Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA



Ann L. Oberg,¹ Douglas W. Mahoney,¹ Jeanette E. Eckel-Passow,¹ Christopher J. Malone,² Russell D. Wolfinger,³ Elizabeth G. Hill,⁴ Leslie T. Cooper,⁵ Oyere K. Onuma,⁶ Craig Spiro,⁷ Terry M. Therneau,¹ and H. Robert Bergen, III⁸, J Proteome Res. 2008 January; 7(1): 225–233 ¹ Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, Minnesota 55905 ² Department of Mathematics and Statistics, Winona State University, Winona, MN 55987 ³ SAS Institute Inc., 100 SAS Campus Drive, Cary, NC 27513-2414 ⁴ Medical University of South Carolina, Department of Biostatistics, Bioinformatics and Epidemiology, 135 Cannon Street, Suite 303, Charleston, SC 29425 ⁵ Division of Cardiology, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, Minnesota 55905 ⁶ Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114 ⁷ Biochemistry, Molecular Biology and Pharmacology, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, Minnesota 55905 ⁸ Mayo Proteomics Research Center, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, Minnesota 55905 CORRESPONDING AUTHOR FOOTNOTE Ann L. Oberg, Mayo Clinic, Cancer Center Statistics, 200 First St SW, Rochester, MN 55905. Telephone (507)538-1556; Fax (507)266-2477; Email: oberg.ann@mayo.edu 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 case study of complex biological samples assessed using the iTRAQ™ relative labeling protocol. Keywords: Proteomics, ANOVA, iTRAQ™, Normalization, relative labeling protocol, Missing data, Gauss-Siedel, Backfitting, Fixed effects model, Mixed effects model

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