A Comparative Study of Various Clustering Methods on Dengue Microarray Gene Expression Dataset and Pathway Enrichment Analysis

Recently, disease progression caused by dengue virus has intensely increased and affected almost 80 countries of WHO. As a result, in the year of 2023, 6.5 million cases of dengue infection followed by 7300 dengue-related deaths have been reported. The global spread of dengue is driven by the lack of vaccines and insufficient vector control efforts. Human and dengue virus interaction affects gene expression value which needs to be investigated for biomarker prediction. Using clustering methods, it is possible to identify genes contributing to a deeper understanding to know how the virus manipulates host cell machinery and gain insight of the same biological pathways or cellular processes. Clustering results may help in identification of biomarkers, uncovering potential therapeutic targets. This study aims to analyze dengue gene expression dataset GSE51808 through various unsupervised clustering algorithms leveraging the identification of new biomarkers through enrichment analysis.