Statistical design of quantitative mass spectrometry-based proteomic experiments

Ann L. Oberg, Olga Vitek

Research output: Contribution to journalReview article

158 Scopus citations

Abstract

We review the fundamental principles of statistical experimental design, and their application to quantitative mass spectrometry-based proteomics. We focus on class comparison using Analysis of Variance (ANOVA), and discuss how randomization, replication and blocking help avoid systematic biases due to the experimental procedure, and help optimize our ability to detect true quantitative changes between groups. We also discuss the issues of pooling multiple biological specimens for a single mass analysis, and calculation of the number of replicates in a future study. When applicable, we emphasize the parallels between designing quantitative proteomic experiments and experiments with gene expression microarrays, and give examples from that area of research. We illustrate the discussion using theoretical considerations, and using real-data examples of profiling of disease.

Original languageEnglish (US)
Pages (from-to)2144-2156
Number of pages13
JournalJournal of Proteome Research
Volume8
Issue number5
DOIs
StatePublished - May 1 2009

Keywords

  • Analysis of Variance
  • Blocking
  • Mixed models
  • Pooling
  • Quantitative proteomics
  • Randomization
  • Replication
  • Sample size
  • Statistical design of experiments

ASJC Scopus subject areas

  • Biochemistry
  • Chemistry(all)

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