Evaluation of cell type annotation reliability using a large language model-based identifier.

Abstract

Ensuring accurate cell type annotation in single-cell RNA sequencing data is a significant challenge, as both expert and automated methods can be biased or constrained by their training data, leading to errors and time-consuming revisions. To address this, we developed LICT (Large Language Model-based Identifier for Cell Types), a tool that leverages multi-model integration and a "talk-to-machine" approach. Validated across diverse datasets, LICT consistently aligns with expert annotations. With its objective framework for assessing annotation reliability, LICT can interpret cases where a single cell population exhibits multifaceted traits, allowing researchers to focus on the underlying biological insights. Comparisons with existing tools highlight LICT's superiority in efficiency, consistency, accuracy, and reliability, establishing it as a powerful tool for single-cell RNA sequencing analysis. Furthermore, its independence from reference data emphasizes LICT's generalizability, enhancing reproducibility and ensuring more reliable results in cellular research.