ScProteoAgent enables natural-language-driven single-cell proteomics analysis and interpretation
- 1ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, China; Department of Chemistry, Zhejiang University, Hangzhou
Abstract
Single-cell proteomics requires computational choices to remain aligned with experimental design as research questions evolve. Here we present ScProteoAgent, which translates natural-language requests into domain-specific calculations and preserves research intent, analysis design, matrix-processing history and statistical outputs as reusable analytical state. Follow-up requests reuse applicable inputs and initiate new calculations when contrasts or experimental units change. We assembled a benchmark comprising 55 tasks and 33 reference conclusions from 11 published studies, combining progressive questions within studies with complementary biological and analytical settings. In a retrospective comparison of 88 archived outputs from eight systems, ScProteoAgent achieved the highest mean score of 93.67 under a common six-dimensional rule-based evaluation. A HeLa migration case connected phenotype composition with proteomic clusters, candidate differences and original-observation support. In liver zonation, a researcher-guided follow-up reused a saved matrix to test mouse-level spatial contrasts and compare full-matrix and observed-only results. Applications in brain development, sample preservation and hematopoiesis illustrated how interpretation depends on processing choices and experimental units. ScProteoAgent connects research questions with statistical results and their analytical context, enabling existing analyses to support more specific biological investigations.
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BibTeX
@misc{hu2026scproteoagent,
title = {{ScProteoAgent enables natural-language-driven single-cell proteomics analysis and interpretation}},
author = {R. Hu and K. Ding and W. Wang and Z. Zhu and P. Chen and S. Wang and Y. Wang},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.24.754011},
url = {https://www.biorxiv.org/content/10.64898/2026.09.24.754011v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - ScProteoAgent enables natural-language-driven single-cell proteomics analysis and interpretation
AU - R. Hu
AU - K. Ding
AU - W. Wang
AU - Z. Zhu
AU - P. Chen
AU - S. Wang
AU - Y. Wang
PY - 2026
DA - 2026/09/24
PB - bioRxiv
DO - 10.64898/2026.09.24.754011
UR - https://www.biorxiv.org/content/10.64898/2026.09.24.754011v1
AB - Single-cell proteomics requires computational choices to remain aligned with experimental design as research questions evolve. Here we present ScProteoAgent, which translates natural-language requests into domain-specific calculations and preserves research intent, analysis design, matrix-processing history and statistical outputs as reusable analytical state. Follow-up requests reuse applicable inputs and initiate new calculations when contrasts or experimental units change. We assembled a benchmark comprising 55 tasks and 33 reference conclusions from 11 published studies, combining progressive questions within studies with complementary biological and analytical settings. In a retrospective comparison of 88 archived outputs from eight systems, ScProteoAgent achieved the highest mean score of 93.67 under a common six-dimensional rule-based evaluation. A HeLa migration case connected phenotype composition with proteomic clusters, candidate differences and original-observation support. In liver zonation, a researcher-guided follow-up reused a saved matrix to test mouse-level spatial contrasts and compare full-matrix and observed-only results. Applications in brain development, sample preservation and hematopoiesis illustrated how interpretation depends on processing choices and experimental units. ScProteoAgent connects research questions with statistical results and their analytical context, enabling existing analyses to support more specific biological investigations.
ER -