A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction
  Following
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.
67871cb6ce7f3eb12692b222
Yin Fang
claimed_verified
2025-01-15T08:48:58.033Z
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Yin Fang
user
Fangyinfff
67871cb6ce7f3eb12692b223
Xinle Deng
admin_assigned
2025-01-15T16:47:14.066Z
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Xinle Deng
user
Linear-Matrix-Probability
67871cb6ce7f3eb12692b224
Kangwei Liu
admin_assigned
2025-01-15T16:47:20.167Z
63e5fbbb2d2c508de9f77550
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kangwei liu
user
sadgaj
67871cb6ce7f3eb12692b225
Ningyu Zhang
claimed_verified
2025-01-15T08:48:54.355Z
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Ningyu Zhang
user
Ningyu
67871cb6ce7f3eb12692b226
Jingyang Qian
67871cb6ce7f3eb12692b227
Penghui Yang
admin_assigned
2025-01-15T16:47:33.365Z
6508463c423b46492eec64e2
https://cdn-avatars.huggingface.co/v1/production/uploads/6508463c423b46492eec64e2/WSU7NSqjk92Pr2xUIWjCk.png
Penghui Yang
user
phyang
67871cb6ce7f3eb12692b228
Xiaohui Fan
67871cb6ce7f3eb12692b229
Huajun Chen
admin_assigned
2025-01-15T16:47:53.920Z
64931296137833d7ec7689cd
https://cdn-avatars.huggingface.co/v1/production/uploads/64931296137833d7ec7689cd/TBihNdp1ZwIWjhfAWjRr6.jpeg
Huajun Chen
user
huajunsir
A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction
  Following
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.
InstructCell, a multi-modal AI copilot, enables researchers to perform single-cell analysis using natural language instructions, improving accessibility and performance compared to existing tools.
multi-modal AI copilot single-cell RNA sequencing scRNA-seq cell type annotation conditional pseudo-cell generation drug sensitivity prediction multi-modal instruction dataset multi-modal cell language architecture