Journal of Proteomics & Bioinformatics
Open Access
ISSN: 0974-276X
ISSN: 0974-276X
Jeanette E. Eckel-Passow
Mayo Clinic College of Medicine,
200 First Street SW Rochester, MN 55905
Tanzania
Research Article
Bi-Linear Regression for 18O Quantification: Modeling across the Elution Profile
Author(s): Jeanette E. Eckel-Passow, Douglas W. Mahoney, Ann L. Oberg, Roman M. Zenka, Kenneth L. Johnson, K. Sreekumaran Nair, Yogish C. Kudva, H. Robert Bergen III and Terry M. TherneauJeanette E. Eckel-Passow, Douglas W. Mahoney, Ann L. Oberg, Roman M. Zenka, Kenneth L. Johnson, K. Sreekumaran Nair, Yogish C. Kudva, H. Robert Bergen III and Terry M. Therneau
Motivation: Interpreting and quantifying labeled mass-spectrometry data is complex and requires automated algorithms, particularly for large scale proteomic profiling. Here, we propose the use of bi-linear regression to quantify relative abundance across the elution profile in a unified model. The bi-linear regression model takes advantage of the fact that while peptides differ in overall abundance across the elution profile multiplicatively, the relative abundance between the mixed samples remains constant across the elution profile. We describe how to apply bi-linear regression models to 18O stable-isotope labeled data, which allows for the direct comparison of two samples simultaneously. Interpretation of model parameters is also discussed. The incorporation rate of the labeling isotope is estimated as part of the modeling process and can be used as a measure of data quality. Appli.. Read More»
DOI:
10.4172/jpb.1000158
