This is the MetSign project
Data analysis in metabolomics is currently a major challenge, particularly when large sample sets are analyzed. This is an MATLAB package providing a set of data preprocessing algorithms for analysis of both high resolution mass spectrometry-based metabolomics and liquid chromatography−mass spectrometry (LC−MS)-based metabolomics data.
For high resolution mass spectrometry-based metabolomics data, MetSign provides a suite of bioinformatics tools to perform raw data deconvolution, metabolite putative assignment, peak list alignment, normalization, statistical significance tests, unsupervised pattern recognition, and time course analysis. MetSign uses a modular design and an interactive visual data mining approach to enable efficient extraction of useful patterns from data sets. Analysis steps, designed as containers, are presented with a wizard for the user to follow analyses. Each analysis step might contain multiple analysis procedures and/or methods and serves as a pausing point where users can interact with the system to review the results, to shape the next steps, and to return to previous steps to repeat them with different methods or parameter settings. MetSign has also been successfully applied to investigate the regulation and time course trajectory of metabolites.
For LC−MS-based metabolomics data, MetSign provides a set of data preprocessing algorithms for peak detection and peak list alignment. For spectrum deconvolution, peak picking is achieved at the selected ion chromatogram (XIC) level. To estimate and remove the noise in XICs, each XIC is first segmented into several peak groups based on the continuity of scan number, and the noise level is estimated by all the XIC signals, except the regions potentially with presence of metabolite ion peaks. After removing noise, the peaks of molecular ions are detected using both the first and the second derivatives, followed by an efficient exponentially modified Gaussian-based (EMG) peak deconvolution method for peak fitting. A two-stage alignment algorithm is also developed, where the retention times of all peaks are first transferred into the z-score domain and the peaks are aligned based on the measure of their mixture scores after retention time correction using a partial linear regression.
Wei, X.; Sun, W.; Shi, X.; Koo, I.; Wang, B.; Zhang, J.; Yin, X.; Tang, Y.; Bogdanov, B.; Kim, S. H.; Zhou, Z.; McClain, C. J.; Zhang, X. MetSign: A computational platform for high-resolution mass spectrometry-based metabolomics. Anal. Chem. 2011, 83, 7668-7675.
Wei, X.; Shi, X.; Kim, S. H.; Zhang, L.; Patrick, J. S.; Binkley, J.; McClain, C.; Zhang, X. Data Preprocessing Method for Liquid Chromatography–Mass Spectrometry Based Metabolomics. Anal. Chem. 2012, 84, 7963-7971.