Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disorder. Its etiology may be associated with genetic, environmental, and lifestyle factors. With the advancement of technology, the integration of genomics, transcriptomics, and imaging data related to AD allows simultaneous exploration of molecular information at different levels and their interaction within the organism. This paper proposes a hypergraph-regularized joint deep semi-non-negative matrix factorization (HR-JDSNMF) algorithm to integrate positron emission tomography (PET), single-nucleotide polymorphism (SNP), and gene expression data for AD. The method employs matrix factorization techniques to nonlinearly decompose the original data at multiple layers, extracting deep features from different omics data, and utilizes hypergraph mining to uncover high-order correlations among the three types of data. Experimental results demonstrate that this approach outperforms several matrix factorization-based algorithms and effectively identifies multi-omics biomarkers for AD. Additionally, single-cell RNA sequencing (scRNA-seq) data for AD were collected, and genes within significant modules were used to categorize different types of cell clusters into high and low-risk cell groups. Finally, the study extensively explores the differences in differentiation and communication between these two cell types. The multi-omics biomarkers unearthed in this study can serve as valuable references for the clinical diagnosis and drug target discovery for AD. The realization of the algorithm in this paper code is available at https://github.com/ShubingKong/HR-JDSNMF.

阿尔茨海默病（AD）是一种进行性且不可逆的神经退行性疾病。其病因可能与遗传、环境和生活方式因素有关。随着技术的进步，与AD相关的基因组学、转录组学和成像数据的整合使得能够同时探索不同水平的分子信息及其在生物体内的相互作用。本文提出了一种超图正则化联合深度半非负矩阵分解 (HR-JDSNMF) 算法，用于集成 AD 的正电子发射断层扫描 (PET)、单核苷酸多态性 (SNP) 和基因表达数据。该方法采用矩阵分解技术对原始数据进行多层非线性分解，从不同的组学数据中提取深层特征，并利用超图挖掘来揭示三类数据之间的高阶相关性。实验结果表明，该方法优于多种基于矩阵分解的算法，并有效识别 AD 的多组学生物标志物。此外，还收集了 AD 的单细胞 RNA 测序 (scRNA-seq) 数据，并使用重要模块内的基因将不同类型的细胞簇分类为高风险细胞组和低风险细胞组。最后，该研究广泛探讨了这两种细胞类型之间分化和通讯的差异。本研究发现的多组学生物标志物可为AD的临床诊断和药物靶点发现提供有价值的参考。本文算法的实现代码可参见https://github.com/ShubingKong/HR-JDSNMF。