美国克莱姆森大学Zhana Duren团队利用图谱级别的外部数据从单细胞多组数据中推断基因调控网络。相关论文于2024年4月12日在线发表在《自然—生物技术》杂志上。

研究人员介绍了一种机器学习方法——LINGER（用于基因调控的终身神经网络），该方法可从单细胞配对基因表达和染色质可及性数据中推断基因调控网络。LINGER结合了不同细胞背景下的图谱级别的外部大容量数据和转录因子模体的先验知识，作为一种流形正则化。

与现有方法相比，LINGER的准确性相对提高了四倍到七倍，并揭示了全基因组关联研究的复杂调控格局，从而加强了对疾病相关变异和基因的解释。在根据参考单细胞多组数据进行基因调控网络（GRN）推断之后，LINGER能够仅根据批量或单细胞基因表达数据估算转录因子的活性，从而利用大量可用的基因表达数据从病例对照研究中找出驱动调控因子。

研究人员表示，现有的GRN推断方法仅依赖于基因表达数据或分辨率较低的批量数据。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。

附：英文原文

Title: Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data

Author: Yuan, Qiuyue, Duren, Zhana

Issue&Volume: 2024-04-12

Abstract: 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.

DOI: 10.1038/s41587-024-02182-7

Source: https://www.nature.com/articles/s41587-024-02182-7