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Combinatorial prediction of marker panels from single‐cell transcriptomic data.

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Title: Combinatorial prediction of marker panels from single‐cell transcriptomic data.
Authors: Delaney, Conor, Schnell, Alexandra, Cammarata, Louis V, Yao‐Smith, Aaron, Regev, Aviv, Kuchroo, Vijay K, Singer, Meromit
Source: Molecular Systems Biology; Oct2019, Vol. 15 Issue 10, p1-18, 18p
Subject Terms: CELL populations, INTEGRATED software, FLOW cytometry, LATENT class analysis (Statistics), COMPUTATIONAL biology
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). Synopsis: COMET is a computational tool for marker‐panel selection from single‐cell RNA‐seq data. It generates ranked predictions of single‐ and multiple‐gene marker panels for a cell population of interest. COMET is a computational tool for combinatorial prediction of marker panels from single‐cell transcriptomic data.COMET's statistical framework enables controlling for specificity and sensitivity in predicted marker panels.Staining by flow‐cytometry validates that COMET identifies novel and favorable single‐ and multi‐gene marker panels for cellular subtypes.COMET is available via a web interface (http://www.cometsc.com/) or downloadable software package (https://github.com/MSingerLab/COMETSC). [ABSTRACT FROM AUTHOR]
Copyright of Molecular Systems Biology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Combinatorial prediction of marker panels from single‐cell transcriptomic data.
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  Data: <searchLink fieldCode="AR" term="%22Delaney%2C+Conor%22">Delaney, Conor</searchLink><br /><searchLink fieldCode="AR" term="%22Schnell%2C+Alexandra%22">Schnell, Alexandra</searchLink><br /><searchLink fieldCode="AR" term="%22Cammarata%2C+Louis+V%22">Cammarata, Louis V</searchLink><br /><searchLink fieldCode="AR" term="%22Yao‐Smith%2C+Aaron%22">Yao‐Smith, Aaron</searchLink><br /><searchLink fieldCode="AR" term="%22Regev%2C+Aviv%22">Regev, Aviv</searchLink><br /><searchLink fieldCode="AR" term="%22Kuchroo%2C+Vijay+K%22">Kuchroo, Vijay K</searchLink><br /><searchLink fieldCode="AR" term="%22Singer%2C+Meromit%22">Singer, Meromit</searchLink>
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  Data: Molecular Systems Biology; Oct2019, Vol. 15 Issue 10, p1-18, 18p
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  Data: <searchLink fieldCode="DE" term="%22CELL+populations%22">CELL populations</searchLink><br /><searchLink fieldCode="DE" term="%22INTEGRATED+software%22">INTEGRATED software</searchLink><br /><searchLink fieldCode="DE" term="%22FLOW+cytometry%22">FLOW cytometry</searchLink><br /><searchLink fieldCode="DE" term="%22LATENT+class+analysis+%28Statistics%29%22">LATENT class analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22COMPUTATIONAL+biology%22">COMPUTATIONAL biology</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  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 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). Synopsis: COMET is a computational tool for marker‐panel selection from single‐cell RNA‐seq data. It generates ranked predictions of single‐ and multiple‐gene marker panels for a cell population of interest. COMET is a computational tool for combinatorial prediction of marker panels from single‐cell transcriptomic data.COMET's statistical framework enables controlling for specificity and sensitivity in predicted marker panels.Staining by flow‐cytometry validates that COMET identifies novel and favorable single‐ and multi‐gene marker panels for cellular subtypes.COMET is available via a web interface (http://www.cometsc.com/) or downloadable software package (https://github.com/MSingerLab/COMETSC). [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Molecular Systems Biology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.15252/msb.20199005
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: INTEGRATED software
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      – SubjectFull: FLOW cytometry
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      – SubjectFull: LATENT class analysis (Statistics)
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      – TitleFull: Combinatorial prediction of marker panels from single‐cell transcriptomic data.
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            – D: 01
              M: 10
              Text: Oct2019
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              Y: 2019
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