CellTypeAgent: Trustworthy cell type annotation with Large Language Models

TL;DR

CellTypeAgent leverages large language models combined with database verification to improve the accuracy and reliability of cell type annotation in single-cell RNA sequencing, reducing errors and hallucinations.

Contribution

This paper introduces CellTypeAgent, a novel LLM-based framework that enhances cell type annotation accuracy by integrating LLMs with database verification.

Findings

Achieves higher accuracy than existing methods

Reduces hallucinations in cell type predictions

Validated across nine real datasets with 303 cell types

Abstract

Cell type annotation is a critical yet laborious step in single-cell RNA sequencing analysis. We present a trustworthy large language model (LLM)-agent, CellTypeAgent, which integrates LLMs with verification from relevant databases. CellTypeAgent achieves higher accuracy than existing methods while mitigating hallucinations. We evaluated CellTypeAgent across nine real datasets involving 303 cell types from 36 tissues. This combined approach holds promise for more efficient and reliable cell type annotation.