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

Ann L. Oberg, Douglas W. Mahoney, Jeanette E. Eckel-Passow, Christopher J. Malone, Russell D. Wolfinger, Elizabeth G. Hill, Leslie T. Cooper, Oyere K. Onuma, Craig Spiro, Terry M. Therneau, H. Robert Bergen

Research output: Contribution to journalArticle

112 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)225-233
Number of pages9
JournalJournal of Proteome Research
Volume7
Issue number1
DOIs
StatePublished - Jan 2008

Keywords

  • ANOVA
  • Backfitting
  • Fixed effects model
  • Gauss-Siedel
  • Missing data
  • Mixed effects model
  • Normalization
  • Proteomics
  • Relative labeling protocol
  • iTRAQ

ASJC Scopus subject areas

  • Biochemistry
  • Chemistry(all)

Fingerprint Dive into the research topics of 'Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA'. Together they form a unique fingerprint.

  • Cite this