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...

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Autores principales:	Francisco Traquete, João Luz, Carlos Cordeiro, Marta Sousa Silva, António E. N. Ferreira
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	metabolomics data treatment data analysis Fourier Transform Ion Cyclotron Resonance mass spectrometry multivariate analysis Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/4eb4d4488aa04b63917ffd9e0f0e5076
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spelling	oai:doaj.org-article:4eb4d4488aa04b63917ffd9e0f0e50762021-11-25T18:20:55ZBinary Simplification as an Effective Tool in Metabolomics Data Analysis10.3390/metabo111107882218-1989https://doaj.org/article/4eb4d4488aa04b63917ffd9e0f0e50762021-11-01T00:00:00Zhttps://www.mdpi.com/2218-1989/11/11/788https://doaj.org/toc/2218-1989Metabolomics 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.Francisco TraqueteJoão LuzCarlos CordeiroMarta Sousa SilvaAntónio E. N. FerreiraMDPI AGarticlemetabolomicsdata treatmentdata analysisFourier Transform Ion Cyclotron Resonance mass spectrometrymultivariate analysisMicrobiologyQR1-502ENMetabolites, Vol 11, Iss 788, p 788 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	metabolomics data treatment data analysis Fourier Transform Ion Cyclotron Resonance mass spectrometry multivariate analysis Microbiology QR1-502
spellingShingle	metabolomics data treatment data analysis Fourier Transform Ion Cyclotron Resonance mass spectrometry multivariate analysis Microbiology QR1-502 Francisco Traquete João Luz Carlos Cordeiro Marta Sousa Silva António E. N. Ferreira Binary Simplification as an Effective Tool in Metabolomics Data Analysis
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	article
author	Francisco Traquete João Luz Carlos Cordeiro Marta Sousa Silva António E. N. Ferreira
author_facet	Francisco Traquete João Luz Carlos Cordeiro Marta Sousa Silva António E. N. Ferreira
author_sort	Francisco Traquete
title	Binary Simplification as an Effective Tool in Metabolomics Data Analysis
title_short	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_sort	binary simplification as an effective tool in metabolomics data analysis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4eb4d4488aa04b63917ffd9e0f0e5076
work_keys_str_mv	AT franciscotraquete binarysimplificationasaneffectivetoolinmetabolomicsdataanalysis AT joaoluz binarysimplificationasaneffectivetoolinmetabolomicsdataanalysis AT carloscordeiro binarysimplificationasaneffectivetoolinmetabolomicsdataanalysis AT martasousasilva binarysimplificationasaneffectivetoolinmetabolomicsdataanalysis AT antonioenferreira binarysimplificationasaneffectivetoolinmetabolomicsdataanalysis
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Binary Simplification as an Effective Tool in Metabolomics Data Analysis

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