Gene expression analysis is a fundamental technique in bioinformatics, enabling researchers to study how genes are activated or suppressed under different conditions. R, a widely used statistical programming language, provides powerful tools for analyzing gene expression data from RNA sequencing (RNA-Seq) and microarrays. With specialized packages like DESeq2, edgeR, and Limma, researchers can perform essential steps such as data preprocessing, normalization, differential expression analysis, and functional enrichment.

This workshop introduces key concepts, guiding participants through hands-on exercises to identify differentially expressed genes, create heatmaps, volcano plots, and interpret biological insights. Understanding gene expression patterns is crucial for fields like cancer research, drug discovery, and precision medicine. By leveraging R’s extensive bioinformatics libraries, researchers can process large-scale transcriptomic datasets with reproducible workflows.

Whether you’re a beginner or looking to refine your skills, this session provides a strong foundation for advanced gene expression studies in bioinformatics and computational biology.