Columbia University researchers 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.
"Our method not only will allow scientists to explore the heterogeneity of cancer cells and to characterize drug-resistant cancer cells, but also will allow physicians to track tumor progression, identify drug-resistant cancer cells, and detect minute quantities of cancer cells that increase the risk of relapse," says Columbia University researcher Dana Pe'er.
The method, called visual interactive Stochastic Neighbor Embedding (viSNE), is based on an algorithm that translates high-dimensional data into visual representations. "Basically, viSNE provides a way to visualize very high-dimensional data in two dimensions, while maintaining the most important organization and structure of the data," Pe'er says. "Color is used as a third dimension to enable users to interactively visualize various features of the cells."
The researchers used mass cytometry and viSNE to study bone marrow cells from patients with acute myeloid leukemia, and also showed that viSNE can detect minimal residual disease.
From Columbia University
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