A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction Following
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arXiv
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| Format: | Preprint |
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2025
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| author | Fang, Yin Deng, Xinle Liu, Kangwei Zhang, Ningyu Qian, Jingyang Yang, Penghui Fan, Xiaohui Chen, Huajun |
| author_facet | Fang, Yin Deng, Xinle Liu, Kangwei Zhang, Ningyu Qian, Jingyang Yang, Penghui Fan, Xiaohui Chen, Huajun |
| contents | Large language models excel at interpreting complex natural language instructions, enabling them to perform a wide range of tasks. In the life sciences, single-cell RNA sequencing (scRNA-seq) data serves as the "language of cellular biology", capturing intricate gene expression patterns at the single-cell level. However, interacting with this "language" through conventional tools is often inefficient and unintuitive, posing challenges for researchers. To address these limitations, we present InstructCell, a multi-modal AI copilot that leverages natural language as a medium for more direct and flexible single-cell analysis. We construct a comprehensive multi-modal instruction dataset that pairs text-based instructions with scRNA-seq profiles from diverse tissues and species. Building on this, we develop a multi-modal cell language architecture capable of simultaneously interpreting and processing both modalities. InstructCell empowers researchers to accomplish critical tasks-such as cell type annotation, conditional pseudo-cell generation, and drug sensitivity prediction-using straightforward natural language commands. Extensive evaluations demonstrate that InstructCell consistently meets or exceeds the performance of existing single-cell foundation models, while adapting to diverse experimental conditions. More importantly, InstructCell provides an accessible and intuitive tool for exploring complex single-cell data, lowering technical barriers and enabling deeper biological insights. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08187 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction Following Fang, Yin Deng, Xinle Liu, Kangwei Zhang, Ningyu Qian, Jingyang Yang, Penghui Fan, Xiaohui Chen, Huajun Computation and Language Artificial Intelligence Computational Engineering, Finance, and Science Human-Computer Interaction Machine Learning Cell Behavior Large language models excel at interpreting complex natural language instructions, enabling them to perform a wide range of tasks. In the life sciences, single-cell RNA sequencing (scRNA-seq) data serves as the "language of cellular biology", capturing intricate gene expression patterns at the single-cell level. However, interacting with this "language" through conventional tools is often inefficient and unintuitive, posing challenges for researchers. To address these limitations, we present InstructCell, a multi-modal AI copilot that leverages natural language as a medium for more direct and flexible single-cell analysis. We construct a comprehensive multi-modal instruction dataset that pairs text-based instructions with scRNA-seq profiles from diverse tissues and species. Building on this, we develop a multi-modal cell language architecture capable of simultaneously interpreting and processing both modalities. InstructCell empowers researchers to accomplish critical tasks-such as cell type annotation, conditional pseudo-cell generation, and drug sensitivity prediction-using straightforward natural language commands. Extensive evaluations demonstrate that InstructCell consistently meets or exceeds the performance of existing single-cell foundation models, while adapting to diverse experimental conditions. More importantly, InstructCell provides an accessible and intuitive tool for exploring complex single-cell data, lowering technical barriers and enabling deeper biological insights. |
| title | A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction Following |
| topic | Computation and Language Artificial Intelligence Computational Engineering, Finance, and Science Human-Computer Interaction Machine Learning Cell Behavior |
| url | https://arxiv.org/abs/2501.08187 |