CellTypeAgent: Trustworthy cell type annotation with Large Language Models
This work addresses the need for more efficient and reliable cell type annotation in bioinformatics, though it appears incremental as it builds on existing LLM and database methods.
The authors tackled the laborious problem of cell type annotation in single-cell RNA sequencing by developing CellTypeAgent, an LLM-based agent that integrates verification from databases, achieving higher accuracy than existing methods while mitigating hallucinations across nine real datasets with 303 cell types from 36 tissues.
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.