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http://hdl.handle.net/1942/33764
Title: | SCENIC: single-cell regulatory network inference and clustering | Authors: | Sara Aibar Carmen Bravo González-Blas Thomas Moerman Vân Anh Huynh-Thu Hana Imrichova Gert Hulselmans Florian Rambow Jean-Christophe Marine Pierre Geurts AERTS, Jan Joost van den Oord Zeynep Kalender Atak Jasper Wouters Stein Aerts |
Issue Date: | 2017 | Publisher: | Springer Science and Business Media {LLC} | Source: | Nature Methods, 14 (11) , p. 1083 -1086 | Abstract: | We present SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data (http://scenic.aertslab.org). On a compendium of single-cell data from tumors and brain, we demonstrate that cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states. SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity. | Keywords: | Algorithms;Animals;Brain;Cluster Analysis;Gene Expression Profiling;Humans;Mice;Gene Regulatory Networks;Single-Cell Analysis | Document URI: | http://hdl.handle.net/1942/33764 | Link to publication/dataset: | https://doi.org/10.1038/nmeth.4463 | ISBN: | 15487105 15487091 | ISSN: | 1548-7091 | e-ISSN: | 1548-7105 | DOI: | 10.1038/nmeth.4463 | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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