Regression analysis for comparing protein samples with 16O/ 18O stable-isotope labeled mass spectrometry

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

Motivation: Using stable isotopes in global proteome scans, labeled molecules from one sample are pooled with unlabeled molecules from another sample and subsequently subjected to mass-spectral analysis. Stable-isotope methodologies make use of the fact that identical molecules of different stable-isotope compositions are differentiated in a mass spectrometer and are represented in a mass spectrum as distinct isotopic clusters with a known mass shift. We describe two multivariable linear regression models for 16O/18O stable-isotope labeled data that jointly model pairs of resolved isotopic clusters from the same peptide and quantify the abundance present in each of the two biological samples while concurrently accounting for peptide-specific incorporation rates of the heavy isotope. The abundance measure for each peptide from the two biological samples is then used in down-stream statistical analyses, e.g. differential expression analysis. Because the multivariable regression models are able to correct for the abundance of the labeled peptide that appear as an unlabeled peptide due to the inability to exchange the natural C-terminal oxygen for the heavy isotope, they are particularly advantageous for a two-step digestion/labeling procedure. We discuss how estimates from the regression model are used to quantify the variability of the estimated abundance measures for the paired samples. Although discussed in the context of 16O/18O stable-isotope labeled data, the multivariable regression models are generalizable to other stable-isotope labeled technologies.

Original languageEnglish (US)
Pages (from-to)2739-2745
Number of pages7
JournalBioinformatics
Volume22
Issue number22
DOIs
StatePublished - Nov 15 2006

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Mass Spectrometry
Regression Analysis
Regression analysis
Isotopes
Mass spectrometry
Peptides
Proteins
Protein
Regression Model
Molecules
Quantify
Linear Models
Oxygen Isotopes
Differential Expression
Linear Regression Model
Spectral Analysis
Spectrometer
Proteome
Labeling
Oxygen

ASJC Scopus subject areas

  • Clinical Biochemistry
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

Regression analysis for comparing protein samples with 16O/ 18O stable-isotope labeled mass spectrometry. / Eckel-Passow, Jeanette E; Oberg, Ann L; Therneau, Terry M; Mason, C. J.; Mahoney, D. W.; Johnson, K. L.; Olson, Janet E; Bergen, Harold Robert (Bob) III.

In: Bioinformatics, Vol. 22, No. 22, 15.11.2006, p. 2739-2745.

Research output: Contribution to journalArticle

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