scChat: A large language model‐powered co‐pilot for contextualized single‐cell RNA sequencing analysis

Abstract
Single‐cell RNA sequencing (scRNA‐seq) has transformed biomedical research by enabling transcriptomic analysis at single‐cell resolution. Yet, existing computational approaches remain primarily data‐driven and lack the ability to integrate research context, limiting their interpretability and impact on hypothesis generation or experimental planning. We present scChat, a large language model‐powered co‐pilot for contextualized scRNA‐seq analysis. Unlike conventional pipelines restricted to tasks such as cell type annotation or enrichment analysis, scChat has an interactive, reasoning‐based framework. It combines quantitative algorithms with retrieval‐augmented generation and a multi‐agent architecture to support hypothesis validation, mechanistic interpretation, and next‐step experimental design. Through showcase and benchmarking studies, we demonstrate that scChat not only achieves high accuracy in cell type annotation but also provides biologically grounded explanations and contextual insights.

Keywords: single-cell RNA-seq (scRNA-seq), Large language models (LLMs), Machine learning, Prompt andcontext engineering

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