scGPT: End-to-End Protocol for Fine-tuned Retina Cell Type Annotation

Abstract Single-cell research faces challenges in accurately annotating cell types at high resolution, especially when dealing with large-scale datasets and rare cell populations. To address this, foundation models like scGPT offer flexible, scalable solutions by leveraging transformer-based architectures. This protocol provides a comprehensive guide to fine-tuning scGPT for cell-type classification in single-cell RNA sequencing (scRNA-seq) data. We demonstrate how to fine-tune scGPT on a custom retina dataset, highlighting the model’s efficiency in handling complex data and improving annotation accuracy achieving 99.5% F1-score. This protocol automates key steps, including data preprocessing, model fine-tuning, and evaluation. This protocol enables researchers to efficiently deploy scGPT for their own datasets. The provided tools, including a command-line interface and Jupyter Notebook, simplify the customization and exploration of the model, offering an accessible workflow for users with basic Python and Linux knowledge. This protocol equips researchers with intermediate bioinformatics skills to enhance the precision and speed of cell-type annotations using scGPT. The source code and example datasets are publicly available on Github and Zenodo.