{"database":"MassIVE","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Other":["ftp://massive-ftp.ucsd.edu/v10/MSV000098622/"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"submitter":["Ozge Karayel"],"full_dataset_link":["https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=fdf984642108451cbdece7303e47f2d2"],"submitter_email":["karayelo@gene.com"],"sample_protocol":[""],"repository":["MassIVE"],"file_size":["84"],"ptm_modification":["MS:1002864 - No post-translational-modifications are included in the identified peptides of this dataset"],"data_protocol":[""],"omics_type":["Proteomics"],"instrument_platform":["Orbitrap Astral"],"species":["Homo Sapiens (ncbitaxon:9606)"],"submitter_affiliation":["Genentech"],"additional_accession":["PXD066486"]},"is_claimable":false,"name":"Accounting for longitudinal peak quality metrics with MSstats+ enhances differential analysis in proteomic experiments with data-independent acquisition","description":"Mass spectrometry-based proteomics with data-independent acquisition benefits\nfrom advanced instrumentation and computational analysis. Despite continued im-\nprovements, the quality of quantification may be poor for some measurements. As\nthe scale of proteomic experiments increases, these poor-quality measurements are\nchallenging to characterize by hand, yet they undermine the detection of differentially\nabundant proteins and the downstream biological conclusions. We introduce MSstats+,\na computational workflow that takes as input not only peak intensities reported by data\nprocessing tools such as Spectronaut, but also quality metrics such as peak shape and\nretention time, as well as longitudinal run order profiles of these metrics. MSstats+\ntranslates these metrics into a single measure of quality, and downweights poor quality\nmeasurements when detecting differentially abundant proteins. The method offers a\nnatural treatment of missing value imputation, weighting the imputed values according\nto the quality metrics in the run. We demonstrate the accuracy of the resulting differ-\nential analysis, as compared to the standard implementations, in four experiments: two\ncustom benchmarking studies with intentionally induced anomalies, a controlled mix-\nture of proteomes, and a large-scale clinical investigation. MSstats+ is implemented in\nthe family of open-source R/Bioconductor packages MSstats, making it accessible for\nroutine and modular use.","dates":{"publication":"Wed Jul 23 11:16:00 BST 2025"},"accession":"MSV000098622","cross_references":{}}