title: PSEA-Quant: a protein set enrichment analysis on label-free and label-based protein quantification data.
abstract: The majority of large-scale proteomics quantification methods yield long lists of quantified proteins that are often difficult to interpret and poorly reproduced. Computational approaches are required to analyze such intricate quantitative proteomics data sets. We propose a statistical approach to computationally identify protein sets (e.g., Gene Ontology (GO) terms) that are significantly enriched with abundant proteins with reproducible quantification measurements across a set of replicates. To this end, we developed PSEA-Quant, a protein set enrichment analysis algorithm for label-free and label-based protein quantification data sets. It offers an alternative approach to classic GO analyses, models protein annotation biases, and allows the analysis of samples originating from a single condition, unlike analogous approaches such as GSEA and PSEA. We demonstrate that PSEA-Quant produces results complementary to GO analyses. We also show that PSEA-Quant provides valuable information about the biological processes involved in cystic fibrosis using label-free protein quantification of a cell line expressing a CFTR mutant. Finally, PSEA-Quant highlights the differences in the mechanisms taking place in the human, rat, and mouse brain frontal cortices based on tandem mass tag quantification. Our approach, which is available online, will thus improve the analysis of proteomics quantification data sets by providing meaningful biological insights.
authors.affiliation: Department of Chemical Physiology, The Scripps Research Institute , 10550 N. Torrey Pines Rd., La Jolla, California 92037, United States.
authors.forename: Mathieu
authors.initials: M
authors.lastname: Lavallée-Adam
authors.forename: Navin
authors.initials: N
authors.lastname: Rauniyar
authors.forename: Daniel B
authors.initials: DB
authors.lastname: McClatchy
authors.forename: John R
authors.initials: JR
authors.lastname: Yates
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000378:metabolism; D002460:Cell Line; D002853:Chromatography, Liquid; D019295:Computational Biology / Q000379:methods*; D019005:Cystic Fibrosis Transmembrane Conductance Regulator / Q000235:genetics / Q000378:metabolism; D030562:Databases, Protein; D006801:Humans; D051379:Mice; D009154:Mutation; D011506:Proteins / Q000032:analysis* / Q000378:metabolism; D020543:Proteome / Q000032:analysis* / Q000378:metabolism; D040901:Proteomics / Q000379:methods*; D051381:Rats; D053719:Tandem Mass Spectrometry
chemical_list: D011506:Proteins; D020543:Proteome; D019005:Cystic Fibrosis Transmembrane Conductance Regulator
keywords: bioinformatics; computational biology; cystic fibrosis; gene ontology; gene set enrichment analysis; isobaric tandem mass tagging; mass spectrometry; protein quantification; spectral counting; statistics