Abstract
Mass spectrometry has become one of the methods of choice in the analytical field due to its high sensitivity and selectivity to retrieve structural information allowing the univocal identification of compounds. From a chemometric point of view, mass spectrometry generates challenging datasets because of their large size with thousands of mass-to-charge values and with a high inherent complexity associated with the multicomponent mixtures analysed. These unresolved challenges have brought about the development of various computational approaches to enable the pre-processing of these mass spectrometry datasets. Here, we present the MSroi software in an attempt to provide a one-for-all solution in the compression and pre-processing of mass spectrometry datasets. MSroi allows the analysis of a variety of mass spectrometry datasets obtained using different acquisition approaches such as direct infusion, hyphenated to a separation technique or imaging. In all these cases, MSroi produces a highly compressed data table containing the measured intensities for relevant mass to charge values at each considered retention time or pixel. This output can be used as input for the feature detection step in, for instance, the ROIMCR approach, or used independently for subsequent multivariate chemometric analysis.
Highlights
Chemometric analysis of mass spectrometry (MS) data remains a daunting challenge at present [1,2]
These data analysis and software tools tend to be specific for a particular type of MS data, such as from direct infusion mass spectrometry (DIMS) data, MS imaging or MS coupled with a separation technique [4,5,6]
We present the MSroi application that focuses on this compression and pre-processing of MS data which can be useful for its further analysis using, for instance, the ROIMCR approach
Summary
Chemometric analysis of mass spectrometry (MS) data remains a daunting challenge at present [1,2]. The scientific community (with researchers from different fields such as analytical chemistry or bioinformatics) has developed various data analysis strategies to deal with these MS datasets both from instrumentation vendor companies and from independent research groups [3] These data analysis and software tools tend to be specific for a particular type of MS data, such as from direct infusion mass spectrometry (DIMS) data, MS imaging or MS coupled with a separation technique (for instance, GC or LC) [4,5,6]. The most common outcome after the multistep pipeline is the list of detected peaks (features) defined by their m/z values, intensities and retention times or pixels This output data matrix provides information regarding the abundance of each feature in each sample that can be further processed (for instance, applying classical methods for normalisation, correction of batch effects or multivariate analysis)
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