标题： 具有指令遵循能力的单细胞分析多模态人工智能副驾驶 显示英文标题

标题： A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction Following

摘要： 大型语言模型在解释复杂的自然语言指令方面表现出色，使其能够执行各种任务。 在生命科学领域，单细胞RNA测序（scRNA-seq）数据作为“细胞生物学的语言”，在单细胞水平上捕捉复杂的基因表达模式。 然而，通过传统工具与这种“语言”进行交互通常效率低下且不直观，给研究人员带来了挑战。 为解决这些限制，我们提出了InstructCell，这是一种多模态人工智能助手，利用自然语言作为更直接和灵活的单细胞分析媒介。 我们构建了一个全面的多模态指令数据集，将基于文本的指令与来自不同组织和物种的scRNA-seq图谱配对。 在此基础上，我们开发了一种多模态细胞语言架构，能够同时解释和处理两种模态。 InstructCell使研究人员能够使用简单的自然语言命令完成关键任务，如细胞类型注释、条件伪细胞生成和药物敏感性预测。 广泛的评估表明，InstructCell始终达到或超过现有单细胞基础模型的性能，同时适应多种实验条件。 更重要的是，InstructCell提供了一个易于使用且直观的工具，用于探索复杂的单细胞数据，降低技术门槛并促进更深入的生物学洞察。 显示英文摘要

摘要： 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.