Combinatorial Prediction of Marker Panels from Single-cell Transcriptomic Data

Overview

Journal Mol Syst Biol

Specialty Molecular Biology

Date 2019 Oct 29

PMID 31657111

Citations 54

Authors

Conor Delaney

Alexandra Schnell

Louis V Cammarata

Aaron Yao-Smith

Aviv Regev

Vijay K Kuchroo

Meromit Singer

Affiliations

Soon will be listed here.

Abstract

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 stand-alone software package (https://github.com/MSingerLab/COMETSC).

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