ACCENSE is a standalone application for exploratory analysis of high-dimensional single-cell data such as that generated by Mass Cytometry ($$CyTOF^{TM}$$, Fluidigm Corp.). The main functions that ACCENSE provides are:

• It performs a nonlinear dimensionality reduction on the high-dimensional single-cell data and obtains the inferred low-dimensional representation.
• The low dimensional data can be visualized with abundant coloring options.
• It provides clustering methods to automate the classification of cellular sub-populations.

ACCENSE is developed based on the algorithm proposed in [ACCENSE2014], with improved classification methods. It is written in Python and employs the following third-party packages.

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Reference¶

 [ACCENSE2014] K Shekhar, P Brodin, MM Davis, AK Chakraborty, Automatic classification of cellular expression by nonlinear stochastic embedding (ACCENSE), Proceedings of the National Academy of Sciences, 2014