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SCENIC: Single-cell regulatory network inference and clustering
Although single-cell RNA-seq is revolutionizing biology, data interpretation remains a challenge. We present SCENIC for the simultaneous reconstruction of gene regulatory networks and identification of cell states. We apply SCENIC to a compendium of single-cell data from tumors and brain, and demons...
| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
2017
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5937676/ https://www.ncbi.nlm.nih.gov/pubmed/28991892 http://dx.doi.org/10.1038/nmeth.4463 |
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| author | Aibar, Sara González-Blas, Carmen Bravo Moerman, Thomas Huynh-Thu, Vân Anh Imrichova, Hana Hulselmans, Gert Rambow, Florian Marine, Jean-Christophe Geurts, Pierre Aerts, Jan van den Oord, Joost Atak, Zeynep Kalender Wouters, Jasper Aerts, Stein |
| author_facet | Aibar, Sara González-Blas, Carmen Bravo Moerman, Thomas Huynh-Thu, Vân Anh Imrichova, Hana Hulselmans, Gert Rambow, Florian Marine, Jean-Christophe Geurts, Pierre Aerts, Jan van den Oord, Joost Atak, Zeynep Kalender Wouters, Jasper Aerts, Stein |
| author_sort | Aibar, Sara |
| collection | PubMed |
| description | Although single-cell RNA-seq is revolutionizing biology, data interpretation remains a challenge. We present SCENIC for the simultaneous reconstruction of gene regulatory networks and identification of cell states. We apply SCENIC to a compendium of single-cell data from tumors and brain, and demonstrate that the genomic regulatory code can be exploited to guide the identification of transcription factors and cell states. SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity. |
| format | Online Article Text |
| id | pubmed-5937676 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2017 |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-59376762018-05-07 SCENIC: Single-cell regulatory network inference and clustering Aibar, Sara González-Blas, Carmen Bravo Moerman, Thomas Huynh-Thu, Vân Anh Imrichova, Hana Hulselmans, Gert Rambow, Florian Marine, Jean-Christophe Geurts, Pierre Aerts, Jan van den Oord, Joost Atak, Zeynep Kalender Wouters, Jasper Aerts, Stein Nat Methods Article Although single-cell RNA-seq is revolutionizing biology, data interpretation remains a challenge. We present SCENIC for the simultaneous reconstruction of gene regulatory networks and identification of cell states. We apply SCENIC to a compendium of single-cell data from tumors and brain, and demonstrate that the genomic regulatory code can be exploited to guide the identification of transcription factors and cell states. SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity. 2017-10-09 2017-11 /pmc/articles/PMC5937676/ /pubmed/28991892 http://dx.doi.org/10.1038/nmeth.4463 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms |
| spellingShingle | Article Aibar, Sara González-Blas, Carmen Bravo Moerman, Thomas Huynh-Thu, Vân Anh Imrichova, Hana Hulselmans, Gert Rambow, Florian Marine, Jean-Christophe Geurts, Pierre Aerts, Jan van den Oord, Joost Atak, Zeynep Kalender Wouters, Jasper Aerts, Stein SCENIC: Single-cell regulatory network inference and clustering |
| title | SCENIC: Single-cell regulatory network inference and clustering |
| title_full | SCENIC: Single-cell regulatory network inference and clustering |
| title_fullStr | SCENIC: Single-cell regulatory network inference and clustering |
| title_full_unstemmed | SCENIC: Single-cell regulatory network inference and clustering |
| title_short | SCENIC: Single-cell regulatory network inference and clustering |
| title_sort | scenic: single-cell regulatory network inference and clustering |
| topic | Article |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5937676/ https://www.ncbi.nlm.nih.gov/pubmed/28991892 http://dx.doi.org/10.1038/nmeth.4463 |
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