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Abstract：
There is a growing interest in inferring context specific gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data. This involves identifying the regulatory relationships between transcription factors (TFs) and genes in individual cells, and then characterizing these relationships at the level of specific cell types or cell states. In this study, we introduce scGATE (single-cell gene regulatory gate) as a novel computational tool for inferring TF-gene interaction networks and reconstructing Boolean logic gates involving regulatory TFs using scRNA-seq data. In contrast to current Boolean models, scGATE eliminates the need for individual formulations and likelihood calculations for each Boolean rule (e.g. AND, OR, XOR). By employing a Bayesian framework, scGATE infers the Boolean rule after fitting the model to the data, resulting in significant reductions in time-complexities for logic-based studies. We have applied assay for transposase-accessible chromatin with sequencing (scATAC-seq) data and TF DNA binding motifs to filter out non-relevant TFs in gene regulations. By integrating single-cell clustering with these external cues, scGATE is able to infer context specific networks. The performance of scGATE is evaluated using synthetic and real single-cell multi-omics data from mouse tissues and human blood, demonstrating its superiority over existing tools for reconstructing TF-gene networks. Additionally, scGATE provides a flexible framework for understanding the complex combinatorial and cooperative relationships among TFs regulating target genes by inferring Boolean logic gates among them.
摘要：
从单细胞RNA测序(scRNA-seq)数据推断背景特异性基因调控网络的兴趣日益增加。这涉及到在单个细胞中确定转录因子（TFs）和基因之间的调控关系，然后在特定细胞类型或细胞状态的水平上表征这些关系。在这项研究中,我们引入scGATE（单细胞基因调控门）作为一种新的计算工具，用于推断TF-基因相互作用网络，并使用scRNA-seq数据重建涉及调控TF的布尔逻辑门。与当前的布尔模型相比，scGATE消除了对每个布尔规则的单独公式和似然计算的需要(例如AND，OR,XOR).通过使用贝叶斯框架，scGATE在将模型拟合到数据后推断布尔规则，导致基于逻辑的研究的时间复杂度显着降低。我们已经使用测序（scATAC-seq）数据和TFDNA结合基序对转座酶可接近的染色质进行了测定，以过滤掉基因调控中的非相关TF。通过整合单细胞聚类和这些外部线索，scGATE能够推断上下文特定的网络。使用来自小鼠组织和人类血液的合成和真实的单细胞多组学数据来评估scGATE的性能，证明其优于现有的重建TF基因网络的工具。此外,scGATE提供了一个灵活的框架，通过推断其中的布尔逻辑门，来理解调节靶基因的TFs之间复杂的组合和合作关系。
