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Computational Cytometry | Flow Cytometry Data Analysis in the Era of Quantitative Data Science

News from FlowMetric

  • Test Tube Throwdown – Finding the Right Tube for the Job View Post Summary

    High quality flow cytometry results start with proper collection, processing and storage of cell specimens. Several factors determine which type of test tube to use for sample collection, including the cell type you are studying, and your preservation and shipping needs.

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  • Pairing Two Techniques: Cell Sorting & Mass Spectrometry View Post Summary

    What is the Technology?

    Flow Cytometry and Mass Spectrometry are widely used analytical tools in cancer research. Flow Cytometry (FACS or Fluorescence Activated Cell Sorting) has been well established for cell counting, cell sorting and biomarker discovery. Its ability to utilize 14+ colors makes it the technique-of-choice for isolation of many cell subtypes especially rare cells such as stem cells. Mass Spectrometry is unparalleled in its ability to selectively detect and quantify target proteins at the molecular level. By combining these two powerful techniques, it is possible to isolate highly selective cellular populations from biological fluids and quantify the cell’s specific proteins.

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  • Data Visualization – t-SNE Plots Explained View Post Summary

    Flow cytometry has moved into the era of “big data,” and data plots have also shifted from scatter plots and histograms toward visualization methods that can handle this complex data. This era of computational cytometry has ushered in the use of T-distributed Stochastic Neighbor Embedding (t-SNE) data analysis, a machine learning algorithm applied to complex flow cytometry data. Check out these three reasons why t-SNE data analysis is a valuable data visualization tool for flow cytometry.   

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