Statistical Analysis of Relative Labeled Mass Spectrometry Data from Complex Samples Using ANOVA

• Published in: Journal of proteome research Vol. 7; no. 1; pp. 225 - 233
• Main Authors: Oberg, Ann L, Mahoney, Douglas W, Eckel-Passow, Jeanette E, Malone, Christopher J, Wolfinger, Russell D, Hill, Elizabeth G, Cooper, Leslie T, Onuma, Oyere K, Spiro, Craig, Therneau, Terry M, Bergen, III, H. Robert
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 01.01.2008
• Subjects: Analysis of Variance; Complex Mixtures - analysis; Computer Graphics; Data Interpretation, Statistical; Isotope Labeling - methods; Isotopes; Mass Spectrometry; Proteomics - methods; relative labeling protocol; proteomics; ANOVA; normalization; iTRAQ; Gauss−Siedel; missing data; backfitting; fixed effects model; mixed effects model

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 c...