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

• URL: http://arxiv.org/abs/2602.11609v1
• Date: Thu, 12 Feb 2026 06:04:11 GMT
• Title: scPilot: Large Language Model Reasoning Toward Automated Single-Cell Analysis and Discovery
• Authors: Yiming Gao, Zhen Wang, Jefferson Chen, Mark Antkowiak, Mengzhou Hu, JungHo Kong, Dexter Pratt, Jieyuan Liu, Enze Ma, Zhiting Hu, Eric P. Xing,
• Abstract summary: scPilot is the first systematic framework to practice omics-native reasoning.<n>It converts core single-cell analyses into step-by-step reasoning problems.<n> Experiments with o1 show that iterative omics-native reasoning lifts average accuracy by 11%.
• Score: 35.61445738447131
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present scPilot, the first systematic framework to practice 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 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 explain marker gene ambiguity and regulatory logic. By grounding LLMs in raw omics data, scPilot enables auditable, interpretable, and diagnostically informative single-cell analyses. Code, data, and package are available at https://github.com/maitrix-org/scPilot

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