Accounting for isotopic clustering in Fourier transform mass spectrometry data analysis for clinical diagnostic studies

IF 0.9 4区 数学 Q3 Mathematics
A. Kakourou, W. Vach, S. Nicolardi, Y. V. D. van der Burgt, B. Mertens
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引用次数: 4

Abstract

Abstract Mass spectrometry based clinical proteomics has emerged as a powerful tool for high-throughput protein profiling and biomarker discovery. Recent improvements in mass spectrometry technology have boosted the potential of proteomic studies in biomedical research. However, the complexity of the proteomic expression introduces new statistical challenges in summarizing and analyzing the acquired data. Statistical methods for optimally processing proteomic data are currently a growing field of research. In this paper we present simple, yet appropriate methods to preprocess, summarize and analyze high-throughput MALDI-FTICR mass spectrometry data, collected in a case-control fashion, while dealing with the statistical challenges that accompany such data. The known statistical properties of the isotopic distribution of the peptide molecules are used to preprocess the spectra and translate the proteomic expression into a condensed data set. Information on either the intensity level or the shape of the identified isotopic clusters is used to derive summary measures on which diagnostic rules for disease status allocation will be based. Results indicate that both the shape of the identified isotopic clusters and the overall intensity level carry information on the class outcome and can be used to predict the presence or absence of the disease.
用于临床诊断研究的傅里叶变换质谱数据分析中的同位素聚类
基于质谱的临床蛋白质组学已经成为高通量蛋白质分析和生物标志物发现的有力工具。质谱技术的最新改进提高了生物医学研究中蛋白质组学研究的潜力。然而,蛋白质组学表达的复杂性为总结和分析获得的数据带来了新的统计挑战。最佳处理蛋白质组学数据的统计方法目前是一个不断发展的研究领域。在本文中,我们提出了简单而适当的方法来预处理,总结和分析高通量MALDI-FTICR质谱数据,以病例对照的方式收集,同时处理伴随这些数据的统计挑战。利用已知的肽分子同位素分布的统计特性对光谱进行预处理,并将蛋白质组学表达转化为浓缩的数据集。关于已确定的同位素簇的强度水平或形状的信息被用于得出疾病状态分配诊断规则所依据的汇总度量。结果表明,鉴定的同位素簇的形状和总体强度水平都携带了分类结果的信息,可用于预测疾病的存在与否。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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