Bayesian Analysis of iTRAQ Data with Nonrandom Missingness: Identification of Differentially Expressed Proteins |
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Authors: | Ruiyan Luo Christopher M Colangelo William C Sessa Hongyu Zhao |
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Institution: | Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520, USA. |
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Abstract: | iTRAQ (isobaric Tags for Relative and Absolute Quantitation) is a technique that allows simultaneous quantitation of proteins
in multiple samples. In this paper, we describe a Bayesian hierarchical model-based method to infer the relative protein expression
levels and hence to identify differentially expressed proteins from iTRAQ data. Our model assumes that the measured peptide
intensities are affected by both protein expression levels and peptide specific effects. The values of these two effects across
experiments are modeled as random effects. The nonrandom missingness of peptide data is modeled with a logistic regression
which relates the missingness probability for a peptide with the expression level of the protein that produces this peptide.
We propose a Markov chain Monte Carlo method for the inference of model parameters, including the relative expression levels
across samples. Our simulation results suggest that the estimates of relative protein expression levels based on the MCMC
samples have smaller bias than those estimated from ANOVA models or fold changes. We apply our method to an iTRAQ dataset
studying the roles of Caveolae for postnatal cardiovascular function. |
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