MathDAMP: a package for differential analysis of metabolite profiles - PubMed (original) (raw)

MathDAMP: a package for differential analysis of metabolite profiles

Richard Baran et al. BMC Bioinformatics. 2006.

Abstract

Background: 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.

Results: 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.

Conclusion: 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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Figures

Figure 1

Figure 1

Peak picking and migration time alignment. (a) Visualization of the position of peaks picked from cation datasets acquired by CE-TOFMS. The data originates from a previous analysis of mouse liver extracts after treatment with acetaminophen [13]. (b) The peak sets were aligned to the peak set from Sample 1 using the alignment procedure described in the main text. The function derived by Reijenga et al. [14] for the normalization of migration times in CE was used as the time shift function.

Figure 2

Figure 2

Comparison of two groups of replicate datasets. Visualization of an absolute × relative difference result between the averages of two groups of replicate cation datasets (n = 4). The result was further filtered as described in the main text with a _t_-score threshold of 3.71 (corresponding p = 0.01 when comparing two groups of four replicate values). The initial _t_-score dataset was smoothed by applying a moving average filter (window size 9) prior to filtering the absolute × relative result. Red color indicates signals with higher levels in Set 2, blue color indicates signals with lower levels in Set 2. The underlying datasets originate from previous work [13].

Figure 3

Figure 3

Visualization of candidate differences as extracted ion electropherograms. Overlaid electropherograms from aligned and normalized datasets corresponding to the vicinities of the 20 most significant differences in the dataset shown in Figure 2. The underlying datasets originate from previous work [13].

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