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Combinatorial prediction of marker panels from single-cell transcriptomic data

By Conor Delaney, Alexandra Schnell, Louis V. Cammarata, Aaron Yao-Smith, Aviv Regev, Vijay K Kuchroo, Meromit Singer

Posted 30 May 2019
bioRxiv DOI: 10.1101/655753 (published 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 (<http://www.cometsc.com>) or a standalone software package (<https://github.com/MSingerlab/COMETSC>).

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