JSM 2011 Online Program

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Abstract Details

Activity Number: 507
Type: Contributed
Date/Time: Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #302693
Title: Adaptive Thresholding at the Peptide Level Improves Estimate of Relative Protein Abundances from LC-MS Experiments
Author(s): Yuping Zhang*+ and Zhengqing Ouyang and Wei-Jun Qian and Richard D. Smith and Wing Hung Wong and Ronald W. Davis
Companies: Stanford University and Stanford University and Pacific Northwest National Laboratory and Pacific Northwest National Laboratory and Stanford University and Stanford University
Address: , , ,
Keywords: proteomics ; LC-MS ; hierarchical mixed model
Abstract:

Quantitative comparison of the relative abundances of proteins from different biological conditions is an important aspect for LC-MS based proteomics. Because LC-MS experiments perform measurements at the peptide level, it is often challenging to obtain relative protein abundances based on these peptide level measurements due to the issues of missing data, varying numbers of peptides per protein, as well as potential multiple isoforms per protein. Here we report a method termed as protein expression through adaptive thresholding (PEAT), which utilizes a two-stage hierarchical additive mixed model for peptide-level modeling of LC-MS data, and adaptively select peptides for protein-level abundance estimation and inference. Tests on simulation data, spiking-in data and biological data from human plasma samples following burn injury demonstrate that our method provides a more accurate estimation of relative protein abundances as well as more powerful detection of differentially expressed proteins than current existing methods.


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