Oubounyt Mhaned, Elkjaer Maria L, Laske Tanja, Grønning Alexander G B, Moeller Marcus J, Baumbach Jan
Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany.
Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
NAR Genom Bioinform. 2023 Mar 3;5(1):lqad018. doi: 10.1093/nargab/lqad018. eCollection 2023 Mar.
Single-cell RNA sequencing (scRNA-seq) technology provides an unprecedented opportunity to understand gene functions and interactions at single-cell resolution. While computational tools for scRNA-seq data analysis to decipher differential gene expression profiles and differential pathway expression exist, we still lack methods to learn differential regulatory disease mechanisms directly from the single-cell data. Here, we provide a new methodology, named DiNiro, to unravel such mechanisms and report them as small, easily interpretable transcriptional regulatory network modules. We demonstrate that DiNiro is able to uncover novel, relevant, and deep mechanistic models that not just predict but explain differential cellular gene expression programs. DiNiro is available at https://exbio.wzw.tum.de/diniro/.
单细胞RNA测序（scRNA-seq）技术为在单细胞分辨率下理解基因功能和相互作用提供了前所未有的机会。虽然存在用于scRNA-seq数据分析以破译差异基因表达谱和差异通路表达的计算工具，但我们仍然缺乏直接从单细胞数据中了解差异调控疾病机制的方法。在这里，我们提供了一种名为DiNiro的新方法，以揭示此类机制，并将其报告为小型、易于解释的转录调控网络模块。我们证明，DiNiro能够发现新颖、相关且深入的机制模型，这些模型不仅能预测而且能解释差异细胞基因表达程序。DiNiro可在https://exbio.wzw.tum.de/diniro/获取。
