Journal
MOLECULAR SYSTEMS BIOLOGY
Volume 15, Issue 10, Pages -Publisher
WILEY
DOI: 10.15252/msb.20199005
Keywords
cell types; computational biology; data analysis; marker panel; single-cell RNA-seq
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Funding
- German Academic Scholarship Foundation (Studienstiftung des Deutschen Volkes)
- Parker Institute for Cancer Immunotherapy
- NIAID of the National Institutes of Health [P01AI129880]
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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 ().
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