Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE)

Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE)

Mass cytometry enables an unprecedented number of parameters to be measured in individual cells at a high throughput, but the large dimensionality of the resulting data severely limits approaches relying on manual "gating." Clustering cells based on phenotypic similarity comes at a loss of...

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Bibliographic Details
Main Authors: Shekhar, Karthik (Contributor), Brodin, Petter (Author), Davis, Mark M. (Author), Chakraborty, Arup K (Author)
Other Authors: Massachusetts Institute of Technology. Institute for Medical Engineering & Science (Contributor), Massachusetts Institute of Technology. Department of Biological Engineering (Contributor), Massachusetts Institute of Technology. Department of Chemical Engineering (Contributor), Massachusetts Institute of Technology. Department of Chemistry (Contributor), Massachusetts Institute of Technology. Department of Physics (Contributor), Ragon Institute of MGH, MIT and Harvard (Contributor), Chakraborty, Arup K. (Contributor)
Format: Article
Language:English
Published: National Academy of Sciences (U.S.), 2014-08-28T15:07:50Z.
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