry is pushing the limit of the number of parameters that can be measured at one time. Sorting faster will impact purity of the final product. As has been discussed before, the optimal sort rate is ¼ the frequency of droplet generation. With cell sorters, Poisson statistics dominate the speed calculation. The limitations are imposed by the physics of flow cytometry, the speed of pulse processing, and more. Look at any vendor’s website and you will see the highlights on how many events per second their instrument can acquire, how many cells can be sorted per second, and more. Speed is a highly touted metric in flow cytometry.
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