A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction
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Authors: name: Yin Fang, name: Xinle Deng, name: Kangwei Liu, name: Ningyu Zhang, name: Jingyang Qian, name: Penghui Yang, name: Xiaohui Fan, name: Huajun Chen

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.