Optimal marker gene selection for cell type discrimination in single cell analyses

Bianca Dumitrascu; Soledad Villar; Dustin G. Mixon; Barbara E. Engelhardt

doi:10.1038/s41467-021-21453-4

Nature Communications (Feb 2021)

Optimal marker gene selection for cell type discrimination in single cell analyses

Bianca Dumitrascu,
Soledad Villar,
Dustin G. Mixon,
Barbara E. Engelhardt

Affiliations

Bianca Dumitrascu: Department of Computer Science and Technology, University of Cambridge
Soledad Villar: Department of Applied Mathematics and Statistics, Johns Hopkins University
Dustin G. Mixon: Department of Mathematics, The Ohio State University
Barbara E. Engelhardt: Department of Computer Science, Princeton University

DOI: https://doi.org/10.1038/s41467-021-21453-4
Journal volume & issue: Vol. 12, no. 1
pp. 1 – 8

Abstract

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The selection of a small set of cellular labels to distinguish a subpopulation of cells from a complex mixture is an important task in cell biology. Here the authors propose a method for supervised genetic marker selection using linear programming and provides a Python package scGeneFit that implements this approach.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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