Schizophrenia, type 2 diabetes, and cardiovascular disease are complex conditions that often co-occur, yet the molecular mechanisms linking them remain not well-understood. This project aimed to explore these comorbidities using single-cell RNA sequencing data and bioinformatics tools to uncover shared and disease-specific gene expression signatures. Publicly available scRNA-seq datasets from Gene Expression Omnibus were collected, pre-processed, and integrated using the Seurat R package. A custom implementation of multi-resolution non-negative matrix factorization was applied to extract transcriptional programs across eight biologically relevant cell types.
Due to limitations and study-design issues, the results of this project were not considered reliable for the biological interpretation related to the goal of this project. Despite this, the project successfully developed a modular reusable pipeline for scRNA-seq analysis, laying the basis for future project continuation aimed at understanding disease comorbidities at the single-cell level.