Combinatorial prediction of marker panels from single‐cell transcriptomic data
Name
msb.20199005.pdf
Description
Published version
Size
2.36 MB
Format
Adobe PDF
Checksum (MD5)
733e9420d4537a0244d02750b3c527f6
Author(s)
Regev, Aviv
Kuchroo, Vijay K
Singer, Meromit
Date Issued
October 2019
Journal
Molecular Systems Biology
Publisher
EMBO
Citation
Delaney, Conor et al. “Combinatorial prediction of marker panels from single‐cell transcriptomic data.” Molecular Systems Biology 15 (2019): e9005 © 2019 The Author(s)
Version
Final published version
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).
Subjects
General Biochemistry, Genetics and Molecular Biology
Computational Theory and Mathematics
General Immunology and Microbiology
Applied Mathematics
General Agricultural and Biological Sciences
Information Systems
MIT Department
Massachusetts Institute of Technology. Department of Biology
Koch Institute for Integrative Cancer Research at MIT
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.15252/msb.20199005
