A semiparametric modeling framework for potential biomarker discovery and the development of metabonomic profiles.
A semiparametric modeling framework for potential biomarker discovery and the development of metabonomic profiles.
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DOI：
10.1186/1471-2105-9-38
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发表时间：
2008-01-23
影响因子：
3
通讯作者：
Hill DW
中科院分区：
文献类型：
--
作者：
Ghosh S;Grant DF;Dey DK;Hill DW
生物标志物的发现是朝着疾病状态的早期诊断标准的发展迈出的重要一步。近年来，电喷雾离子化（ESI）和基质辅助激光解吸（MALDI）飞行时间（TOF）质谱已被用于蛋白质组学和代谢组学研究中的生物标志物的鉴定。这些研究产生的数据集通常非常庞大，因此需要使用复杂的统计技术来收集有用的信息。最近尝试处理这些类型的数据模型每种化合物的强度离散的位置（质荷比）聚类或通过每种化合物自己的强度分布。传统的数据处理步骤，如噪声去除，背景消除和m/z对齐，通常是分开进行，导致在最终模型中的信号的传播不令人满意。在本研究中，一种新的半参数方法已被开发出来，以区分尿代谢谱在一组创伤患者的正常人组成的对照组。从单个受试者的重复获得的数据集用于通过β分布的Dirichlet混合物开发功能谱。该功能配置文件足够灵活，以适应仪器的可变性和每个个体的固有可变性，从而同时解决系统误差的不同来源。为了解决仪器变异性，所有数据集均重复分析，这是过去大多数研究忽略的重要问题。进行不同的模型比较以选择每个受试者的最佳模型。然后进一步推荐不规则图案的窗口中的m/z值用于可能的生物标志物发现。据我们所知，这是第一次尝试模拟飞行时间质谱背后的物理过程。大多数现有技术在对这些数据进行建模时没有考虑这些物理原理。只要本文提出的基本物理原理是有效的，所提出的建模过程将适用。值得注意的是，我们目前的工作主要局限于建模方面。尽管如此，我们推荐的潜在生物标志物列表仍需要临床验证。因此，我们将我们的建模方法称为进一步工作的“框架”。
The discovery of biomarkers is an important step towards the development of criteria for early diagnosis of disease status. Recently electrospray ionization (ESI) and matrix assisted laser desorption (MALDI) time-of-flight (TOF) mass spectrometry have been used to identify biomarkers both in proteomics and metabonomics studies. Data sets generated from such studies are generally very large in size and thus require the use of sophisticated statistical techniques to glean useful information. Most recent attempts to process these types of data model each compound's intensity either discretely by positional (mass to charge ratio) clustering or through each compounds' own intensity distribution. Traditionally data processing steps such as noise removal, background elimination and m/z alignment, are generally carried out separately resulting in unsatisfactory propagation of signals in the final model. In the present study a novel semi-parametric approach has been developed to distinguish urinary metabolic profiles in a group of traumatic patients from those of a control group consisting of normal individuals. Data sets obtained from the replicates of a single subject were used to develop a functional profile through Dirichlet mixture of beta distribution. This functional profile is flexible enough to accommodate variability of the instrument and the inherent variability of each individual, thus simultaneously addressing different sources of systematic error. To address instrument variability, all data sets were analyzed in replicate, an important issue ignored by most studies in the past. Different model comparisons were performed to select the best model for each subject. The m/z values in the window of the irregular pattern are then further recommended for possible biomarker discovery. To the best of our knowledge this is the very first attempt to model the physical process behind the time-of flight mass spectrometry. Most of the state of the art techniques does not take these physical principles in consideration while modeling such data. The proposed modeling process will apply as long as the basic physical principle presented in this paper is valid. Notably we have confined our present work mostly within the modeling aspect. Nevertheless clinical validation of our recommended list of potential biomarkers will be required. Hence, we have termed our modeling approach as a "framework" for further work.
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影响因子：
1.9
作者：
Randolph, T. W.;Yasui, Y.
通讯作者：
Yasui, Y.
影响因子：
2.4
作者：
Ghosh, Samiran;Dennis, W. H.;Dey, Dipak K.
通讯作者：
Dey, Dipak K.
影响因子：
3.7
作者：
GELFAND, AE;SMITH, AFM
通讯作者：
SMITH, AFM
影响因子：
3.6
作者：
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通讯作者：
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影响因子：
5.8
作者：
Tibshirani, R;Hastie, T;Le, QT
通讯作者：
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