Cell2Sentence: Teaching Large Language Models the Language of Biology

Abstract

Large language models like GPT have shown impressive performance on natural language tasks. Here, we present a novel method to directly adapt these pretrained models to a biological context, specifically single-cell transcriptomics, by representing gene expression data as text. Our Cell2Sentence approach converts each cell’s gene expression profile into a sequence of gene names ordered by expression level. We show that these gene sequences, which we term “cell sentences”, can be used to fine-tune causal language models like GPT-2. Critically, we find that natural language pretraining boosts model performance on cell sentence tasks. When fine-tuned on cell sentences, GPT-2 generates biologically valid cells when prompted with a cell type. Conversely, it can also accurately predict cell type labels when prompted with cell sentences. This demonstrates that language models fine-tuned using Cell2Sentence can gain a biological understanding of single-cell data, while retaining their ability to generate text. Our approach provides a simple, adaptable framework to combine natural language and transcriptomics using existing models and libraries. Our code is available at: https://github.com/vandijklab/cell2sentence-ft.