EFFECT OF MALDI-TOF SPECTRUM PREPROCESSING METHOD ON SERUM BIOMARKER DISCOVERY

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 我们收集了54名确诊为乳腺癌的患者和443名多年未患病的患者的血清样本。样本在手术前和手术后7个月每隔一个月从癌症患者身上收集。在不同时间点抽取对照组患者的血样。所有抽血都在相同的设施中按照相同的方案进行。从血清样本中提取白蛋白，并捕获附着在白蛋白上的蛋白质。对这些提取的蛋白质进行了高分辨率的MALDI-TOF质谱分析。对所有样品进行技术重复，得到1750个总光谱，每个光谱包含大约500,000个数据点。来自所得光谱的峰被用来建立样本的癌症与正常分类器。参见：利用峰值对指示翻译后修饰的卵巢癌和CTCL血清样本的蛋白质组学模式。蛋白质组学。2007年11月；7(22)：4045-52。光谱的预处理对于正确识别样品间的差示峰起着重要作用。目前还没有公认的光谱前处理的标准方法。在这项研究中，我们建议将两种不同的预处理工作流程应用于整个数据集，并构建一个分类器来比较预处理方法对最终结果的影响。这两个预处理工作流都是在MatLab中实现的，并可供一般公众使用。尽管MatLab可能不是最有效的实现，但在这种情况下，对方法的即时访问超过了与MatLab相关的小开销。我们将测试的两个工作流程是：1.德克萨斯农工统计局的PrepMS：http://www.stat.tamu.edu/~yuliya/prepMS.html 2.MatLab演示质谱学前处理工作流程：http://www.mathworks.com/products/demos/shipping/bioinfo/mspreprodemo.html每个工作流程的去噪、归一化和峰值检测方法不同。在成功完成这项小型比较研究后，我们将测试其他已发表的预处理方法。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. We have collected serum samples from 54 patients diagnosed with breast cancer and 443 patients who have been disease free for several years. Samples were collected from cancer patients before surgery and at month intervals for seven months after surgery. Blood draws from control set of patients was drawn at different time points. All blood draws were performed in the same facility under the same protocol. Albumin was extracted from the serum samples and proteins attached to the albumin were captured. High resolution orthogonal MALDI-TOF mass spectrometry was performed on these extacted proteins. Technical repeats were performed for all samples yielding 1750 total spectra containing approximately 500,000 data points each. Peaks from the resulting spectra were used to build a cancer vs normal classifier for the samples. See: Proteomic patterns for classification of ovarian cancer and CTCL serum samples utilizing peak pairs indicative of post-translational modifications. Proteomics. 2007 Nov;7(22):4045-52. Preprocessing of the spectra plays an important role in correctly identifying differential peaks among samples. There is no currently accepted standard method of preprocessing spectra. In this study we propose to apply two different preprocessing workflows to the entire data set and build a classifier to compare the effect of the preprocessing method on final outcome. Both preprocessing workflows are implemented in Matlab and are available to the general public. Although Matlab may not be the most efficient implementation, ready access to the methods outweighs the small overhead associated with Matlab in this case. The two workflows we will test are: 1. PrepMS, from Texas A&M Department of Statistics: http://www.stat.tamu.edu/~yuliya/prepMS.html 2. Matlab demo mas spectrometry preprocessing workflow: http://www.mathworks.com/products/demos/shipping/bioinfo/mspreprodemo.html The method of denoising, normalization and peak detection differ in each workflow. Upon successful completion of this small comparison study we will test other published methods for preprocessing.