Wang Pei, Tang Hua, Zhang Heidi, Whiteaker Jeffrey, Paulovich Amanda G, Mcintosh Martin
Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
Pac Symp Biocomput. 2006:315-26.
We propose a two-step normalization procedure for high-throughput mass spectrometry (MS) data, which is a necessary step in biomarker clustering or classification. First, a global normalization step is used to remove sources of systematic variation between MS profiles due to, for instance, varying amounts of sample degradation over time. A probability model is then used to investigate the intensity-dependent missing events and provides possible substitutions for the missing values. We illustrate the performance of the method with a LC-MS data set of synthetic protein mixtures.
我们提出了一种针对高通量质谱（MS）数据的两步归一化程序，这是生物标志物聚类或分类中的必要步骤。首先，使用全局归一化步骤来消除MS谱之间系统变异的来源，例如，由于样品随时间的降解量不同。然后使用概率模型来研究强度依赖性缺失事件，并为缺失值提供可能的替代值。我们用合成蛋白质混合物的液相色谱-质谱数据集说明了该方法的性能。
