scPilot: Large Language Model Reasoning Toward Automated Single-Cell Analysis and Discovery
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
6990ec1550fb2c0be4783741
Yiming Gao
6990ec1550fb2c0be4783742
Zhen Wang
claimed_verified
2026-02-17T15:52:21.067Z
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Zhen Wang
zhenwang9102
user
zhenwang9102
6990ec1550fb2c0be4783743
Jefferson Chen
6990ec1550fb2c0be4783744
Mark Antkowiak
6990ec1550fb2c0be4783745
Mengzhou Hu
6990ec1550fb2c0be4783746
JungHo Kong
6990ec1550fb2c0be4783747
Dexter Pratt
6990ec1550fb2c0be4783748
Jieyuan Liu
6990ec1550fb2c0be4783749
Enze Ma
6990ec1550fb2c0be478374a
Zhiting Hu
6990ec1550fb2c0be478374b
Eric P. Xing
scPilot: Large Language Model Reasoning Toward Automated Single-Cell Analysis and Discovery
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 presents a framework for omics-native reasoning where large language models directly analyze single-cell RNA-seq data through step-by-step reasoning processes, improving accuracy and interpretability in cell-type annotation and developmental trajectory reconstruction.
large language model single-cell RNA-seq data cell-type annotation developmental-trajectory reconstruction transcription-factor targeting omics-native reasoning step-by-step reasoning reasoning traces marker gene ambiguity regulatory logic