Robust enhancer-gene regulation identified by single-cell transcriptomes and epigenomes
Abstract: Single-cell sequencing could help to solve the fundamental challenge of linking millions of cell-type-specific enhancers with their target genes. However, this task is confounded by patterns of gene co-expression in much the same way that genetic correlation due to linkage disequilibrium confounds fine-mapping in genome-wide association studies (GWAS). We developed a non-parametric permutation-based procedure to establish stringent statistical criteria to control the risk of false-positive associations in enhancer-gene association studies (EGAS). We applied our procedure to large-scale transcriptome and epigenome data from multiple tissues and species, including the mouse and human brain, to predict enhancer-gene associations genome wide. We tested the functional validity of our predictions by comparing them with chromatin conformation data and causal enhancer perturbation experiments. Our study shows how controlling for gene co-expression enables robust enhancer-gene linkage using single-cell sequencing data
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
Cell genomics. - 3, 7 (2023) , 100342, ISSN: 2666-979X
- Klassifikation
-
Biowissenschaften, Biologie
- Ereignis
-
Veröffentlichung
- (wo)
-
Freiburg
- (wer)
-
Universität
- (wann)
-
2023
- Urheber
-
Xie, Fangming
Armand, Ethan J.
Yao, Zizhen
Liu, Hanqing
Bartlett, Anna
Behrens, M. Margarita
Li, Yang Eric
Lucero, Jacinta D.
Luo, Chongyuan
Nery, Joseph R.
Pinto-Duarte, Antonio
Poirion, Olivier B.
Preißl, Sebastian
Rivkin, Angeline C.
Tasic, Bosiljka
Zeng, Hongkui
Ren, Bing
Ecker, Joseph R.
Mukamel, Eran A.
- DOI
-
10.1016/j.xgen.2023.100342
- URN
-
urn:nbn:de:bsz:25-freidok-2381987
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
25.03.2025, 13:42 MEZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Xie, Fangming
- Armand, Ethan J.
- Yao, Zizhen
- Liu, Hanqing
- Bartlett, Anna
- Behrens, M. Margarita
- Li, Yang Eric
- Lucero, Jacinta D.
- Luo, Chongyuan
- Nery, Joseph R.
- Pinto-Duarte, Antonio
- Poirion, Olivier B.
- Preißl, Sebastian
- Rivkin, Angeline C.
- Tasic, Bosiljka
- Zeng, Hongkui
- Ren, Bing
- Ecker, Joseph R.
- Mukamel, Eran A.
- Universität
Entstanden
- 2023
Ähnliche Objekte (12)
scMEGA : Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference
Integrated analysis of single-cell chromatin state and transcriptome identified common vulnerability despite glioblastoma heterogeneity
Single-cell and single-molecule methods
Technologies for single-cell isolation
Robust and efficient single-cell Hi-C clustering with approximate k-nearest neighbor graphs
Robust inference in models identified via heteroskedasticity
Robust Bayesian inference for set-identified models
Robust Bayesian inference for set-identified models
Statistical Models for Single-Cell Genetics
Improving & applying single-cell RNA sequencing
Adult neurogenesis at single-cell resolution
Modeling single-cell perturbations using deep learning
scMEGA : Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference
Integrated analysis of single-cell chromatin state and transcriptome identified common vulnerability despite glioblastoma heterogeneity
Single-cell and single-molecule methods
Technologies for single-cell isolation
Robust and efficient single-cell Hi-C clustering with approximate k-nearest neighbor graphs
Robust inference in models identified via heteroskedasticity
Robust Bayesian inference for set-identified models
Robust Bayesian inference for set-identified models
Statistical Models for Single-Cell Genetics
Improving & applying single-cell RNA sequencing
Adult neurogenesis at single-cell resolution
Modeling single-cell perturbations using deep learning
scMEGA : Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference
Integrated analysis of single-cell chromatin state and transcriptome identified common vulnerability despite glioblastoma heterogeneity
Single-cell and single-molecule methods
Technologies for single-cell isolation
Robust and efficient single-cell Hi-C clustering with approximate k-nearest neighbor graphs
Robust inference in models identified via heteroskedasticity
Robust Bayesian inference for set-identified models
Robust Bayesian inference for set-identified models
Statistical Models for Single-Cell Genetics
Improving & applying single-cell RNA sequencing
Adult neurogenesis at single-cell resolution