#Fcs express download

To use this for tSNE analysis, the user must select the number of events to be downsampled (plotted as “sample size” in the graphs below), save the layout, wait for the downsampling to finish, and use the tSNE plugin to calculate tSNE. Thus, as in Cytobank, the time for downsampling is automatically part of the tSNE calculation time itself.įlowJo® requires installation of the DownSample plugin. Thus, the time for downsampling is automatically part of the viSNE (tSNE) calculation time itself.įCS Express™ does not require a separate downsampling step, as “sample size” is built into the FCSE Express tSNE transformation Tool. Cytobank™ does not require a separate downsampling step, as “desired total events” is a setting built into the viSNE (tSNE) module. The queue waiting time is likely variable, depending on how many other people around the world have samples waiting to be analyzed by tSNE, so your mileage may vary. The timings below include both the upload and wait time (in these tests, these were under 2 minutes each, for a total of ~4 minutes). These are presented below, in alphabetical order.Ĭytobank™ requires uploading the data to the cloud, where it can inform you that your data is in a queue to be processed. For those more sophisticated, and as a benchmark, the freely available R implementation of tSNE was also run.īefore the results are revealed and the winner of the first tSNE speed race is named, it is important to understand how the timing was done and the steps in each implementation. The competitors in this test were: Cytobank™, FCS Express™, and FlowJo®.

#Fcs express download

The question becomes, “How fast can each of these implementations perform the tSNE analysis on a standard file, using a typical desktop computer?” In the interest of fairness, you can download the file that was used and the method for running the competition here. There are several commercially available implementations of the tSNE algorithm available on the market. It becomes a balancing act between adding more data and keeping the overall analysis time manageable. The goal of our high-dimensional experiments is to identify changes in the experimental system, finding those rare events that allow for a more complete understanding of the biology. This brings us back to the need for speed. Since these low frequency events are often the pieces of data the research is most interested in, the larger the sample size that can be processed, the less likely this is to occur. When the data is downsampled, there is a probability that rare events will be removed from the data. However, if you are a true audiophile, for example, there is a difference between an electronic copy of a piece of music and hearing it from the original source. This happens all time in our daily lives and generally we don’t notice it.

In order to complete the tSNE algorithm in a reasonable amount of time, most datasets are downsampled.ĭownsampling is a process where a smaller number of events is used as representative of the whole sample. 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.