{
  "abstract": "Motivation Single-sample Gene/Post-Translational Modification Set Enrichment Analysis (ssGSEA/PTM-SEA) methods assess pathway or signature activity per sample using permutation-based significance testing. ssGSEA2.0's weighted scoring (ssGSEA2.0-orig) uses a correlation-vector reordering that inflates false positives at nonzero weight; directly fixing this (ssGSEA2.0-fixed) restores null-distribution calibration but sacrifices detection power at nonzero weight. Results ssGSEA3.0 replaces feature-label (gene- or site-level) permutation with feature-level bootstrap resampling, which remains calibrated across weighting schemes. On published lung adenocarcinoma and phosphoproteomic perturbation datasets, ssGSEA3.0 shows greater detection power than ssGSEA2.0-fixed, with an additional gain for directional (PTM-SEA) scoring. Previously published findings remain robust across methods, including at nonzero weight. Bootstrapping adds a ~1.5- to 2-fold computational cost. Availability and Implementation ssGSEA3.0 is implemented as an open-source R package (github.com/broadinstitute/ssGSEA3.0, DOI: 10.5281/zenodo.23734262) and includes the worked examples on biological data presented here.",
  "abstract_shown": true,
  "affiliations": [
    "Broad Institute of MIT and Harvard"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "N. M. Clark"
    },
    {
      "affs": [
        0
      ],
      "name": "D. R. Mani"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/akp2m9m1dkv9n.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.28.762524",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762524v1.full",
  "id": "akp2m9m1dkv9n",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762524v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762524v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/akp2m9m1dkv9n.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T00:21:37Z",
      "native_id": "10.64898/2026.10.28.762524",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762524v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "ssGSEA3.0: Feature-Level Bootstrap Resampling for Robust Assessment of Statistical Significance",
  "title_text": "ssGSEA3.0: Feature-Level Bootstrap Resampling for Robust Assessment of Statistical Significance",
  "type": "preprint",
  "updated_at": "2026-11-05T00:21:06Z",
  "url": "https://research.draconitic.com/paper/akp2m9m1dkv9n",
  "venue": "bioRxiv",
  "versions": []
}