CellVoyager: AI CompBio agent generates new insights by autonomously analyzing biological data.

Abstract

Modern biology increasingly relies on complex, high-dimensional datasets such as single-cell RNA sequencing (scRNA-seq), which present a vast space of potential hypotheses. Systematically exploring this space is often impractical, as scRNA-seq analyses are time-consuming and require substantial computational and domain expertise. To address this challenge, we introduce CellVoyager, an AI agent built on large language models that autonomously generates and implements scRNA-seq analyses within a Jupyter notebook environment. We evaluate CellVoyager on CellBench, a benchmark of 76 published scRNA-seq studies, where it outperforms GPT-4o and o3-mini by up to 23% in predicting which analyses authors ultimately conducted, given only the papers' background sections. Across three in-depth case studies, CellVoyager generated novel findings in COVID-19, cell-cell communication and aging that experts consistently rated as creative and scientifically sound. These results demonstrate CellVoyager's potential to accelerate computational biology and uncover missing insights by autonomously analyzing biological data at scale.