推断基因调控网络（GRN）使我们能够更深入地了解细胞功能和疾病发病机制。单细胞 RNA 测序 (scRNA-seq) 技术的最新进展提高了 GRN 推断的准确性。然而，许多从 scRNA-seq 数据推断个体 GRN 的方法都是有限的，因为它们忽略了数据中经常存在的细胞间异质性和不同细胞亚群之间的相似性。在这里，我们提出了一个基于深度学习的框架 DeepGRNCS，用于联合推断跨细胞亚群的 GRN。我们遵循普遍接受的假设，即由于潜在的调控关系，可以根据转录因子 (TF) 的表达来预测靶基因的表达。我们最初通过使用等宽方法离散化数据散射来处理 scRNA-seq 数据。然后，我们训练深度学习模型来预测 TF 的目标基因表达。通过从表达矩阵中单独删除每个 TF，我们使用预先训练的深度模型预测来推断 TF 和基因之间的调控关系，从而构建 GRN。对于各种模拟和真实的 scRNA-seq 数据集，我们的方法优于现有的 GRN 推理方法。最后，我们将 DeepGRNCS 应用于非小细胞肺癌 scRNA-seq 数据，以识别每个细胞亚群中的关键基因并分析其生物学相关性。总之，DeepGRNCS 有效地预测了细胞亚群特异性的 GRN。源代码可在 https://github.com/Nastume777/DeepGRNCS 获取。© 作者 2024。由牛津大学出版社出版。

Inferring gene regulatory networks (GRNs) allows us to obtain a deeper understanding of cellular function and disease pathogenesis. Recent advances in single-cell RNA sequencing (scRNA-seq) technology have improved the accuracy of GRN inference. However, many methods for inferring individual GRNs from scRNA-seq data are limited because they overlook intercellular heterogeneity and similarities between different cell subpopulations, which are often present in the data. Here, we propose a deep learning-based framework, DeepGRNCS, for jointly inferring GRNs across cell subpopulations. We follow the commonly accepted hypothesis that the expression of a target gene can be predicted based on the expression of transcription factors (TFs) due to underlying regulatory relationships. We initially processed scRNA-seq data by discretizing data scattering using the equal-width method. Then, we trained deep learning models to predict target gene expression from TFs. By individually removing each TF from the expression matrix, we used pre-trained deep model predictions to infer regulatory relationships between TFs and genes, thereby constructing the GRN. Our method outperforms existing GRN inference methods for various simulated and real scRNA-seq datasets. Finally, we applied DeepGRNCS to non-small cell lung cancer scRNA-seq data to identify key genes in each cell subpopulation and analyzed their biological relevance. In conclusion, DeepGRNCS effectively predicts cell subpopulation-specific GRNs. The source code is available at https://github.com/Nastume777/DeepGRNCS.© The Author(s) 2024. Published by Oxford University Press.