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bioRxiv · 10.1101/2020.03.04.977140

Polar Gini Curve: a Technique to Discover Single-cell Biomarker Using 2D Visual Information

Nguyen, T. M.·Jeevan, J. J.·Xu, N.·Chen, J. Y.
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Abstract

In this work, we design the Polar Gini Curve (PGC) technique, which combines the gene expression and the 2D embedded visual information to detect biomarkers from single-cell data. Theoretically, a Polar Gini Curve characterizes the shape and evenness of cell-point distribution of cell-point set. To quantify whether a gene could be a marker in a cell cluster, we can combine two Polar Gini Curves: one drawn upon the cell-points expressing the gene, and the other drawn upon all cell-points in the cluster. We hypothesize that the closers these two curves are, the more likely the gene would be cluster markers. We demonstrate the framework in several simulation case-studies. Applying our framework in analyzing neonatal mouse heart single-cell data, the detected biomarkers may characterize novel subtypes of cardiac muscle cells. The source code and data for PGC could be found at https://figshare.com/projects/Polar_Gini_Curve/76749.

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Nguyen, T. M., Jeevan, J. J., Xu, N., Chen, J. Y.. 2020-03-05. Polar Gini Curve: a Technique to Discover Single-cell Biomarker Using 2D Visual Information. https://doi.org/10.1101/2020.03.04.977140

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