SigSel: A MATLAB package for the pre and post-treatment of high-resolution mass spectrometry signals using the ROIMCR methodology
The Regions of Interest Multivariate curve Resolution (ROIMCR) methodology has gained significance for analyzing mass spectrometry data. The new SigSel package improves the ROIMCR methodology by providing a filtering step to reduce computational costs and to identify chemical compounds giving low-in...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/308772 |
| Acceso en línea: | http://hdl.handle.net/10261/308772 https://api.elsevier.com/content/abstract/scopus_id/85154544980 |
| Access Level: | acceso abierto |
| Palabra clave: | Quantitative analysis Chemical compound identification Mass spectrometry Metabolomics Non-target analysis http://metadata.un.org/sdg/6 Ensure availability and sustainable management of water and sanitation for all |
| Sumario: | The Regions of Interest Multivariate curve Resolution (ROIMCR) methodology has gained significance for analyzing mass spectrometry data. The new SigSel package improves the ROIMCR methodology by providing a filtering step to reduce computational costs and to identify chemical compounds giving low-intensity signals. SigSel allows the visualization and assessment of ROIMCR results and filters out components resolved as interferences and background noise. This improves the analysis of complex mixtures and facilitates the identification of chemical compounds for statistical or chemometrics analysis. SigSel has been tested using metabolomics samples of mussels exposed to the sulfamethoxazole antibiotic. It begins by analyzing the data according to their charge state, eliminating signals considered background noise, and reducing the size of the datasets. In the ROIMCR analysis, the resolution of 30 ROIMCR components was achieved. After evaluating these components, 24 were ultimately selected explaining 99.05% of the total data variance. From ROIMCR results, chemical annotation is performed using different methods: • Generating a list of signals and reanalyzing them in a data-dependent analysis. • Comparing the ROIMCR resolved mass spectra to those stored in online repositories. • Searching MS signals of chemical compounds in the ROIMCR resolved spectra profiles. |
|---|