title: Total variance should drive data handling strategies in third generation proteomic studies.
abstract: Quantitative proteomics is entering its "third generation," where intricate experimental designs aim to increase the spatial and temporal resolution of protein changes. This paper re-analyses multiple internally consistent proteomic datasets generated from whole cell homogenates and fractionated brain tissue samples providing a unique opportunity to explore the different factors influencing experimental outcomes. The results clearly indicate that improvements in data handling are required to compensate for the increased mean CV associated with complex study design and intricate upstream tissue processing. Furthermore, applying arbitrary inclusion thresholds such as fold change in protein abundance between groups can lead to unnecessary exclusion of important and biologically relevant data.
authors.affiliation: Centre for Cognitive and Neural Systems, University of Edinburgh, Edinburgh, UK.
authors.forename: Abigail G
authors.initials: AG
authors.lastname: Herrmann
authors.forename: James L
authors.initials: JL
authors.lastname: Searcy
authors.forename: Thierry
authors.initials: T
authors.lastname: Le Bihan
authors.forename: James
authors.initials: J
authors.lastname: McCulloch
authors.forename: Ruth F
authors.initials: RF
authors.lastname: Deighton
mesh_terms: D000818:Animals; D015415:Biomarkers / Q000032:analysis* / Q000737:chemistry; D001923:Brain Chemistry; D002460:Cell Line; D002853:Chromatography, Liquid / Q000379:methods; D030562:Databases, Protein; D042541:Intracellular Space / Q000737:chemistry; D051379:Mice; D011506:Proteins / Q000032:analysis / Q000737:chemistry; D040901:Proteomics / Q000379:methods*; D053719:Tandem Mass Spectrometry / Q000379:methods
chemical_list: D015415:Biomarkers; D011506:Proteins
keywords: Bioinformatics; Differential protein expression; LC-MS/MS; Protein marker discovery; Proteome analysis