Please use this identifier to cite or link to this item: 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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