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Published February 2017 | Accepted Version
Journal Article Open

Single Cell Proteomics in Biomedicine: High-dimensional Data Acquisition, Visualization and Analysis


New insights on cellular heterogeneity in the last decade provoke the development of a variety of single cell omics tools at a lightning pace. The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. In this review, we briefly survey the state-of-the-art single cell proteomic tools with a particular focus on data acquisition and quantification, followed by an elaboration of a number of statistical and computational approaches developed to date for dissecting the high-dimensional single cell data. The underlying assumptions, unique features, and limitations of the analytical methods with the designated biological questions they seek to answer will be discussed. Particular attention will be given to those information theoretical approaches that are anchored in a set of first principles of physics and can yield detailed (and often surprising) predictions.

Additional Information

© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim. Received: October 31, 2016; Revised: January 20, 2017; Accepted: January 23, 2017. The authors acknowledge the following funding agencies and grants for support some of the work described in this Review: NIH/NCI 1U54 CA199090-01 (W.W.); 5U54 CA151819 (W.W.); the Phelps Family Foundation (W.W.); Youth Program of the National 1000 Talents Project (Q.H.S.). The authors have declared no conflict of interest.

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Accepted Version - nihms890872.pdf


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