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Binary Simplification as an Effective Tool in Metabolomics Data Analysis
Metabolomics aims to perform a comprehensive identification and quantification of the small molecules present in a biological system. Due to metabolite diversity in concentration, structure, and chemical characteristics, the use of high-resolution methodologies, such as mass spectrometry (MS) or nuc...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8621519/ https://www.ncbi.nlm.nih.gov/pubmed/34822446 http://dx.doi.org/10.3390/metabo11110788 |
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author | Traquete, Francisco Luz, João Cordeiro, Carlos Sousa Silva, Marta Ferreira, António E. N. |
author_facet | Traquete, Francisco Luz, João Cordeiro, Carlos Sousa Silva, Marta Ferreira, António E. N. |
author_sort | Traquete, Francisco |
collection | PubMed |
description | Metabolomics aims to perform a comprehensive identification and quantification of the small molecules present in a biological system. Due to metabolite diversity in concentration, structure, and chemical characteristics, the use of high-resolution methodologies, such as mass spectrometry (MS) or nuclear magnetic resonance (NMR), is required. In metabolomics data analysis, suitable data pre-processing, and pre-treatment procedures are fundamental, with subsequent steps aiming at highlighting the significant biological variation between samples over background noise. Traditional data analysis focuses primarily on the comparison of the features’ intensity values. However, intensity data are highly variable between experimental batches, instruments, and pre-processing methods or parameters. The aim of this work was to develop a new pre-treatment method for MS-based metabolomics data, in the context of sample profiling and discrimination, considering only the occurrence of spectral features, encoding feature presence as 1 and absence as 0. This “Binary Simplification” encoding (BinSim) was used to transform several benchmark datasets before the application of clustering and classification methods. The performance of these methods after the BinSim pre-treatment was consistently as good as and often better than after different combinations of traditional, intensity-based, pre-treatments. Binary Simplification is, therefore, a viable pre-treatment procedure that effectively simplifies metabolomics data-analysis pipelines. |
format | Online Article Text |
id | pubmed-8621519 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86215192021-11-27 Binary Simplification as an Effective Tool in Metabolomics Data Analysis Traquete, Francisco Luz, João Cordeiro, Carlos Sousa Silva, Marta Ferreira, António E. N. Metabolites Article Metabolomics aims to perform a comprehensive identification and quantification of the small molecules present in a biological system. Due to metabolite diversity in concentration, structure, and chemical characteristics, the use of high-resolution methodologies, such as mass spectrometry (MS) or nuclear magnetic resonance (NMR), is required. In metabolomics data analysis, suitable data pre-processing, and pre-treatment procedures are fundamental, with subsequent steps aiming at highlighting the significant biological variation between samples over background noise. Traditional data analysis focuses primarily on the comparison of the features’ intensity values. However, intensity data are highly variable between experimental batches, instruments, and pre-processing methods or parameters. The aim of this work was to develop a new pre-treatment method for MS-based metabolomics data, in the context of sample profiling and discrimination, considering only the occurrence of spectral features, encoding feature presence as 1 and absence as 0. This “Binary Simplification” encoding (BinSim) was used to transform several benchmark datasets before the application of clustering and classification methods. The performance of these methods after the BinSim pre-treatment was consistently as good as and often better than after different combinations of traditional, intensity-based, pre-treatments. Binary Simplification is, therefore, a viable pre-treatment procedure that effectively simplifies metabolomics data-analysis pipelines. MDPI 2021-11-18 /pmc/articles/PMC8621519/ /pubmed/34822446 http://dx.doi.org/10.3390/metabo11110788 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Traquete, Francisco Luz, João Cordeiro, Carlos Sousa Silva, Marta Ferreira, António E. N. Binary Simplification as an Effective Tool in Metabolomics Data Analysis |
title | Binary Simplification as an Effective Tool in Metabolomics Data Analysis |
title_full | Binary Simplification as an Effective Tool in Metabolomics Data Analysis |
title_fullStr | Binary Simplification as an Effective Tool in Metabolomics Data Analysis |
title_full_unstemmed | Binary Simplification as an Effective Tool in Metabolomics Data Analysis |
title_short | Binary Simplification as an Effective Tool in Metabolomics Data Analysis |
title_sort | binary simplification as an effective tool in metabolomics data analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8621519/ https://www.ncbi.nlm.nih.gov/pubmed/34822446 http://dx.doi.org/10.3390/metabo11110788 |
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