MathDAMP: a package for differential analysis of metabolite profiles.
MathDAMP: a package for differential analysis of metabolite profiles.
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DOI：
10.1186/1471-2105-7-530
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发表时间：
2006-12-13
影响因子：
3
通讯作者：
Tomita M
中科院分区：
文献类型：
--
作者：
Baran R;Kochi H;Saito N;Suematsu M;Soga T;Nishioka T;Robert M;Tomita M
随着代谢组学作为功能和生物标志物发现的强大工具的出现，复杂代谢物谱之间的特定差异的识别正在成为数据分析流程中的主要挑战。考虑到数据集的大小、复杂性以及联用质谱方法分析的样品之间迁移（洗脱/保留）时间的常见变化，这项任务仍然很困难。我们提出了 Mathematica (Wolfram Research, Inc.) 软件包 MathDAMP（用于代谢物概况差异分析的 Mathematica 软件包），它通过逐个数据点对所有相应信号强度应用算术运算，突出显示通过联用质谱方法获取的原始数据集之间的差异。因此，峰识别和积分被绕过，结果以图形方式显示。为了便于直接比较，原始数据集会根据迁移时间和信号强度自动进行预处理和标准化。动态规划和全局优化的组合用于沿迁移时间维度对齐数据集。使用密度图（轴代表迁移时间和 m/z 值，而峰值显示为颜色编码点）来可视化处理后的数据集以及它们之间直接比较的结果，提供直观的整体视图。可以应用各种形式的比较和统计测试来突出细微的差异。可以按照重要性降序生成与任何结果的候选差异附近相对应的重叠电泳图（色谱图），以进行视觉确认。此外，标准库表（已知化合物的 m/z 值和迁移时间列表）可以对齐并覆盖在图上，以便更轻松地识别代谢物。我们的工具有助于根据各种标准以自动化方式可视化和识别复杂代谢物谱之间的差异，并且对于数据驱动的生物标志物和功能基因组学发现非常有用。
With the advent of metabolomics as a powerful tool for both functional and biomarker discovery, the identification of specific differences between complex metabolite profiles is becoming a major challenge in the data analysis pipeline. The task remains difficult, given the datasets' size, complexity, and common shifts in migration (elution/retention) times between samples analyzed by hyphenated mass spectrometry methods. We present a Mathematica (Wolfram Research, Inc.) package MathDAMP (Mathematica package for Differential Analysis of Metabolite Profiles), which highlights differences between raw datasets acquired by hyphenated mass spectrometry methods by applying arithmetic operations to all corresponding signal intensities on a datapoint-by-datapoint basis. Peak identification and integration is thus bypassed and the results are displayed graphically. To facilitate direct comparisons, the raw datasets are automatically preprocessed and normalized in terms of both migration times and signal intensities. A combination of dynamic programming and global optimization is used for the alignment of the datasets along the migration time dimension. The processed datasets and the results of direct comparisons between them are visualized using density plots (axes represent migration time and m/z values while peaks appear as color-coded spots) providing an intuitive overall view. Various forms of comparisons and statistical tests can be applied to highlight subtle differences. Overlaid electropherograms (chromatograms) corresponding to the vicinities of the candidate differences from any result may be generated in a descending order of significance for visual confirmation. Additionally, a standard library table (a list of m/z values and migration times for known compounds) may be aligned and overlaid on the plots to allow easier identification of metabolites. Our tool facilitates the visualization and identification of differences between complex metabolite profiles according to various criteria in an automated fashion and is useful for data-driven discovery of biomarkers and functional genomics.
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影响因子：
7.4
作者：
Pierce, KM;Wood, LF;Synovec, RE
通讯作者：
Synovec, RE
影响因子：
7.4
作者：
Broeckling, Corey D.;Reddy, Indira R.;Sumner, Lloyd W.
通讯作者：
Sumner, Lloyd W.
影响因子：
4.8
作者：
Soga, Tomoyoshi;Baran, Richard;Tomita, Masaru
通讯作者：
Tomita, Masaru
DOI：
10.1016/j.jasms.2003.12.011
发表时间：
2004-04-01
影响因子：
3.2
作者：
Katz, JE;Dumlao, DS;Hau, J
通讯作者：
Hau, J
影响因子：
4.4
作者：
Saito, Natsumi;Robert, Martin;Tomita, Masaru
通讯作者：
Tomita, Masaru