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

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...

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Bibliographic Details
Published inJournal 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
LanguageEnglish
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
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Summary: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.
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AUTHOR EMAIL ADDRESS Ann Oberg, oberg.ann@mayo.edu; Douglas Mahoney, mahoney@mayo.edu; Jeanette Eckel-Passow, eckelpassow.jeanette@mayo.edu; Christopher Malone, cmalone@winona.edu; Russell Wolfinger, russ.wolfinger@jmp.com; Elizabeth Hill, hille@musc.edu; Leslie Cooper, cooper.leslie@mayo.edu; Oyere Onuma, oyere.onuma@yahoo.com; Craig Spiro, spiro.craig@mayo.edu; Terry Therneau, therneau@mayo.edu; H. Robert Bergen, bergen.bob@mayo.edu
ISSN:1535-3893
1535-3907
DOI:10.1021/pr700734f