ProteoScopeR: A Shiny Workflow for Method Comparison and Reproducible Quantitative Proteomics Analysis
- 1Biomedical Research Center, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
Abstract
Quantitative proteomics requires decisions about normalization, missing-data handling, and statistical modeling that can change the reported results. ProteoScopeR is an R package and Shiny application that connects these decisions in a traceable workflow and complements downstream exploration in xOmicsShiny. Side-by-side comparisons expose changes in distributions, feature retention, effect estimates, and selected protein sets. An optional external artificial-intelligence assistant reviews exported evidence and proposes settings for researcher approval; statistical calculations remain in R. A group-only case study used DIA-NN-derived aqueous humor data from 69 samples and 3,667 imported protein groups. Six normalization methods and seven missing-data strategies were screened, and the approved cyclic-loess analysis compared five differential workflows. For nAMD versus pmCNV, limma and proDA selected 54 and 37 protein groups, respectively, with 31 shared selections at adjusted P < 0.05 and absolute log2 fold change [≥] 0.5. Input routing, scientist decisions, locked settings, and an execution receipt preserve the analysis record. These comparisons describe sensitivity to analytical choices rather than identify a universally superior method.
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BibTeX
@misc{gao2026proteoscoper,
title = {{ProteoScopeR: A Shiny Workflow for Method Comparison and Reproducible Quantitative Proteomics Analysis}},
author = {B. Gao and H. Zhao},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.20.752977},
url = {https://www.biorxiv.org/content/10.64898/2026.09.20.752977v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - ProteoScopeR: A Shiny Workflow for Method Comparison and Reproducible Quantitative Proteomics Analysis
AU - B. Gao
AU - H. Zhao
PY - 2026
DA - 2026/09/20
PB - bioRxiv
DO - 10.64898/2026.09.20.752977
UR - https://www.biorxiv.org/content/10.64898/2026.09.20.752977v1
AB - Quantitative proteomics requires decisions about normalization, missing-data handling, and statistical modeling that can change the reported results. ProteoScopeR is an R package and Shiny application that connects these decisions in a traceable workflow and complements downstream exploration in xOmicsShiny. Side-by-side comparisons expose changes in distributions, feature retention, effect estimates, and selected protein sets. An optional external artificial-intelligence assistant reviews exported evidence and proposes settings for researcher approval; statistical calculations remain in R. A group-only case study used DIA-NN-derived aqueous humor data from 69 samples and 3,667 imported protein groups. Six normalization methods and seven missing-data strategies were screened, and the approved cyclic-loess analysis compared five differential workflows. For nAMD versus pmCNV, limma and proDA selected 54 and 37 protein groups, respectively, with 31 shared selections at adjusted P < 0.05 and absolute log2 fold change [≥] 0.5. Input routing, scientist decisions, locked settings, and an execution receipt preserve the analysis record. These comparisons describe sensitivity to analytical choices rather than identify a universally superior method.
ER -