ROIMCR: a powerful analysis strategy for LC-MS metabolomic datasets

Abstract

Background The analysis of LC-MS metabolomic datasets appears to be a challenging task in a wide range of disciplines since it demands the highly extensive processing of a vast amount of data. Different LC-MS data analysis packages have been developed in the last few years to facilitate this analysis. However, most of these strategies involve chromatographic alignment and peak shaping and often associate each “feature” (i.e., chromatographic peak) with a unique m/z measurement. Thus, the development of an alternative data analysis strategy that is applicable to most types of MS datasets and properly addresses these issues is still a challenge in the metabolomics field. Results Here, we present an alternative approach called ROIMCR to: i) filter and compress massive LC-MS datasets while transforming their original structure into a data matrix of features without losing relevant information through the search of regions of interest (ROIs) in the m/z domain and ii) resolve compressed data to identify their contributing pure components without previous alignment or peak shaping by applying a Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis. In this study, the basics of the ROIMCR method are presented in detail and a detailed description of its implementation is also provided. Data were analyzed using the MATLAB (The MathWorks, Inc., www.mathworks.com ) programming and computing environment. The application of the ROIMCR methodology is described in detail, with an example of LC-MS data generated in a lipidomic study and with other examples of recent applications. Conclusions The methodology presented here combines the benefits of data filtering and compression based on the searching of ROI features, without the loss of spectral accuracy. The method has the benefits of the application of the powerful MCR-ALS data resolution method without the necessity of performing chromatographic peak alignment or modelling. The presented method is a powerful alternative to other existing data analysis approaches that do not use the MCR-ALS method to resolve LC-MS data. The ROIMCR method also represents an improved strategy compared to the direct applications of the MCR-ALS method that use less-powerful data compression strategies such as binning and windowing. Overall, the strategy presented here confirms the usefulness of the ROIMCR chemometrics method for analyzing LC-MS untargeted metabolomics data. Electronic supplementary material The online version of this article (10.1186/s12859-019-2848-8) contains supplementary material, which is available to authorized users.

Sequential 3D OrbiSIMS and LESA-MS/MS-based metabolomics for prediction of brain tumor relapse from sample-limited primary tissue archives

“…For LESA-MS data, files were first converted to .mzXML (32-bit) using ProteoWizard 3.0.1908 (51). Ions were selected from the spectra using the mspeaks function and ions were aligned within 5 ppm m/z tolerance (15,52). Ions with >20% missing values across all samples were removed (53).…”

GM2-GM3 gangliosides ratio is dependent on GRP94 through down-regulation of GM2-AP cofactor in brain metastasis cells

“…These ROI values are also represented by a minimum of consecutive data points in the chromatogram and have a particular m/z deviation error. In our case, ROI values have been obtained from the analysis of 18 samples (6 independent samples for the 3 cell lines) simultaneously, using an in-house written MATLAB routine 35 . The threshold value was set to 1% of the MS maximum intensity, the number of occurrences to 5 and the error in m/z values to 0.025.…”

“…Software used in this work includes MassLynx V 4.1 (Waters) for raw UPLC-TOF data analysis and cdf format conversion. For matrix data processing and statistical analyses, home-made routines for ROI 35 Bioinformatics Toolbox (The Mathworks Inc.), Statistics ToolBox (The Mathworks Inc.), PLS-Toolbox (Eigenvector Research Inc.) and MCR-ALS Toolbox 40 were used in MATLAB environment (release 2016b, The Mathworks Inc.).…”

Recent applications of chemometrics in one‐ and two‐dimensional chromatography

“…The ROI strategy allows for compression of data, while maintaining spectral resolution. The approach is often applied in combination with deconvolution strategies, such as multivariate curve resolution-alternating least squares (MCR-ALS, see Sections 2.2.2 and 3.4), as in this case peak alignment is not required [89]. Such an ROI-MCR approach has been applied by Navarro-Reig et al [90] in combination with LC×LC-HRMS for the identification of metabolites in the rice metabolome.…”