Combinatorial prediction of marker panels from single‐cell transcriptomic data
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 identi...
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| Format: | Article |
| Language: | English |
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EMBO
2020
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| Online Access: | https://hdl.handle.net/1721.1/124945 |
| _version_ | 1826213325582630912 |
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| author | Regev, Aviv Kuchroo, Vijay K Singer, Meromit |
| author2 | Massachusetts Institute of Technology. Department of Biology |
| author_facet | Massachusetts Institute of Technology. Department of Biology Regev, Aviv Kuchroo, Vijay K Singer, Meromit |
| author_sort | Regev, Aviv |
| collection | MIT |
| description | 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). |
| first_indexed | 2024-09-23T15:47:16Z |
| format | Article |
| id | mit-1721.1/124945 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:47:16Z |
| publishDate | 2020 |
| publisher | EMBO |
| record_format | dspace |
| spelling | mit-1721.1/1249452022-10-02T04:05:20Z Combinatorial prediction of marker panels from single‐cell transcriptomic data Regev, Aviv Kuchroo, Vijay K Singer, Meromit Massachusetts Institute of Technology. Department of Biology Koch Institute for Integrative Cancer Research at MIT General Biochemistry, Genetics and Molecular Biology Computational Theory and Mathematics General Immunology and Microbiology Applied Mathematics General Agricultural and Biological Sciences Information Systems 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). National Institute of Allergy and Infectious Diseases (U.S.) (Award P01AI129880) 2020-04-30T18:10:07Z 2020-04-30T18:10:07Z 2019-10 2020-01-28T19:02:58Z Article http://purl.org/eprint/type/JournalArticle 1744-4292 1744-4292 https://hdl.handle.net/1721.1/124945 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 10.15252/msb.20199005 Molecular Systems Biology Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf EMBO EMBO Press |
| spellingShingle | General Biochemistry, Genetics and Molecular Biology Computational Theory and Mathematics General Immunology and Microbiology Applied Mathematics General Agricultural and Biological Sciences Information Systems Regev, Aviv Kuchroo, Vijay K Singer, Meromit Combinatorial prediction of marker panels from single‐cell transcriptomic data |
| title | Combinatorial prediction of marker panels from single‐cell transcriptomic data |
| title_full | Combinatorial prediction of marker panels from single‐cell transcriptomic data |
| title_fullStr | Combinatorial prediction of marker panels from single‐cell transcriptomic data |
| title_full_unstemmed | Combinatorial prediction of marker panels from single‐cell transcriptomic data |
| title_short | Combinatorial prediction of marker panels from single‐cell transcriptomic data |
| title_sort | combinatorial prediction of marker panels from single cell transcriptomic data |
| topic | General Biochemistry, Genetics and Molecular Biology Computational Theory and Mathematics General Immunology and Microbiology Applied Mathematics General Agricultural and Biological Sciences Information Systems |
| url | https://hdl.handle.net/1721.1/124945 |
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