scPilot: Large Language Model Reasoning Toward Automated Single-Cell Analysis and Discovery

We present scPilot, the first systematic framework to practice \textit{omics-native reasoning}: a large language model (LLM) converses in natural language while directly inspecting single-cell RNA-seq data and on-demand bioinformatics tools. scPilot converts core single-cell analyses, i. e. , cell-type annotation, developmental-trajectory reconstruction, and transcription-factor targeting, into step-by-step reasoning problems that the model must solve, justify, and, when needed, revise with new evidence. To measure progress, we release \scbench, a suite of 9 expertly curated datasets and graders that faithfully evaluate the omics-native reasoning capability of scPilot w. r. t various LLMs. Experiments with o1 show that \textit{iterative} omics-native reasoning lifts average accuracy by 11\% for cell-type annotation and Gemini 2. 5 Pro cuts trajectory graph-edit distance by 30\% versus one-shot prompting, while generating transparent reasoning traces that explain marker gene ambiguity and regulatory logic. By grounding LLMs in raw omics data, scPilot enables auditable, interpretable, and diagnostically informative single-cell analyses.

Authors

• Yiming Gao
• Zhen Wang
• Jefferson Chen
• Mark Antkowiak
• Mengzhou Hu
• JungHo Kong
• Dexter Pratt
• Jieyuan Liu
• Enze Ma
• Zhiting Hu
• Eric Xing Washington University in St. Louis

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