{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Biomedical Research Center, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "B. Gao"
    },
    {
      "name": "H. Zhao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5vk9w1x1bw532.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.20.752977",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.20.752977v1.full",
  "id": "5vk9w1x1bw532",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.20.752977v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1607|Advanced Proteomics Techniques and Applications"
    ]
  ],
  "openalex": "W7214286080",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.20.752977v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5vk9w1x1bw532.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:35Z",
      "native_id": "10.64898/2026.09.20.752977",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.20.752977v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "ProteoScopeR: A Shiny Workflow for Method Comparison and Reproducible Quantitative Proteomics Analysis",
  "title_text": "ProteoScopeR: A Shiny Workflow for Method Comparison and Reproducible Quantitative Proteomics Analysis",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:35Z",
  "url": "https://research.draconitic.com/paper/5vk9w1x1bw532",
  "venue": "bioRxiv",
  "versions": []
}