Single-cell RNA sequencing has enabled to capture the gene activities at single-cell resolution, thus allowing reconstruction of cell-type-specific gene regulatory networks (GRNs). The available algorithms for reconstructing GRNs are commonly designed for bulk RNA-seq data, and few of them are applicable to analyze scRNA-seq data by dealing with the dropout events and cellular heterogeneity. In this paper, we represent the joint gene expression distribution of a gene pair as an image and propose a novel supervised deep neural network called DeepDRIM which utilizes the image of the target TF-gene pair and the ones of the potential neighbors to reconstruct GRN from scRNA-seq data. Due to the consideration of TF-gene pair’s neighborhood context, DeepDRIM can effectively eliminate the false positives caused by transitive gene–gene interactions. We compared DeepDRIM with nine GRN reconstruction algorithms designed for either bulk or single-cell RNA-seq data. It achieves evidently better performance for the scRNA-seq data collected from eight cell lines. The simulated data show that DeepDRIM is robust to the dropout rate, the cell number and the size of the training data. We further applied DeepDRIM to the scRNA-seq gene expression of B cells from the bronchoalveolar lavage fluid of the patients with mild and severe coronavirus disease 2019. We focused on the cell-type-specific GRN alteration and observed targets of TFs that were differentially expressed between the two statuses to be enriched in lysosome, apoptosis, response to decreased oxygen level and microtubule, which had been proved to be associated with coronavirus infection.

中文翻译：

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DeepDRIM：使用单细胞 RNA-seq 数据重建细胞类型特异性基因调控网络的深度神经网络

单细胞 RNA 测序能够以单细胞分辨率捕获基因活动，从而允许重建细胞类型特异性基因调控网络 (GRN)。用于重建 GRN 的可用算法通常是为批量 RNA-seq 数据设计的，其中很少有适用于通过处理丢失事件和细胞异质性来分析 scRNA-seq 数据的算法。在本文中，我们将基因对的联合基因表达分布表示为图像，并提出了一种称为 DeepDRIM 的新型监督深度神经网络，该网络利用目标 TF 基因对和潜在邻居的图像从scRNA-seq 数据。由于考虑了 TF-基因对的邻域上下文，DeepDRIM 可以有效地消除由传递基因-基因相互作用引起的误报。我们将 DeepDRIM 与为大量或单细胞 RNA-seq 数据设计的九种 GRN 重建算法进行了比较。对于从八个细胞系收集的 scRNA-seq 数据，它实现了明显更好的性能。模拟数据表明，DeepDRIM 对 dropout 率、细胞数量和训练数据的大小具有鲁棒性。我们进一步将 DeepDRIM 应用于 2019 年轻度和重度冠状病毒病患者支气管肺泡灌洗液中 B 细胞的 scRNA-seq 基因表达。我们专注于细胞类型特异性 GRN 改变并观察到差异表达的 TF 靶点两种状态之间富集溶酶体、细胞凋亡、对氧水平降低的反应和微管，已被证明与冠状病毒感染有关。