ChatCell：利用自然語言促進單細胞分析

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

隨著大型語言模型（LLMs）的快速演進，它們在科學領域中的影響日益突出。LLMs在任務泛化和自由對話方面的新興能力可以顯著推動化學和生物學等領域的發展。然而，作為構成生物體基礎結構的單細胞生物學領域仍面臨著一些挑戰。目前方法中存在的高知識門檻和有限的可擴展性限制了LLMs在掌握單細胞數據方面的充分應用，阻礙了直接訪問和快速迭代。為此，我們引入了ChatCell，通過自然語言促進單細胞分析，標誌著一種範式轉變。ChatCell利用詞彙適應和統一序列生成，已經獲得了對單細胞生物學的深刻專業知識和適應各種分析任務的能力。大量實驗進一步展示了ChatCell的穩健表現和加深單細胞洞察力的潛力，為在這一關鍵領域中進行更易接近和直觀探索鋪平了道路。我們的項目主頁位於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.