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Bioinformatics Advance Access originally published online on September 5, 2006
Bioinformatics 2006 22(22):2739-2745; doi:10.1093/bioinformatics/btl464
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

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

J. E. Eckel-Passow *, A. L. Oberg , T. M. Therneau , C. J. Mason 2, D. W. Mahoney , K. L. Johnson 2, J. E. Olson 1 and H. R. Bergen, III 2

Division of Biostatistics, Department of Health Sciences Research 200 First Street SW, Rochester, MN 55905, USA
1 Division of Epidemiology, Department of Health Sciences Research 200 First Street SW, Rochester, MN 55905, USA
2 Mayo Proteomics Research Center, Mayo Clinic 200 First Street SW, Rochester, MN 55905, USA

*To whom correspondence should be addressed.

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.

Contact: eckel{at}mayo.edu

Received on August 1, 2006; revised on August 23, 2006; accepted on August 26, 2006

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