Differential gene expression and functional profiling in ER+ recurrent breast cancer

During the STEM-Away Bioinformatics Pathway Internship, I collaborated with international teammates to analyze gene expression data using R. It was my first coding experience working directly with raw biological data, learning to perform quality control, normalization, and batch correction while troubleshooting real coding issues. Through weekly deliverables, I applied techniques such as differential gene expression analysis (limma), functional enrichment (GO, KEGG, STRING, and GSEA), and visualization with PCA, heatmaps, and volcano plots.

Midway through the internship, I began an independent project on estrogen-positive breast cancer recurrence, creating metadata from GEO dataset and conducting the full analysis pipeline from preprocessing to survival analysis.

Full history of bioinformatics pipeline in Stem-Away virtual internship: Self-assessment

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