scMEGA：基于单细胞多组学增强子的基因调控网络推断
scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference.
作者信息
Li Zhijian, Nagai James S, Kuppe Christoph, Kramann Rafael, Costa Ivan G
机构信息
Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Aachen 52062, Germany.
Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen 52062, Germany.
出版信息
Bioinform Adv. 2023 Jan 12;3(1):vbad003. doi: 10.1093/bioadv/vbad003. eCollection 2023.
SUMMARY
The increasing availability of single-cell multi-omics data allows to quantitatively characterize gene regulation. We here describe scMEGA (Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference) that enables an end-to-end analysis of multi-omics data for gene regulatory network inference including modalities integration, trajectory analysis, enhancer-to-promoter association, network analysis and visualization. This enables to study the complex gene regulation mechanisms for dynamic biological processes, such as cellular differentiation and disease-driven cellular remodeling. We provide a case study on gene regulatory networks controlling myofibroblast activation in human myocardial infarction.
AVAILABILITY AND IMPLEMENTATION
scMEGA is implemented in R, released under the MIT license and available from https://github.com/CostaLab/scMEGA. Tutorials are available from https://costalab.github.io/scMEGA.
SUPPLEMENTARY INFORMATION
Supplementary data are available at online.
摘要
单细胞多组学数据的可用性不断提高，使得对基因调控进行定量表征成为可能。我们在此描述了scMEGA（基于单细胞多组学增强子的基因调控网络推断），它能够对多组学数据进行端到端分析，以推断基因调控网络，包括模态整合、轨迹分析、增强子与启动子关联、网络分析和可视化。这使得能够研究动态生物学过程（如细胞分化和疾病驱动的细胞重塑）的复杂基因调控机制。我们提供了一个关于控制人类心肌梗死中肌成纤维细胞激活的基因调控网络的案例研究。
可用性和实现方式
scMEGA用R语言实现，根据麻省理工学院许可发布，可从https://github.com/CostaLab/scMEGA获取。教程可从https://costalab.github.io/scMEGA获得。
补充信息
补充数据可在网上获取。
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