My practical experience in single-cell transcriptomics is rooted in disease-focused research, particularly in the context of Parkinson’s disease. I have worked across the complete analytical pipeline – from raw FASTQ files through to biological interpretation. This includes quality control, normalization, dimensionality reduction, clustering, cell-type annotation, differential gene expression, and pathway enrichment. For preprocessing and quality control, I have primarily used Seurat, implementing both standard and custom workflows. Cell annotation was carried out using SingleR and scType, enabling accurate and automated cell-type identification. For downstream pseudobulk differential expression analysis, I employed limma and edgeR, integrating metadata-driven comparisons. Enrichment analyses were conducted using VISION and gficf, and I also performed eigengene-based assessments of cellular senescence signatures using the Pigengene R package. This toolkit has allowed me to explore cell-type-specific transcriptional changes and uncover disease-relevant pathways.