scGPT: end-to-end protocol for fine-tuned retinal 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 such as single-cell generative pretrained transformer (scGPT) offer flexible, scalable solutions by leveraging transformer-based architectures. Here we provide a comprehensive guide to fine-tuning scGPT for cell-type classification in single-cell RNA sequencing 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 script and Jupyter Notebook, simplify the customization and exploration of the model, proposing an accessible workflow for users with minimal Python and Linux knowledge. The protocol offers an off-the-shell solution of high-precision cell-type annotation using scGPT for researchers with intermediate bioinformatics. The source code and example datasets are publicly available on GitHub and Zenodo.