Abstract Motivation Cell annotation is fundamental for single-cell data interpretation. Accurate annotation allows us to identify cell types, understand their functions, trace developmental trajectories, and pinpoint alterations associated with a condition of interest. However, this complex process demands extensive manual curation, domain expertise, and proficiency across diverse bioinformatics tools. These challenges impede reproducibility and consistency. Results We have developed a new approach for semi-automatic cell type annotation, powered by large language models (LLMs). Given the input single-cell data, we first perform dimension reduction, clustering, and differential analysis to identify distinct cell groups and their respective markers. Next, we utilize Meta’s Llama and structured prompting to infer potential cell types. This approach greatly reduces manual labor from researchers while maintaining biological accuracy through enforced ontology, tissue context, and marker gene signatures. Our solution is freely accessible through our web-based platform named CytoAnalyst, hosted on a high-performance infrastructure with optimized networking and storage capabilities. CytoAnalyst also offers capabilities for quality control, embedding analysis, clustering, differential analysis, gene set analysis, cell enrichment, cell type annotation, and pseudo-time trajectory inference. Availability CytoAnalyst is freely available at https://cytoanalyst.tinnguyen-lab.com/. The CytoAnalyst handbook, including step-by-step tutorials and example case studies, is available at https://cytoanalyst.tinnguyen-lab.com/docs/. Supplementary information Supplementary data are available at Bioinformatics Advances online.
Nguyen et al. (Sat,) studied this question.