Protein quantification in label-free LC-MS experiments

Timothy Clough, Melissa Key, Ilka Ott, Susanne Ragg, Gunther Schadow, Olga Vitek

Research output: Contribution to journalArticle

62 Scopus citations

Abstract

The goal of many LC-MS proteomic investigations is to quantify and compare the abundance of proteins in complex biological mixtures. However, the output of an LC-MS experiment is not a list of proteins, but a list of quantified spectral features. To make protein-level conclusions, researchers typically apply ad hoc rules, or take an average of feature abundance to obtain a single protein-level quantity for each sample. We argue that these two approaches are inadequate. We discuss two statistical models, namely, fixed and mixed effects Analysis of Variance (ANOVA), which views individual features as replicate measurements of a protein's abundance, and explicitly account for this redundancy. We demonstrate, using a spike-in and a clinical data set, that the proposed models improve the sensitivity and specificity of testing, improve the accuracy of patient-specific protein quantifications, and are more robust in the presence of missing data.

Original languageEnglish (US)
Pages (from-to)5275-5284
Number of pages10
JournalJournal of Proteome Research
Volume8
Issue number11
DOIs
StatePublished - Nov 6 2009

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Keywords

  • Analysis of variance
  • LC-MS
  • Missing data
  • Mixed models
  • Protein quantification
  • Quantitative proteomics

ASJC Scopus subject areas

  • Biochemistry
  • Chemistry(all)

Cite this

Clough, T., Key, M., Ott, I., Ragg, S., Schadow, G., & Vitek, O. (2009). Protein quantification in label-free LC-MS experiments. Journal of Proteome Research, 8(11), 5275-5284. https://doi.org/10.1021/pr900610q