Handling the Cellular Complex Systems in Alzheimer's Disease Through a Graph Mining Approach.

Area of Science:

• Biomedical Sciences
• Systems Biology
• Computational Biology

Background:

• Medical sciences increasingly rely on advanced biomedical technology and research for understanding complex diseases.
• Systems biology and pathway analysis are crucial for interpreting high-throughput omics data.
• Omics technologies generate vast amounts of biological data, necessitating sophisticated analytical tools.

Purpose of the Study:

• To develop a subpathway analysis method for single-cell RNA sequencing (scRNA-seq) experiments.
• To identify differentially expressed subpathways indicative of perturbed biological processes in diseases.
• To apply and validate the method for tracking microglia activation in neurodegeneration.

Main Methods:

• Developed a novel subpathway analysis approach for scRNA-seq data.
• Integrated multiple RNA-seq differential expression analysis tools to determine gene differential expression status.
• Utilized graph mining techniques to analyze pathway complexity.
• Applied the method to a scRNA-seq dataset for temporal tracking of microglia activation.

Main Results:

• The developed method successfully isolates differentially expressed subpathways.
• Consensus-based differential expression analysis minimizes false discoveries.
• The approach effectively identified perturbed biological processes associated with neurodegeneration in microglia activation data.
• Demonstrated efficacy on a real-world scRNA-seq dataset.

Conclusions:

• The subpathway analysis method provides a robust approach for dissecting complex biological systems from scRNA-seq data.
• This method aids in understanding disease pathogenesis by pinpointing specific perturbed pathways.
• The findings highlight the potential of systems biology tools in neurodegeneration research and disease diagnostics.