Combinatorial prediction of marker panels from single-cell transcriptomic data

Mol Syst Biol. 2019 Oct;15(10):e9005. doi: 10.15252/msb.20199005.


Single-cell transcriptomic studies are identifying novel cell populations with exciting functional roles in various in vivo contexts, but identification of succinct gene marker panels for such populations remains a challenge. In this work, we introduce COMET, a computational framework for the identification of candidate marker panels consisting of one or more genes for cell populations of interest identified with single-cell RNA-seq data. We show that COMET outperforms other methods for the identification of single-gene panels and enables, for the first time, prediction of multi-gene marker panels ranked by relevance. Staining by flow cytometry assay confirmed the accuracy of COMET's predictions in identifying marker panels for cellular subtypes, at both the single- and multi-gene levels, validating COMET's applicability and accuracy in predicting favorable marker panels from transcriptomic input. COMET is a general non-parametric statistical framework and can be used as-is on various high-throughput datasets in addition to single-cell RNA-sequencing data. COMET is available for use via a web interface ( or a stand-alone software package (

Keywords: cell types; computational biology; data analysis; marker panel; single-cell RNA-seq.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Computational Biology
  • Gene Expression Profiling / methods*
  • Gene Expression Regulation
  • Genetic Markers*
  • High-Throughput Nucleotide Sequencing
  • Mice
  • Sequence Analysis, RNA
  • Single-Cell Analysis / methods*


  • Genetic Markers