Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular behavior in diseases such as cancer, neurodegenerative disorders, and inflammatory diseases. This project leverages transformer-based models (scGPT, scVI) to predict cell state transitions during disease progression.
🔍 Key Features:
- ✅ Preprocess and normalize scRNA-seq data.
- ✅ Train transformer-based models (scGPT, scVI) to predict cell states.
- ✅ Compare predictions with traditional methods (Monocle3, PAGA).
- ✅ Deploy an interactive web application for visualization.