GRLGRN:基于图形表示的学习,从单细胞RNA-seq数据推断基因调节网络
Kai Wang1, Yulong Li1, Fei Liu1
1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, Jiangsu, China.
BMC bioinformatics
|April 18, 2025
概括
我们开发了GRLGRN,这是一个深度学习模型,用于从单细胞RNA测序数据中推断基因调节网络 (GRN). GRLGRN显著提高了基因相互作用的预测准确性,有助于生物发现.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 基因调节网络 (GRNs) 模型基因相互作用对于细胞功能至关重要.
- 单细胞RNA测序 (scRNA-seq) 提供了洞察力,但也带来了GRN重建的噪音和脱落等挑战.
- 现有的机器学习和深度学习方法与scRNA-seq数据复杂性作斗争.
研究的目的:
- 开发一种新的深度学习模型,GRLGRN,用于准确的GRN推理.
- 利用以前的GRN知识和scRNA-seq数据来改善基因调节关系的预测.
- 为应对由细胞异质性和scRNA-seq.q.中的数据缺陷所带来的挑战.
主要方法:
- GRLGRN使用图形变压器网络来捕获从以前的GRN中隐含的监管链接.
- 基因特征是使用隐性链接和基因表达特征的相邻矩阵编码的.
- 注意力机制增强特征提取,完善基因嵌入用于关系推断.
主要成果:
- 在预测7个细胞系数据集中的基因相互作用方面,GRLGRN的表现优于常见的模型.
- 在大多数数据集上,在AUROC (接收器操作特征曲线下的区域) 和AUPRC (精度召回曲线下的区域) 中实现了卓越的性能.
- 显示了显著的平均改善:AUROC的7.3%,AUPRC的30.7%.
结论:
- 在从scRNA-seq数据预测基因相互作用方面,GRLGRN表现出强的表现.
- 该模型提供了可解释性,可以识别关键的调节基因 (枢纽基因) 并发现隐藏的调节链接.
- 结果突出了GRLGRN通过准确的GRN重建来推动生物研究的潜力.
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