Center for Translational Immunology, Department of Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Commun Biol. 2022 Jan 11;5(1):31. doi: 10.1038/s42003-021-02991-5.
Changes in a few key transcriptional regulators can lead to different biological states. Extracting the key gene regulators governing a biological state allows us to gain mechanistic insights. Most current tools perform pathway/GO enrichment analysis to identify key genes and regulators but tend to overlook the gene/protein regulatory interactions. Here we present RegEnrich, an open-source Bioconductor R package, which combines differential expression analysis, data-driven gene regulatory network inference, enrichment analysis, and gene regulator ranking to identify key regulators using gene/protein expression profiling data. By benchmarking using multiple gene expression datasets of gene silencing studies, we found that RegEnrich using the GSEA method to rank the regulators performed the best. Further, RegEnrich was applied to 21 publicly available datasets on in vitro interferon-stimulation of different cell types. Collectively, RegEnrich can accurately identify key gene regulators from the cells under different biological states, which can be valuable in mechanistically studying cell differentiation, cell response to drug stimulation, disease development, and ultimately drug development.
一些关键转录调控因子的变化可导致不同的生物状态。提取控制生物状态的关键基因调控因子可使我们深入了解其机制。目前大多数工具都通过通路/GO 富集分析来识别关键基因和调控因子，但往往忽略了基因/蛋白质的调控相互作用。我们在此介绍 RegEnrich，这是一个开源的 Bioconductor R 包，它结合了差异表达分析、数据驱动的基因调控网络推断、富集分析和基因调控因子排名，使用基因/蛋白质表达谱数据来识别关键调控因子。通过使用多个基因沉默研究的基因表达数据集进行基准测试，我们发现 RegEnrich 使用 GSEA 方法对调控因子进行排名的效果最佳。此外，RegEnrich 还应用于 21 个公开的体外干扰素刺激不同细胞类型的数据集。总的来说，RegEnrich 可以从不同生物状态的细胞中准确识别关键基因调控因子，这对于深入研究细胞分化、细胞对药物刺激的反应、疾病发展以及最终药物开发具有重要意义。
