title: Statistical design and analysis of label-free LC-MS proteomic experiments: a case study of coronary artery disease.
abstract: This chapter presents a case study, which applies statistical design and analysis to an LC-MS-based -investigation of subjects with coronary artery disease. First, we discuss the principles of statistical -experimental design, and the specification of an Analysis of Variance (ANOVA) model that describes the major sources of variation in the data. Second, we discuss procedures for detecting differentially abundant proteins, estimating protein abundance in individual samples, testing predefined groups of proteins for enrichment in differential abundance, and calculating sample size for a future experiment. The discussion is accompanied by examples of computer code implemented in the open-source statistical software R, which can be followed for an independent implementation of a similar investigation.
authors.affiliation: Department of Statistics, Purdue University, West Lafayette, IN, USA.
authors.forename: Timothy
authors.initials: T
authors.lastname: Clough
authors.forename: Siegmund
authors.initials: S
authors.lastname: Braun
authors.forename: Vladimir
authors.initials: V
authors.lastname: Fokin
authors.forename: Ilka
authors.initials: I
authors.lastname: Ott
authors.forename: Susanne
authors.initials: S
authors.lastname: Ragg
authors.forename: Gunther
authors.initials: G
authors.lastname: Schadow
authors.forename: Olga
authors.initials: O
authors.lastname: Vitek
mesh_terms: D000704:Analysis of Variance; D016022:Case-Control Studies; D002853:Chromatography, Liquid; D003324:Coronary Artery Disease / Q000097:blood / Q000378:metabolism*; D030562:Databases, Protein; D005260:Female; D006801:Humans; D008297:Male; D013058:Mass Spectrometry / Q000379:methods*; D008875:Middle Aged; D015233:Models, Statistical; D020543:Proteome / Q000378:metabolism; D040901:Proteomics / Q000379:methods*; D018401:Sample Size; D013194:Staining and Labeling
chemical_list: D020543:Proteome