Cell2Sentence
Overview
Open-source framework that turns single-cell gene expression into 'cell sentences' so LLMs can analyze and generate biology insights.
Cell2Sentence (C2S) is an open-source framework from the van Dijk Lab that represents single-cell RNA-seq profiles as textual “cell sentences” (genes ordered by expression), enabling large language models to natively model biology. The project includes code, docs, and open models (C2S-Scale) released with Google Research/DeepMind for state-of-the-art single-cell tasks like cell typing, perturbation prediction, and virtual cell generation.
Product overview
Capabilities, pricing and fit
Cell2Sentence is best understood as a biology language-model research project rather than a general research agent. Cell2Sentence: Teaching Large Language Models the Language of Biology.
Information as of July 30, 2026
What it can do
- Research and decision support
Cell2Sentence: Teaching Large Language Models the Language of Biology.[1]
Where it fits best
- Transforming scRNA-seq data into text so LLMs can learn cellular patterns.[1]
Pricing and availability
Access: Source repository[1]
Sources (1)
Autonomy level
54/100
Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.
Comparisons
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Some of the use cases of Cell2Sentence:
- Transforming scRNA-seq data into text so LLMs can learn cellular patterns.
- Training and evaluating LLMs on single-cell tasks (e.g., cell type prediction).
- Generating “virtual cells” and simulating perturbation responses.
- Integrating biology text and metadata with transcriptomic signals for joint modeling.
- Reproducing C2S pipelines with open code, docs, and released models.
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