SCENIC: single-cell regulatory network inference and clustering.

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.

• Authors: Sara Aibar, Carmen Bravo González-Blas, Thomas Moerman, Vân Anh Huynh-Thu, Hana Imrichova, Gert Hulselmans, Florian Rambow, Jean-Christophe Marine, Pierre Geurts, Jan Aerts, Joost van den Oord, Zeynep Kalender Atak, Jasper Wouters, Stein Aerts
• Journal: Nature methods (Nat Methods) Vol. 14 Issue 11 Pg. 1083-1086 (Nov 2017) ISSN: 1548-7105 [Electronic] United States
• PMID: 28991892 (Publication Type: Journal Article)

Topics

• Algorithms
• Animals
• Brain (metabolism)
• Cluster Analysis
• Gene Expression Profiling
• Gene Regulatory Networks
• Humans
• Mice
• Single-Cell Analysis