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首页> 外文期刊>Statistics in Biosciences >Bayesian Analysis of iTRAQ Data with Nonrandom Missingness: Identification of Differentially Expressed Proteins
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Bayesian Analysis of iTRAQ Data with Nonrandom Missingness: Identification of Differentially Expressed Proteins

机译：具有非随机缺失的iTRAQ数据的贝叶斯分析：差异表达蛋白的鉴定

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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 sampleshave 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.
机译：iTRAQ（用于相对定量和绝对定量的等压标记）是一种允许同时定量多个样品中蛋白质的技术。在本文中，我们描述了一种基于贝叶斯层次模型的方法来推断相对蛋白质表达水平，从而从iTRAQ数据中识别差异表达的蛋白质。我们的模型假设所测量的肽强度受蛋白质表达水平和肽特异性作用的影响。在整个实验中，这两种效应的值被建模为随机效应。肽数据的非随机缺失用逻辑回归建模，该回归将肽的缺失概率与产生该肽的蛋白质的表达水平相关联。我们提出了一种马尔可夫链蒙特卡罗方法来推断模型参数，包括样本之间的相对表达水平。我们的模拟结果表明，基于MCMC样品的相对蛋白质表达水平的估计值比从ANOVA模型或倍数变化估计的值具有较小的偏差。我们将我们的方法应用于研究小窝对产后心血管功能的作用的iTRAQ数据集。

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