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

Academic Article

Abstract

  • AbstractIn 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 athttps://figshare.com/projects/Polar_Gini_Curve/76749.
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    Author List

  • Nguyen TM; Jeevan JJ; Xu N; Chen J