Signatures for mass spectrometry data quality.

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Metrics for describing liquid chromatography mass spectrometry (LC-MS) data have been developed; however, the wide variety of LC-MS instruments and configurations precludes applying a simple cutoff. Using 1150 manually classified quality control (QC) data sets, we trained logistic regression classification models to predict whether a data set is in or out of control. Model parameters were optimized by minimizing a loss function that accounts for the trade-off between false positive and false negative errors. The classifier models detected bad data sets with high sensitivity while maintaining high specificity. Moreover, the composite classifier was dramatically more specific than single metrics. Finally, we evaluated the performance of the classifier on a separate validation set where it performed comparably to the results for the testing/training data sets. By presenting the methods and software used to create the classifier, other groups can create a classifier for their specific QC regimen, which is highly variable lab-to-lab. In total, this manuscript presents 3400 LC-MS data sets for the same QC sample (whole cell lysate of Shewanella oneidensis), deposited to the ProteomeXchange with identifiers PXD000320-PXD000324.", "claims": [{"id": "claim-method", "statement": "Leveraging a large corpus of manually curated QC data, we developed a Lasso logistic regression classifier (LLRC) that predicts, with high sensitivity and specificity, whether a QC data set is in or out of control.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:24611607", "locator": "sections[1] \"Introduction\" paragraph 4", "quote": "Leveraging a large corpus of manually curated QC data, we developed a Lasso logistic regression classifier (LLRC) that predicts, with high sensitivity and specificity, whether a QC data set is in or out of control."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data used for this project are available at http://omics.pnl.gov and have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository16 with the data set identifiers PXD000320, PXD000321, PXD000322, PXD000323, and PXD000324. 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Moreover, the composite classifier was dramatically more specific than single metrics."}, {"asset": "source:pmid:24611607", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In total, this manuscript presents 3400 LC–MS data sets for the same QC sample (whole cell lysate of Shewanella oneidensis), deposited to the ProteomeXchange with identifiers PXD000320–PXD000324."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Lasso logistic regression classifiers trained on 1150 manually classified QC data sets detected out-of-control data sets with high sensitivity and specificity and were dramatically more specific than single metrics; the complete corpus of 3400 LC–MS data sets is accessible through ProteomeXchange identifiers PXD000320–PXD000324.", "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:24611607"], "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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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["glm_logit_fit"], "assets": ["source:pmid:24611607", "data:geo:PXD000320", "asset:table:pmid:24611607:1", "asset:table:pmid:24611607:2", "asset:table:pmid:24611607:3", "asset:table:pmid:24611607:4"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:PXD000320"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-10-31T18:22:08Z", "independence": "same_provider"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 8, "usage": {"prompt_tokens": 70874, "completion_tokens": 4249, "cached_tokens": 53668}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:24611607", "type": "paper", "label": "pmid:24611607"}, {"id": "source:pmid:24611607", "type": "manuscript", "label": "PMID 24611607 full text"}, {"id": "data:geo:PXD000320", "type": "dataset", "label": "GEO PXD000320"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:24611607", "type": "paper", "label": "pmid:24611607"}, {"id": "source:pmid:24611607", "type": "manuscript", "label": "PMID 24611607 full text"}, {"id": "data:geo:PXD000320", "type": "dataset", "label": "GEO PXD000320"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-24611607", "type": "extraction", "used": ["source:pmid:24611607"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-PXD000320", "type": "accession_resolution", "used": ["data:geo:PXD000320"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:24611607", "parent_hash": "sha256:6e34f6982acfd2b6ab216088bade5647252edc6822c633a84e29346820679c1d", "fork_of": null, "generation": 5}, "gaps": ["asset:data:geo:PXD000320 licence not read", "asset:data:geo:PXD000320 not downloaded", "asset:data:geo:PXD000321 licence not read", "asset:data:geo:PXD000321 not downloaded", "asset:data:geo:PXD000322 licence not read", "asset:data:geo:PXD000322 not downloaded", "asset:data:geo:PXD000323 licence not read", "asset:data:geo:PXD000323 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "mass_spectrometry", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["Signatures for mass spectrometry data quality.", "Metrics for describing liquid chromatography mass spectrometry (LC-MS) data have been developed; however, t", "mple in our laboratory to provide sufficient proteomic complexity to assess both LC and MS performa", "C) and frozen at −80 °C until processing for proteomic analysis. Enough material was prepared in th"], "order_basis": "paper_text_order:0", "id": "assay:1"}, {"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.77, "evidence": ["d to a concentration of 1 mM. Next, trypsin (Affymetrix, Santa Clara CA) was added in a ratio 1:50 t"], "order_basis": "paper_text_order:3227", "id": "assay:2"}, {"role": "analysis", "definition_id": "regression_modelling", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["glm_logit_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Leveraging a large corpus of manually curated QC data, we developed a Lasso logistic regression classifier (LLRC) that predicts, with high sensitivity and specificity, whether a QC data set is in or out of control."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}