Högskolan i Skövde

Inflammatory Bowel Disease (IBD) is a complicated and multifaceted illness marked by chronic inflammation of the gastrointestinal system. Understanding the underlying molecular causes of IBD is critical for creating tailored therapeutics and better patient outcomes. This thesis compares Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) methods on two datasets, GSE66407 and GSE186582. Three ORA tools—ClusterProfiler, WebGestalt, and Enrichr—were tested to determine their sensitivity, specificity, and robustness in discovering enriched pathways. The results showed that the techniques were quite consistent, especially in the GSE186582 dataset, where there was perfect overlap in pathway discovery. However, small differences were seen in the GSE66407 dataset, with ClusterProfiler and Enrichr detecting distinct pathways. Furthermore, GSEA was conducted using clusterProfiler, WebGestalt, and fgsea, which provided a dynamic analysis of the whole ranked list of genes. GSEA results from both datasets consistently revealed immune-related pathways involved in IBD pathogenesis, including IL-17 signaling, NF-kappaB signaling, and TNF signaling. Particularly, ClusterProfiler, WebGestalt, and fgsea both identified many distinct pathways, demonstrating differences in pathway recognition depending on the approach utilized. While ORA approaches are strong and accurate for quickly identifying pathways, GSEA may give a more detailed knowledge of the biological processes by capturing minor gene expression variations.