dc.contributor.author | Regev, Aviv | |
dc.contributor.author | Kuchroo, Vijay K | |
dc.contributor.author | Singer, Meromit | |
dc.date.accessioned | 2020-04-30T18:10:07Z | |
dc.date.available | 2020-04-30T18:10:07Z | |
dc.date.issued | 2019-10 | |
dc.identifier.issn | 1744-4292 | |
dc.identifier.issn | 1744-4292 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/124945 | |
dc.description.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). | en_US |
dc.description.sponsorship | National Institute of Allergy and Infectious Diseases (U.S.) (Award P01AI129880) | en_US |
dc.language.iso | en | |
dc.publisher | EMBO | en_US |
dc.relation.isversionof | 10.15252/msb.20199005 | en_US |
dc.rights | Creative Commons Attribution 4.0 International license | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.source | EMBO Press | en_US |
dc.subject | General Biochemistry, Genetics and Molecular Biology | en_US |
dc.subject | Computational Theory and Mathematics | en_US |
dc.subject | General Immunology and Microbiology | en_US |
dc.subject | Applied Mathematics | en_US |
dc.subject | General Agricultural and Biological Sciences | en_US |
dc.subject | Information Systems | en_US |
dc.title | Combinatorial prediction of marker panels from single‐cell transcriptomic data | en_US |
dc.type | Article | en_US |
dc.identifier.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) | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Biology | en_US |
dc.contributor.department | Koch Institute for Integrative Cancer Research at MIT | en_US |
dc.relation.journal | Molecular Systems Biology | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dc.date.updated | 2020-01-28T19:02:58Z | |
dspace.date.submission | 2020-01-28T19:03:01Z | |
mit.journal.volume | 15 | en_US |
mit.journal.issue | 10 | en_US |
mit.metadata.status | Complete | |