title: A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction
    Following
summary: 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.
paper_authors._id: 67871cb6ce7f3eb12692b222
paper_authors.name: Yin Fang
paper_authors.status: claimed_verified
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paper_authors.fullname: Yin Fang
paper_authors.type: user
paper_authors.user: Fangyinfff
paper_authors._id: 67871cb6ce7f3eb12692b223
paper_authors.name: Xinle Deng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-15T16:47:14.066Z
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paper_authors.avatarUrl: /avatars/8232a7c5db590ed26751a47c45d481b8.svg
paper_authors.fullname: Xinle Deng
paper_authors.type: user
paper_authors.user: Linear-Matrix-Probability
paper_authors._id: 67871cb6ce7f3eb12692b224
paper_authors.name: Kangwei Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-15T16:47:20.167Z
paper_authors._id: 63e5fbbb2d2c508de9f77550
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paper_authors.fullname: kangwei liu
paper_authors.type: user
paper_authors.user: sadgaj
paper_authors._id: 67871cb6ce7f3eb12692b225
paper_authors.name: Ningyu Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-15T08:48:54.355Z
paper_authors._id: 620b3bbb0668e435407c8d0a
paper_authors.avatarUrl: /avatars/e0fccbb2577d76088e09f054c35cffbc.svg
paper_authors.fullname: Ningyu Zhang
paper_authors.type: user
paper_authors.user: Ningyu
paper_authors._id: 67871cb6ce7f3eb12692b226
paper_authors.name: Jingyang Qian
paper_authors._id: 67871cb6ce7f3eb12692b227
paper_authors.name: Penghui Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-15T16:47:33.365Z
paper_authors._id: 6508463c423b46492eec64e2
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6508463c423b46492eec64e2/WSU7NSqjk92Pr2xUIWjCk.png
paper_authors.fullname: Penghui Yang
paper_authors.type: user
paper_authors.user: phyang
paper_authors._id: 67871cb6ce7f3eb12692b228
paper_authors.name: Xiaohui Fan
paper_authors._id: 67871cb6ce7f3eb12692b229
paper_authors.name: Huajun Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-15T16:47:53.920Z
paper_authors._id: 64931296137833d7ec7689cd
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64931296137833d7ec7689cd/TBihNdp1ZwIWjhfAWjRr6.jpeg
paper_authors.fullname: Huajun Chen
paper_authors.type: user
paper_authors.user: huajunsir
paper_title: A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction
    Following
paper_summary: 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.