Differential Gene Expression (DGE) analysis identifies genes with significant expression changes across conditions using RNA-Seq data. This process involves normalization, statistical modeling, and significance testing. Three common R packages—DESeq2, edgeR, and limma-voom—use different approaches to detect differentially expressed genes (DEGs). DESeq2 applies a negative binomial model, edgeR utilizes the TMM method and GLM, and limma-voom transforms count data to log2 scale for linear modeling. Combining DEGs from multiple methods enhances robustness. Further analysis, such as Gene Ontology (GO) and KEGG pathway enrichment, helps interpret biological significance, ensuring reliable transcriptomic insights.