scKAN:通过Kolmogorov-Arnold网络进行细胞类型特异性基因发现和药物再利用的可解释单细胞分析

IF 10.1 1区 生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Haohuai He, Zhenchao Tang, Guanxing Chen, Fan Xu, Yao Hu, Yinglan Feng, Jibin Wu, Yu-An Huang, Zhi-An Huang, Kay Chen Tan
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引用次数: 0

摘要

单细胞RNA测序(scRNA-seq)数据分析彻底改变了我们对细胞异质性的理解,但目前的方法在效率、可解释性和将分子见解与治疗应用联系起来方面面临挑战。尽管深度学习方法取得了进步,但识别细胞类型特异性功能基因集仍然很困难。在这项研究中,我们提出了scKAN,一个用于scRNA-seq分析的可解释框架,其主要目标有两个:准确的细胞类型注释和发现细胞类型特异性标记基因和基因集。关键的创新是使用Kolmogorov-Arnold网络的可学习激活曲线来模拟基因与细胞的关系。与典型的注意力机制的聚合加权方案相比,这种方法提供了一种更直接的方式来可视化和解释这些特定的相互作用。该体系结构在细胞类型注释方面实现了卓越的性能,与最先进的方法相比,宏F1分数提高了6.63%。此外,它能够系统地识别功能一致的细胞类型特异性基因集。我们通过胰腺导管腺癌的案例研究证明了该框架的翻译潜力,其中scKAN识别的基因特征导致了潜在的药物重新利用候选药物,其结合稳定性得到了分子动力学模拟的支持。我们的工作建立了scKAN作为一个高效和可解释的框架,有效地将单细胞分析与药物发现联系起来。通过将轻量级架构与揭示细微生物模式的能力相结合,我们的方法为将大规模单细胞数据转化为可操作的治疗策略提供了一种可解释的方法。这种方法为在复杂疾病中加速识别细胞类型特异性靶点提供了坚实的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
scKAN: interpretable single-cell analysis for cell-type-specific gene discovery and drug repurposing via Kolmogorov-Arnold networks
Analysis of single-cell RNA sequencing (scRNA-seq) data has revolutionized our understanding of cellular heterogeneity, yet current approaches face challenges in efficiency, interpretability, and connecting molecular insights to therapeutic applications. Despite advances in deep learning methods, identifying cell-type-specific functional gene sets remains difficult. In this study, we present scKAN, an interpretable framework for scRNA-seq analysis with two primary goals: accurate cell-type annotation and the discovery of cell-type-specific marker genes and gene sets. The key innovation is using the learnable activation curves of the Kolmogorov-Arnold network to model gene-to-cell relationships. This approach provides a more direct way to visualize and interpret these specific interactions compared to the aggregated weighting schemes typical of attention mechanisms. This architecture achieves superior performance in cell-type annotation, with a 6.63% improvement in macro F1 score over state-of-the-art methods. Additionally, it enables the systematic identification of functionally coherent cell-type-specific gene sets. We demonstrate the framework’s translational potential through a case study on pancreatic ductal adenocarcinoma, where gene signatures identified by scKAN led to a potential drug repurposing candidate, whose binding stability was supported by molecular dynamics simulations. Our work establishes scKAN as an efficient and interpretable framework that effectively bridges single-cell analysis with drug discovery. By combining lightweight architecture with the ability to uncover nuanced biological patterns, our approach offers an interpretable method for translating large-scale single-cell data into actionable therapeutic strategies. This approach provides a robust foundation for accelerating the identification of cell-type-specific targets in complex diseases.
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来源期刊
Genome Biology
Genome Biology Biochemistry, Genetics and Molecular Biology-Genetics
CiteScore
21.00
自引率
3.30%
发文量
241
审稿时长
2 months
期刊介绍: Genome Biology stands as a premier platform for exceptional research across all domains of biology and biomedicine, explored through a genomic and post-genomic lens. With an impressive impact factor of 12.3 (2022),* the journal secures its position as the 3rd-ranked research journal in the Genetics and Heredity category and the 2nd-ranked research journal in the Biotechnology and Applied Microbiology category by Thomson Reuters. Notably, Genome Biology holds the distinction of being the highest-ranked open-access journal in this category. Our dedicated team of highly trained in-house Editors collaborates closely with our esteemed Editorial Board of international experts, ensuring the journal remains on the forefront of scientific advances and community standards. Regular engagement with researchers at conferences and institute visits underscores our commitment to staying abreast of the latest developments in the field.
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