ChatCell: Facilitating Single-Cell Analysis with Natural Language
Authors: name: Yin Fang, name: Kangwei Liu, name: Ningyu Zhang, name: Xinle Deng, name: Penghui Yang, name: Zhuo Chen, name: Xiangru Tang, name: Mark Gerstein, name: Xiaohui Fan, name: Huajun Chen

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.