scPilot：基於大型語言模型推理的自動化單細胞分析與發現系統

摘要

我們推出scPilot——首個實踐組學原生推理的系統性框架：大型語言模型（LLM）能以自然語言對話，同時直接檢視單細胞RNA測序數據並按需調用生物信息學工具。scPilot將核心單細胞分析（包括細胞類型註釋、發育軌跡重建和轉錄因子靶向分析）轉化為逐步推理問題，要求模型必須解決、論證並在必要時根據新證據修正結論。為量化進展，我們發布scBench——包含9個專家精心策劃的數據集與評分模組，可精準評估scPilot相較各類LLM的組學原生推理能力。使用o1模型的實驗表明，迭代式組學原生推理使細胞類型註釋的平均準確率提升11%；Gemini-2.5-Pro相較單次提示法將軌跡圖編輯距離縮減30%，同時生成可解釋標記基因模糊性與調控邏輯的透明推理軌跡。通過將LLM錨定於原始組學數據，scPilot實現了可審計、可解釋且具診斷價值的單細胞分析。代碼、數據及軟件包已開源於：https://github.com/maitrix-org/scPilot

English

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

scPilot：基於大型語言模型推理的自動化單細胞分析與發現系統

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

摘要

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