Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, United States.
Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, United States.
Brief Bioinform. 2024 Jul 25;25(5). doi: 10.1093/bib/bbae369.
Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks (eGRNs) is crucial in understanding gene regulatory programs in a complex biological system. This study introduces STREAM, a novel method that leverages a Steiner forest problem model, a hybrid biclustering pipeline, and submodular optimization to infer eGRNs from jointly profiled single-cell transcriptome and chromatin accessibility data. Compared to existing methods, STREAM demonstrates enhanced performance in terms of TF recovery, TF-enhancer linkage prediction, and enhancer-gene relation discovery. Application of STREAM to an Alzheimer's disease dataset and a diffuse small lymphocytic lymphoma dataset reveals its ability to identify TF-enhancer-gene relations associated with pseudotime, as well as key TF-enhancer-gene relations and TF cooperation underlying tumor cells.
通过推断增强子驱动的基因调控网络（eGRNs），解析转录因子（TFs）、增强子和基因之间错综复杂的关系，对于理解复杂生物系统中的基因调控程序至关重要。本研究介绍了 STREAM，这是一种利用 Steiner 森林问题模型、混合双聚类管道和子模优化的新方法，可从联合分析的单细胞转录组和染色质可及性数据中推断 eGRNs。与现有方法相比，STREAM 在 TF 恢复、TF-增强子连接预测和增强子-基因关系发现方面表现出更好的性能。将 STREAM 应用于阿尔茨海默病数据集和弥漫性小淋巴细胞淋巴瘤数据集，揭示了它识别与拟时间相关的 TF-增强子-基因关系、肿瘤细胞中关键的 TF-增强子-基因关系和 TF 合作的能力。
