MCC-SP

"MCC-SP" (Maximal Correlation Coefficient-Shortest Path) is a computational method designed to enhance the analysis of pathways from genetic variants to diseases within the context of Genome-Wide Association Studies (GWAS). By addressing the limitations of traditional gene expression analysis methods, which often overlook the network structure and the propagation effect of genetic variants across the network to the disease, MCC-SP introduces a sophisticated approach for identifying and ranking potential pathways that could mediate the effect of genetic variants on complex diseases.

Key Features and Functionalities:

- Maximal Correlation Coefficient (MCC): MCC-SP utilizes the maximal correlation coefficient to represent the connection strength between nodes in a gene regulatory network. This measurement is more effective than Pearson correlation, Spearman correlation, and other metrics in capturing the between-node connection strength.

- Integration with K Shortest Paths Algorithm: By integrating MCC with the K shortest paths algorithm, MCC-SP can rank and identify potential pathways from a specific genetic variant to a disease outcome, considering the entire network structure.

- Pathway Importance Score (PIS): MCC-SP provides a Pathway Importance Score to quantify the importance of each identified pathway. This score is crucial for prioritizing pathways for further investigation or potential drug development.

- Application to Real Data: Applied to a real dataset from the Religious Orders Study and the Memory and Aging Project, MCC-SP successfully detected pathways from the APOE genotype to Alzheimer's disease through gene expression enriched in the Alzheimer's disease pathway, demonstrating its practical utility.