Robust determination of differential abundance in shotgun proteomics using nonparametric statistics.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:30259924", "kind": "science-record", "revision": 3, "content_hash": "sha256:9d874bf99d15abab2c2cfabb9510560652fa83b168f3a89f3f6561f864383697", "created_at": "2026-11-01T17:32:23Z", "updated_at": "2026-11-01T21:21:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Robust determination of differential abundance in shotgun proteomics using nonparametric statistics.", "doi": "10.1039/c8mo00077h", "pmid": "30259924", "authors": ["Patrick Slama", "Michael R Hoopmann", "Robert L Moritz", "Donald Geman"], "journal": "Molecular omics", "year": 2018, "abstract": "Label-free shotgun mass spectrometry enables the detection of significant changes in protein abundance between different conditions. Due to often limited cohort sizes or replication, large ratios of potential protein markers to number of samples, as well as multiple null measurements pose important technical challenges to conventional parametric models. From a statistical perspective, a scenario similar to that of unlabeled proteomics is encountered in genomics when looking for differentially expressed genes. Still, the difficulty of detecting a large fraction of the true positives without a high false discovery rate is arguably greater in proteomics due to even smaller sample sizes and peptide-to-peptide variability in detectability. These constraints argue for nonparametric (or distribution-free) tests on normalized peptide values, thus minimizing the number of free parameters, as well as for measuring significance with permutation testing. We propose such a procedure with a class-based statistic, no parametric assumptions, and no parameters to select other than a nominal false discovery rate. Our method was tested on a new dataset which is available via ProteomeXchange with identifier PXD006447. The dataset was prepared using a standard proteolytic digest of a human protein mixture at 1.5-fold to 3-fold protein concentration changes and diluted into a constant background of yeast proteins. We demonstrate its superiority relative to other approaches in terms of the realized sensitivity and realized false discovery rates determined by ground truth, and recommend it for detecting differentially abundant proteins from MS data.", "claims": [{"id": "claim-method", "statement": "We applied our score (CSNorm, Eq. 2) to PI data in order to detect proteins with differential abundance across samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30259924", "locator": "sections[14] \"Performance of CSNorm-PI\" paragraph 2", "quote": "We applied our score (CSNorm, Eq. 2) to PI data in order to detect proteins with differential abundance across samples."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository30 with dataset identifier PXD006447.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30259924", "locator": "sections[5] \"MS/MS-data processing\" paragraph 2", "quote": "The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository30 with dataset identifier PXD006447."}], "state": "complete"}, {"id": "claim-target-result", "statement": "For C5vsC6, CSNorm-PI detected all 48 UPS1 proteins at ND = 60, whereas CSLog-PI missed 4 UPS1 proteins among its 60 most significant ones and only 1 among its 100 most significant ones.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:30259924", "locator": "sections[14] \"Performance of CSNorm-PI\" paragraph 3", "quote": "For C5vsC6, CSNorm-PI detected all 48 UPS1 proteins at ND = 60 (Table 2), whereas CSLog-PI missed 4 UPS1 proteins among its 60 most significant ones and only 1 among its 100 most significant ones."}, {"asset": "source:pmid:30259924", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We demonstrate its superiority relative to other approaches in terms of the realized sensitivity and realized false discovery rates determined by ground truth, and recommend it for detecting differentially abundant proteins from MS data."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "For C5vsC6, CSNorm-PI detected all 48 UPS1 proteins at ND = 60, whereas CSLog-PI missed 4 UPS1 proteins among its 60 most significant ones and only 1 among its 100 most significant ones.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:30259924"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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