# scAgent: Universal Single-Cell Annotation via a LLM Agent

arXiv: https://arxiv.org/abs/2504.04698 · HTML: https://mlsanity.com/paper/2504.04698/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 10 of 10)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [DOC: Deep Open Classification of Text Documents](https://mlsanity.com/paper/1709.08716/) (2017)
- [A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2101.12631/) (2021)
- [A Survey on LoRA of Large Language Models](https://mlsanity.com/paper/2407.11046/) (2024)
- [scHyena: Foundation Model for Full-Length Single-Cell RNA-Seq Analysis in Brain](https://mlsanity.com/paper/2310.02713/) (2023)
- [scInterpreter: Training Large Language Models to Interpret scRNA-seq Data for Cell Type Annotation](https://mlsanity.com/paper/2402.12405/) (2024)

## Built on by (most cited first, 5 of 5)

- [Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics](https://mlsanity.com/paper/2510.09901/) (2025)
- [LLM4Cell: A Survey of Large Language and Agentic Models for Single-Cell Biology](https://mlsanity.com/paper/2510.07793/) (2025)
- [MAT-Cell: A Multi-Agent Tree-Structured Reasoning Framework for Batch-Level Single-Cell Annotation](https://mlsanity.com/paper/2604.06269/) (2026)
- [AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositories](https://mlsanity.com/paper/2604.19606/) (2026)
- [SCTA: An Agentic Framework for Stable and Interpretable Target Gene Discovery from Single-Cell RNA Sequencing](https://mlsanity.com/paper/2607.23821/) (2026)

## Abstract (arXiv)

Cell type annotation is critical for understanding cellular heterogeneity. Based on single-cell RNA-seq data and deep learning models, good progress has been made in annotating a fixed number of cell types within a specific tissue. However, universal cell annotation, which can generalize across tissues, discover novel cell types, and extend to novel cell types, remains less explored. To fill this gap, this paper proposes scAgent, a universal cell annotation framework based on Large Language Models (LLMs). scAgent can identify cell types and discover novel cell types in diverse tissues; furthermore, it is data efficient to learn novel cell types. Experimental studies in 160 cell types and 35 tissues demonstrate the superior performance of scAgent in general cell-type annotation, novel cell discovery, and extensibility to novel cell type.