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Identifying Network Biomarkers for Alzheimer's Disease Using Single-Cell RNA Sequencing Data
Ioannis Aslanis1, Marios G Krokidis1, Georgios N Dimitrakopoulos1
1Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.
Advances in Experimental Medicine and Biology
|July 31, 2023
Summary
This study introduces an R workflow to identify disease-perturbed subpathways using network biomarkers and single-cell RNA sequencing data for Alzheimer's disease (AD) research.
Area of Science:
- Biomedical Informatics
- Systems Biology
- Genomics
Background:
- Biological networks offer efficient analysis of complex diseases.
- Network biomarkers, perturbed molecular groups, indicate disease processes.
- Alzheimer's disease (AD) research benefits from advanced computational approaches.
Purpose of the Study:
- To develop an R workflow for extracting disease-perturbed subpathways.
- To integrate gene-gene interaction networks with single-cell RNA sequencing (scRNA-seq) data.
- To identify novel network biomarkers for complex diseases like AD.
Main Methods:
- Constructed a gene-gene interaction network.
- Integrated network with scRNA-seq expression profiles.
- Applied network processing and pruning to isolate active subnetworks.
Main Results:
- Successfully applied the methodology to AD scRNA-seq data.
- Identified existing and novel potential AD biomarkers.
- Provided biomarkers within a gene network context.
Conclusions:
- The proposed workflow effectively extracts disease-perturbed subpathways.
- This approach enhances the discovery of network biomarkers for AD.
- Integrative analysis of network and scRNA-seq data is powerful for complex disease research.
