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ACCENSE is a tool for exploratory analysis of high-dimensional single-cell data such as that generated by Mass Cytometry (CyTOF™, Fluidigm Corp.). By combining a nonlinear dimensionality reduction algorithm (t-SNE or Barnes-Hut SNE) with a k-means clustering algorithm both visualization for exploratory analysis and automated cell classification into subpopulations is performed.

By automating cell classification, yet retaining single-cell resolution, ACCENSE facilitates exploratory data analysis while circumventing the need for any manual "gating". Importantly, by outputting tabular single-cell data, ACCENSE facilitates downstream statistical analysis.

The dimensionality reduction step within ACCENSE employs t-distributed Stochastic Neighbor Embedding (t-SNE), published in van der Maaten L and Hinton G, Journal of Machine Learning Research, 9 (2008) 2579-2605 or a Barnes-Hut SNE algorithm published in van der Maaten, Proceedings of the International Conference on Learning Representations, 2013.

ACCENSE was developed by Petter Brodin (Karolinska Institutet and previously Mark Davis lab at Stanford University), and Karthik Shekhar (prof. Arup Chakraborty at M.I.T. and the Ragon Institute of MIT, MGH and Harvard).
The ACCENSE User Interface was developed and is maintained by dr Yang Chen, senior data scientist at Karolinska Institutet.