scChat: A Large Language Model-Powered Co-Pilot for Contextualized Single-Cell RNA Sequencing Analysis
Open accessPreprint — published as 10.1002/aic.70285
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 (LLM)–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.
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Specialist agents such as BrainGPT for 3D CT scans, HistoGPT for histopathology images, and scChat for single-cell sequencing data can be easily integrated into the MoMA workflow.
“For example, specialist agents such as BrainGPT66 for 3D CT scans, HistoGPT67 for histopathology images, and scChat68 for single-cell sequencing data can be easily integrated into the MoMA workflow when these modalities are involved.”
From MoMA: a mixture-of-multimodal-agents architecture for enhancing clinical prediction modelling (2025) · cites this paper as [68]
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Cited by
Sorted by citations
MoMA: a mixture-of-multimodal-agents architecture for enhancing clinical prediction modelling
Gao, J., Rahman, M., Caskey, J., Oguss, M., O’Rourke, A., Brown, R., Stey, A., Mayampurath, A., Churpek, M. M., Chen, G., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02219-4 · Cited by 2 · see citing works
- Authors
- Chiu, H.-H., Varghese, A., Shao, K., Lu, Y.-C., Nahar, R., Chen, H., Deng, Q., Bao, X., & Li, C.
- Affiliations
- Purdue University West Lafayette (US)
- Corresponding author
- Bao, Xiaoping
- Li, Can
- Venue
- AIChE Journal
- Publisher
- openRxiv
- Published
- 2024-10-03
- Volume
- 72
- Issue
- 6
- ISSN
- 0001-1541 (ISSN-L)
- PMC
- PMC13061372
- OpenAlex
- W7128591464
- Cited by
- 6
- References
- 20
- Citations by year
- 2026: 2 · 2025: 4
- FWCI
- 4.13
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- AIChE Journal (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion)
- Funders
- National Science Foundation (2425704, 2424004, 2143064)
- American Cancer Society (RSG-25-1422119-01-IBCD)
- Walther Cancer Foundation
- Purdue University
- National Institutes of Health (1r01ca293514-01a1)
- Davidson School of Chemical Engineering, Purdue University
- Purdue Institute for Integrative Neuroscience, Purdue University
- National Cancer Institute (P30CA023168, R37CA265926)
- National Institute of General Medical Sciences (R35GM119787)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- single-cell RNA sequencing, large language models, retrieval-augmented generation, multi-agent systems, cell type annotation, hypothesis generation, experimental design, interpretability, mechanistic interpretation
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