Existing methods for gene regulatory network (GRN) inference rely on gene expression data alone or on lower resolution bulk data. Despite the recent integration of chromatin accessibility and RNA sequencing data, learning complex mechanisms from limited independent data points still presents a daunting challenge. Here we present LINGER (Lifelong neural network for gene regulation), a machine-learning method to infer GRNs from single-cell paired gene expression and chromatin accessibility data. LINGER incorporates atlas-scale external bulk data across diverse cellular contexts and prior knowledge of transcription factor motifs as a manifold regularization. LINGER achieves a fourfold to sevenfold relative increase in accuracy over existing methods and reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Following the GRN inference from reference single-cell multiome data, LINGER enables the estimation of transcription factor activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies.

现有的基因调控网络 (GRN) 推断方法仅依赖于基因表达数据或较低分辨率的批量数据。尽管最近整合了染色质可及性和 RNA 测序数据，但从有限的独立数据点学习复杂的机制仍然是一项艰巨的挑战。在这里，我们提出了 LINGER（用于基因调控的终身神经网络），这是一种机器学习方法，可根据单细胞配对基因表达和染色质可及性数据推断 GRN。 LINGER 将跨不同细胞环境的图谱规模外部批量数据和转录因子基序的先验知识合并为流形正则化。与现有方法相比，LINGER 的准确性相对提高了四到七倍，并揭示了全基因组关联研究的复杂监管格局，从而增强了对疾病相关变异和基因的解释。根据参考单细胞多组数据的 GRN 推断，LINGER 能够仅根据大量或单细胞基因表达数据估计转录因子活性，利用大量可用的基因表达数据从病例对照研究中识别驱动调节因子。