Combinatorial prediction of marker panels from single‐cell transcriptomic data
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 t...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2019-10-01
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| Series: | Molecular Systems Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.15252/msb.20199005 |
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| _version_ | 1849235321734037504 |
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| author | Conor Delaney Alexandra Schnell Louis V Cammarata Aaron Yao‐Smith Aviv Regev Vijay K Kuchroo Meromit Singer |
| author_facet | Conor Delaney Alexandra Schnell Louis V Cammarata Aaron Yao‐Smith Aviv Regev Vijay K Kuchroo Meromit Singer |
| author_sort | Conor Delaney |
| collection | DOAJ |
| 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 ). |
| format | Article |
| id | doaj-art-8ae8c8b133ec45cba9848dd63f118b80 |
| institution | Kabale University |
| issn | 1744-4292 |
| language | English |
| publishDate | 2019-10-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-8ae8c8b133ec45cba9848dd63f118b802025-08-20T04:02:50ZengSpringer NatureMolecular Systems Biology1744-42922019-10-01151011810.15252/msb.20199005Combinatorial prediction of marker panels from single‐cell transcriptomic dataConor Delaney0Alexandra Schnell1Louis V Cammarata2Aaron Yao‐Smith3Aviv Regev4Vijay K Kuchroo5Meromit Singer6Department of Data Sciences, Dana‐Farber Cancer InstituteEvergrande Center for Immunologic Diseases and Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's HospitalDepartment of Statistics, Harvard UniversityDepartment of Computer Science, Cornell UniversityDepartment of Biology and Koch Institute of Integrative Cancer Research, Howard Hughes Medical Institute, Massachusetts Institute of TechnologyEvergrande Center for Immunologic Diseases and Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's HospitalDepartment of Data Sciences, Dana‐Farber Cancer InstituteAbstract 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 ).https://doi.org/10.15252/msb.20199005cell typescomputational biologydata analysismarker panelsingle‐cell RNA‐seq |
| spellingShingle | Conor Delaney Alexandra Schnell Louis V Cammarata Aaron Yao‐Smith Aviv Regev Vijay K Kuchroo Meromit Singer Combinatorial prediction of marker panels from single‐cell transcriptomic data Molecular Systems Biology cell types computational biology data analysis marker panel single‐cell RNA‐seq |
| 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 | cell types computational biology data analysis marker panel single‐cell RNA‐seq |
| url | https://doi.org/10.15252/msb.20199005 |
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