Cargando…
Mapping gene regulatory networks from single-cell omics data
Single-cell techniques are advancing rapidly and are yielding unprecedented insight into cellular heterogeneity. Mapping the gene regulatory networks (GRNs) underlying cell states provides attractive opportunities to mechanistically understand this heterogeneity. In this review, we discuss recently...
Autores principales: | Fiers, Mark W E J, Minnoye, Liesbeth, Aibar, Sara, Bravo González-Blas, Carmen, Kalender Atak, Zeynep, Aerts, Stein |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6063279/ https://www.ncbi.nlm.nih.gov/pubmed/29342231 http://dx.doi.org/10.1093/bfgp/elx046 |
Ejemplares similares
-
SCENIC: Single-cell regulatory network inference and clustering
por: Aibar, Sara, et al.
Publicado: (2017) -
Identification of High-Impact cis-Regulatory Mutations Using Transcription Factor Specific Random Forest Models
por: Svetlichnyy, Dmitry, et al.
Publicado: (2015) -
SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
por: Bravo González-Blas, Carmen, et al.
Publicado: (2023) -
Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks
por: Kalender Atak, Zeynep, et al.
Publicado: (2017) -
Interpretation of allele-specific chromatin accessibility using cell state–aware deep learning
por: Atak, Zeynep Kalender, et al.
Publicado: (2021)