MathDAMP and TriDAMP

Category Metabolomics/Metabonomics>Metabolic Profiling/Analysis Systems/Tools

Abstract MathDAMP (Mathematica package for Differential Analysis of Metabolite Profiles) is a software tool that can be used for the differential analysis of metabolite profiles.

MathDAMP highlights differences between raw datasets acquired by hyphenated mass spectrometry (MS) 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 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 top of the plots to allow easier identification of metabolites.

This 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.

MathDAMP process - Raw Data - Normalization - Results --

When highlighting differences between metabolite profiles with MathDAMP, arithmetic operations are applied to all corresponding signal intensities from raw datasets on a datapoint-by-datapoint basis.

The datasets have to be normalized in order to allow the direct comparison. For this purpose, a representative set of peaks is picked from every dataset.

The ‘peak sets’ sole purpose is alignment, and they do Not have to contain all the peaks from the datasets and may also contain erroneous peak picks as well.

Parameters of the time shift function [mathematical function(s) to model the retention/migration time shifts between samples] are found using a combination of dynamic programming and global optimization.

The normalization procedure is able to reliably find the optimal parameters even if the peak sets contain a small number of corresponding peaks.

Various types of differences can be highlighted between the normalized datasets.

These include a simple comparison of two (2) datasets, identification of outliers within multiple datasets, comparison of two (2) groups of replicate datasets, and the comparison of multiple groups of replicate datasets.

The results are all visualized using density plots.

Overlaid chromatograms/electropherograms in the vicinities of the most significant differences can be plotted as well. The plots may also be annotated using a standard compound library to allow easier identification of the peaks.

TriDAMP --

TriDAMP is a MathDAMP (see above...) extension for the visualization of three-way comparisons between metabolite profiles. An intuitive color-coding process is used to represent the three-way differences on density plots.

Three-way comparison of metabolite profiles is performed by calculating the color specifications representing the three-way difference for all corresponding datapoints.

MathDAMP functionality is used to normalize the datasets and the results are displayed on density plots.

To calculate the color specifications for a three-way comparison, the three compared values are assigned characteristic hues (red, green, and blue) from a circular hue range [Hue, Saturation, Brightness (HSB) color model].

From the three compared values, the two most distant are found -- as measured by the absolute value of their difference.

A color gradient determined by the characteristic hues of the two most distant values is then selected. The hue specifier for the color representing the three-way difference is then selected from this gradient according to the relative position of the third value between the two most distant values.

The resulting hue is red, if the second and third value are identical and the first value is different (green if the first and third values are identical and the second value is different; yellow if the third value lies half-way between the first and second values, etc.).

The saturation specifier for the color representing the three-way difference is calculated according to the extent of the difference between the most distant values and the brightness specifier is set to 1.

For a more detailed description of the color-coding procedure please refer to: Baran, R. et al. (2007) Visualization of three-way comparisons of omics data. BMC Bioinformatics 8:72

Overlaid chromatograms corresponding to the most significant differences from the three-way comparisons can be generated in a ranked order.

System Requirements

MathDAMP requires Mathematica to run.

TriDAMP requires MathDAMP (freely available) and Mathematica to run.


Manufacturer Web Site MathDAMP


Price Contact manufacturer.

G6G Abstract Number 20668

G6G Manufacturer Number 104172