ChatCell：利用自然语言促进单细胞分析

ChatCell: Facilitating Single-Cell Analysis with Natural Language

February 13, 2024

作者: Yin Fang, Kangwei Liu, Ningyu Zhang, Xinle Deng, Penghui Yang, Zhuo Chen, Xiangru Tang, Mark Gerstein, Xiaohui Fan, Huajun Chen

cs.AI

摘要

随着大型语言模型（LLMs）的快速发展，它们在科学领域中的影响日益突出。LLMs在任务泛化和自由对话方面的新兴能力可以显著推动化学和生物学等领域的发展。然而，作为构成生物体基础组成的单细胞生物学领域仍面临一些挑战。当前方法中存在的高知识门槛和有限的可扩展性限制了LLMs在掌握单细胞数据方面的充分利用，阻碍了直接获取和快速迭代。为此，我们引入了ChatCell，通过自然语言促进单细胞分析，标志着一种范式转变。利用词汇适应和统一序列生成，ChatCell在单细胞生物学领域获得了深厚的专业知识，并具备容纳各种分析任务的能力。广泛的实验进一步证明了ChatCell的稳健性能以及加深单细胞洞见的潜力，为这一关键领域的更易访问和直观探索铺平道路。我们的项目主页位于https://zjunlp.github.io/project/ChatCell。

English

As Large Language Models (LLMs) rapidly evolve, their influence in science is becoming increasingly prominent. The emerging capabilities of LLMs in task generalization and free-form dialogue can significantly advance fields like chemistry and biology. However, the field of single-cell biology, which forms the foundational building blocks of living organisms, still faces several challenges. High knowledge barriers and limited scalability in current methods restrict the full exploitation of LLMs in mastering single-cell data, impeding direct accessibility and rapid iteration. To this end, we introduce ChatCell, which signifies a paradigm shift by facilitating single-cell analysis with natural language. Leveraging vocabulary adaptation and unified sequence generation, ChatCell has acquired profound expertise in single-cell biology and the capability to accommodate a diverse range of analysis tasks. Extensive experiments further demonstrate ChatCell's robust performance and potential to deepen single-cell insights, paving the way for more accessible and intuitive exploration in this pivotal field. Our project homepage is available at https://zjunlp.github.io/project/ChatCell.