ChatCell: Facilitating Single-Cell Analysis with Natural Language

TL;DR

ChatCell introduces a natural language interface for single-cell biology analysis, leveraging large language models to improve accessibility, versatility, and understanding of complex biological data.

Contribution

The paper presents ChatCell, a novel framework that adapts LLMs for single-cell analysis, enabling diverse tasks through vocabulary adaptation and sequence generation.

Findings

ChatCell demonstrates robust performance across multiple single-cell analysis tasks.

The approach significantly lowers barriers to entry in single-cell biology research.

ChatCell enhances interpretability and accessibility of complex biological data.

Abstract

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.