Standardization and denoising algorithms for mass spectra to classify whole-organism bacterial specimens
Standardization and denoising algorithms for mass spectra to classify whole-organism bacterial specimens
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DOI：
10.1093/bioinformatics/bth372
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发表时间：
2004-11-22
期刊：
影响因子：
5.8
通讯作者：
Barr, JR
中科院分区：
文献类型：
--
作者：
Satten, GA;Datta, S;Barr, JR
动机：质谱技术在蛋白质组学中的应用是高通量分析的一个突破。早期的应用集中在蛋白质表达谱上，以区分各种类型的组织样本（例如正常与肿瘤）。在这里，我们的目标是使用质谱来区分细菌物种使用整个有机体样品。原始光谱类似于组织样本的光谱，引起一些相同的统计问题（例如，不均匀的基线和与较高基线相关联的较高噪声），但是噪声实质上更大。因此，需要新的预处理程序之前，这些光谱可以用于统计classification.Results：在这项研究中，我们引入了新的预处理步骤，可以与任何质谱。这些步骤包括标准化步骤和去噪步骤。每个光谱的噪声水平仅使用来自该光谱的数据来确定。只有超过由噪声水平定义的阈值的光谱特征随后用于分类。使用这种方法，我们训练随机森林程序将240个质谱分为四种细菌类型。该方法导致在训练样本和两个测试数据集分别具有240和300个光谱的预测误差为零。
Motivation: Application of mass spectrometry in proteomics is a breakthrough in high-throughput analyses. Early applications have focused on protein expression profiles to differentiate among various types of tissue samples (e.g. normal versus tumor). Here our goal is to use mass spectra to differentiate bacterial species using whole-organism samples. The raw spectra are similar to spectra of tissue samples, raising some of the same statistical issues (e.g. non-uniform baselines and higher noise associated with higher baseline), but are substantially noisier. As a result, new preprocessing procedures are required before these spectra can be used for statistical classification.Results: In this study, we introduce novel preprocessing steps that can be used with any mass spectra. These comprise a standardization step and a denoising step. The noise level for each spectrum is determined using only data from that spectrum. Only spectral features that exceed a threshold defined by the noise level are subsequently used for classification. Using this approach, we trained the Random Forest program to classify 240 mass spectra into four bacterial types. The method resulted in zero prediction errors in the training samples and in two test datasets having 240 and 300 spectra, respectively.