Integrating Multi-omics Data for Alzheimer's Disease to Explore Its Biomarkers Via the Hypergraph-Regularized Joint...

Area of Science:

• Neuroscience
• Computational Biology
• Genetics

Background:

• Alzheimer's disease (AD) is a progressive neurodegenerative disorder with complex etiology involving genetic, environmental, and lifestyle factors.
• Integrating multi-omics data (genomics, transcriptomics, imaging) offers a powerful approach to explore molecular interactions in AD.
• Existing methods may not fully capture high-order correlations within diverse AD-related datasets.

Purpose of the Study:

• To develop and validate a novel algorithm for integrating multi-omics data in Alzheimer's disease research.
• To identify robust multi-omics biomarkers for AD diagnosis and therapeutic target discovery.
• To explore cellular differences and communication in AD using single-cell RNA sequencing data.

Main Methods:

• Proposed a hypergraph-regularized joint deep semi-non-negative matrix factorization (HR-JDSNMF) algorithm.
• Integrated positron emission tomography (PET), single-nucleotide polymorphism (SNP), and gene expression data for AD.
• Utilized hypergraph mining to uncover high-order correlations and single-cell RNA sequencing (scRNA-seq) for cell-type analysis.

Main Results:

• The HR-JDSNMF algorithm outperformed existing matrix factorization methods in integrating multi-omics data.
• Successfully identified multi-omics biomarkers associated with Alzheimer's disease.
• Categorized cell clusters into high and low-risk groups and analyzed their differentiation and communication.

Conclusions:

• The developed algorithm effectively integrates diverse omics data for AD research.
• Identified multi-omics biomarkers hold potential for clinical diagnosis and drug target discovery in AD.
• Further exploration of cell-type specific differences and communication provides insights into AD pathogenesis.