Wednesday, October 14, 2009
3:00 - 4:00
Room 457 Blocker
Department of Statistics
Protein quantification in label-free LC-MS experiments
The goal of many LC-MS proteomic investigations is to quantify and compare the abundances 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 abundances to obtain a single protein-level quantify 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 view 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 dataset that the proposed models improve the sensitivity and specificity of testing, improve the accuracy of patient-specific protein quantifications, and are more robust to missing data.
T. Clough, M. Key, I. Ott, S. Ragg, G. Schadow, O. Vitek. “Protein quantification in label-free LC-MS experiments”, Journal of Proteome Research, in press, 2009.