Cell2SentenceOpen-source framework that turns single-cell gene expression into 'cell sentences' so LLMs can analyze and generate biology insights.

Cell2Sentence is an open-source framework that transforms single-cell gene expression data into 'cell sentences' for analysis and insight generation by Large Language Models (LLMs). It proposes a rank-ordering transformation of expression vectors into cell sentences, which are space-separated gene names ordered by descending expression. This allows LLMs to natively model single-cell RNA sequencing (scRNA-seq) data using natural language. The framework includes the C2S-Scale models, which unify transcriptomic and textual data and enable advanced single-cell tasks such as perturbation prediction, dataset summarization, cluster captioning, and biological question answering. The C2S-Scale models are available on Hugging Face and are based on architectures like Pythia and Gemma-2. Cell2Sentence is aimed at researchers and scientists working with single-cell transcriptomics data. The framework has been updated with new models and features, including support for fine-tuning on custom prompt templates and multi-cell prompt formatting. It also includes a suite of Pythia models for cell type prediction, cell type conditioned generation, and a diverse multi-cell multi-task model trained on over 57 million human and mouse cells. The Cell2Sentence framework is documented and has tutorials for use, including examples of fine-tuning and multi-cell prompt formatting. The development of Cell2Sentence involves the van Dijk Lab and has been published in a preprint on bioRxiv. Cell2Sentence enables next-generation single-cell discovery with LLMs.