Computational tool translates complex data into simplified 2-dimensional images

In their quest to learn more about the variability of cells between and within tissues, biomedical scientists have devised tools capable of simultaneously measuring dozens of characteristics of individual cells. These technologies have led to new challenges, however, as scientists now struggle with how to make sense of the resulting trove of data. Now a solution may be at hand. Researchers at Columbia University and Stanford University have developed a computational method that enables scientists to visualize and interpret "high-dimensional" data produced by single-cell measurement technologies such as mass cytometry. The method, published in the online edition of Nature Biotechnology, has particular relevance to cancer research and therapeutics.