title: Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA.
abstract: Statistical tools enable unified analysis of data from multiple global proteomic experiments, producing unbiased estimates of normalization terms despite the missing data problem inherent in these studies. The modeling approach, implementation, and useful visualization tools are demonstrated via a case study of complex biological samples assessed using the iTRAQ relative labeling protocol.
authors.affiliation: Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, Minnesota 55905, USA. oberg.ann@mayo.edu
authors.forename: Ann L
authors.initials: AL
authors.lastname: Oberg
authors.forename: Douglas W
authors.initials: DW
authors.lastname: Mahoney
authors.forename: Jeanette E
authors.initials: JE
authors.lastname: Eckel-Passow
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Malone
authors.forename: Russell D
authors.initials: RD
authors.lastname: Wolfinger
authors.forename: Elizabeth G
authors.initials: EG
authors.lastname: Hill
authors.forename: Leslie T
authors.initials: LT
authors.lastname: Cooper
authors.forename: Oyere K
authors.initials: OK
authors.lastname: Onuma
authors.forename: Craig
authors.initials: C
authors.lastname: Spiro
authors.forename: Terry M
authors.initials: TM
authors.lastname: Therneau
authors.forename: H Robert
authors.initials: HR
authors.lastname: Bergen
mesh_terms: D000704:Analysis of Variance; D045424:Complex Mixtures / Q000032:analysis*; D003196:Computer Graphics; D003627:Data Interpretation, Statistical; D007553:Isotope Labeling / Q000379:methods; D007554:Isotopes; D013058:Mass Spectrometry; D040901:Proteomics / Q000379:methods
chemical_list: D045424:Complex Mixtures; D007554:Isotopes