Sample classification from protein mass spectrometry, by 'peak probability contrasts'
Sample classification from protein mass spectrometry, by 'peak probability contrasts'
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DOI：
10.1093/bioinformatics/bth357
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发表时间：
2004-11-22
期刊：
影响因子：
5.8
通讯作者：
Le, QT
中科院分区：
文献类型：
--
作者：
Tibshirani, R;Hastie, T;Le, QT
动机：早期癌症检测一直是实体肿瘤学的主要研究重点。理论上，早期肿瘤检测可以导致较低阶段的肿瘤，更多的可治疗疾病，并最终提高治愈率，减少治疗相关的发病率。蛋白质质谱是一种潜在的强有力的早期癌症检测工具。我们提出了一种新的方法，从蛋白质质谱数据的样本分类。当应用于患病和健康患者的光谱时，“峰值概率对比”技术提供了光谱中所有共同峰值的列表、它们的统计显著性和它们在区分两组中的相对重要性。我们说明了从一项研究卵巢cancer.Results的矩阵辅助激光解吸和电离质谱数据的方法：相比其他统计方法的类prediction，峰值概率对比方法执行以及或优于几种方法，需要的全光谱，而不仅仅是标记的峰。它也更具有生物学解释性。峰值概率对比法是一种潜在的有用的工具，从蛋白质质谱数据的样品分类。
Motivation: Early cancer detection has always been a major research focus in solid tumor oncology. Early tumor detection can theoretically result in lower stage tumors, more treatable diseases and ultimately higher cure rates with less treatment-related morbidities. Protein mass spectrometry is a potentially powerful tool for early cancer detection.We propose a novel method for sample classification from protein mass spectrometry data. When applied to spectra from both diseased and healthy patients, the 'peak probability contrast' technique provides a list of all common peaks among the spectra, their statistical significance and their relative importance in discriminating between the two groups. We illustrate the method on matrix-assisted laser desorption and ionization mass spectrometry data from a study of ovarian cancers.Results: Compared to other statistical approaches for class prediction, the peak probability contrast method performs as well or better than several methods that require the full spectra, rather than just labelled peaks. It is also much more interpretable biologically. The peak probability contrast method is a potentially useful tool for sample classification from protein mass spectrometry data.