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首页> 外文会议>2011 IEEE International Conference on Bioinformatics and Biomedicine >Bayesian Alignment Model for LC-MS Data
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Bayesian Alignment Model for LC-MS Data

机译：LC-MS数据的贝叶斯比对模型

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A Bayesian alignment model (BAM) is proposed for alignment of liquid chromatography-mass spectrometry (LC-MS) data. BAM is composed of two important components: prototype function and mapping function. Estimation of both functions is crucial for the alignment result. We use Markov chain Monte Carlo (MCMC) methods for inference of model parameters. To address the trapping effect of local mode, we propose a block Metropolis-Hastings algorithm that led to better mixing behavior in updating the mapping function coefficients. We applied BAM to both simulated and real LC-MS datasets, and compared its performance with the Bayesian hierarchical curve registration model (BHCR). Performance evaluation on both simulated and real datasets shows satisfactory results in terms of correlation coefficients and ratio of overlapping peak areas.
机译：提出了贝叶斯比对模型（BAM）用于液相色谱-质谱（LC-MS）数据的比对。 BAM由两个重要组件组成：原型功能和映射功能。这两个函数的估计对于对齐结果至关重要。我们使用马尔可夫链蒙特卡罗（MCMC）方法来推断模型参数。为了解决局部模式的捕获效应，我们提出了一种Metropolis-Hastings块算法，该算法在更新映射函数系数时带来了更好的混合行为。我们将BAM应用于模拟和实际LC-MS数据集，并将其性能与贝叶斯层次曲线配准模型（BHCR）进行了比较。在模拟数据集和实际数据集上的性能评估在相关系数和重叠峰面积之比方面均显示出令人满意的结果。

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