Khullar Saniya, Huang Xiang, Ramesh Raghu, Svaren John, Wang Daifeng
Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, United States.
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53076, United States.
Bioinform Adv. 2024 Dec 20;5(1):vbae206. doi: 10.1093/bioadv/vbae206. eCollection 2025.
Transcription factor (TF) coordination plays a key role in gene regulation via direct and/or indirect protein-protein interactions (PPIs) and co-binding to regulatory elements on DNA. Single-cell technologies facilitate gene expression measurement for individual cells and cell-type identification, yet the connection between TF-TF coordination and target gene (TG) regulation of various cell types remains unclear.
To address this, we introduce our innovative computational approach, Network Regression Embeddings (NetREm), to reveal cell-type TF-TF coordination activities for TG regulation. NetREm leverages network-constrained regularization, using prior knowledge of PPIs among TFs, to analyze single-cell gene expression data, uncovering cell-type coordinating TFs and identifying revolutionary TF-TG candidate regulatory network links. NetREm's performance is validated using simulation studies and benchmarked across several datasets in humans, mice, yeast. Further, we showcase NetREm's ability to prioritize valid novel human TF-TF coordination links in 9 peripheral blood mononuclear and 42 immune cell sub-types. We apply NetREm to examine cell-type networks in central and peripheral nerve systems (e.g. neuronal, glial, Schwann cells) and in Alzheimer's disease versus Controls. Top predictions are validated with experimental data from rat, mouse, and human models. Additional functional genomics data helps link genetic variants to our TF-TG regulatory and TF-TF coordination networks.
转录因子（TF）协同作用通过直接和/或间接的蛋白质-蛋白质相互作用（PPI）以及与DNA上调控元件的共结合在基因调控中起关键作用。单细胞技术有助于测量单个细胞的基因表达并进行细胞类型鉴定，然而TF-TF协同作用与各种细胞类型的靶基因（TG）调控之间的联系仍不清楚。
为了解决这个问题，我们引入了创新的计算方法——网络回归嵌入（NetREm），以揭示细胞类型中TF-TF协同作用对TG调控的活动。NetREm利用TF之间PPI的先验知识进行网络约束正则化，以分析单细胞基因表达数据，揭示细胞类型中协同作用的TF，并识别具有创新性的TF-TG候选调控网络链接。NetREm的性能通过模拟研究进行了验证，并在人类、小鼠、酵母的多个数据集上进行了基准测试。此外，我们展示了NetREm在9种外周血单核细胞和42种免疫细胞亚型中对有效的新型人类TF-TF协同作用链接进行优先级排序的能力。我们应用NetREm来检查中枢和外周神经系统中的细胞类型网络（例如神经元、神经胶质细胞、施万细胞）以及阿尔茨海默病与对照的情况。顶级预测结果通过来自大鼠、小鼠和人类模型的实验数据得到了验证。额外的功能基因组学数据有助于将基因变异与我们的TF-TG调控和TF-TF协同作用网络联系起来。
