If large numbers of cells were run on the original t-SNE algorithm, the t-SNE plot would look like one population of cells without distinct islands. The problem with this is that important information could be lost by downsampling, especially if there are rare populations that are of interest. With the original t-SNE algorithm, many users needed to downsample their data in order to get results. Anna Belkina, who modified the t-SNE algorithm to be able to examine more data points ( ). With the ongoing interest for examining more and more parameters on on a single cell, researchers are turning to unsupervised methods for high-parameter data analysis. Efforts need to be put in training in computational analysis tools.Once again, GLIIFCA proved to be a thought-provoking meeting. If you want a look at any of these presentations, let me know! The CAT Facility was well represented this year, with a presentation from Laura Johnston on our Spectral Flow training program during the Cytek pre-GLIIFCA User Group meeting, a poster from Bert on the identification of high-performance brewer yeast using the Image Stream and my presentation on marketing strategies to promote new technologies in a core facility. It reliably offers a stellar scientific program as well as the opportunity to learn about the very latest developments in the field from others attendees and vendors alike. While it remains a hidden gem for the greater research community, it is an inescapable meeting for core facility people in the field of flow cytometry and imaging.
We just came back from the Great Lakes International Imaging and Flow Cytometry Association held in Troy, Michigan.
0 kommentar(er)
