ssGSEA3.0: Feature-Level Bootstrap Resampling for Robust Assessment of Statistical Significance
- 1Broad Institute of MIT and Harvard
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.
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BibTeX
@misc{clark2026ssgsea30,
title = {{ssGSEA3.0: Feature-Level Bootstrap Resampling for Robust Assessment of Statistical Significance}},
author = {N. M. Clark and D. R. Mani},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.28.762524},
url = {https://www.biorxiv.org/content/10.64898/2026.10.28.762524v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - ssGSEA3.0: Feature-Level Bootstrap Resampling for Robust Assessment of Statistical Significance
AU - N. M. Clark
AU - D. R. Mani
PY - 2026
DA - 2026/10/28
PB - bioRxiv
DO - 10.64898/2026.10.28.762524
UR - https://www.biorxiv.org/content/10.64898/2026.10.28.762524v1
AB - 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.
ER -