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Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles

INTRODUCTION: Contemporary metabolomic fingerprinting is based on multiple spectrometric and chromatographic signals, used either alone or combined with structural and chemical information of metabolic markers at the qualitative and semiquantitative level. However, signal shifting, convolution, and...

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Autores principales: Rácz, Anita, Andrić, Filip, Bajusz, Dávid, Héberger, Károly
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5846857/
https://www.ncbi.nlm.nih.gov/pubmed/29568246
http://dx.doi.org/10.1007/s11306-018-1327-y
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author Rácz, Anita
Andrić, Filip
Bajusz, Dávid
Héberger, Károly
author_facet Rácz, Anita
Andrić, Filip
Bajusz, Dávid
Héberger, Károly
author_sort Rácz, Anita
collection PubMed
description INTRODUCTION: Contemporary metabolomic fingerprinting is based on multiple spectrometric and chromatographic signals, used either alone or combined with structural and chemical information of metabolic markers at the qualitative and semiquantitative level. However, signal shifting, convolution, and matrix effects may compromise metabolomic patterns. Recent increase in the use of qualitative metabolomic data, described by the presence (1) or absence (0) of particular metabolites, demonstrates great potential in the field of metabolomic profiling and fingerprint analysis. OBJECTIVES: The aim of this study is a comprehensive evaluation of binary similarity measures for the elucidation of patterns among samples of different botanical origin and various metabolomic profiles. METHODS: Nine qualitative metabolomic data sets covering a wide range of natural products and metabolomic profiles were applied to assess 44 binary similarity measures for the fingerprinting of plant extracts and natural products. The measures were analyzed by the novel sum of ranking differences method (SRD), searching for the most promising candidates. RESULTS: Baroni-Urbani–Buser (BUB) and Hawkins–Dotson (HD) similarity coefficients were selected as the best measures by SRD and analysis of variance (ANOVA), while Dice (Di1), Yule, Russel-Rao, and Consonni-Todeschini 3 ranked the worst. ANOVA revealed that concordantly and intermediately symmetric similarity coefficients are better candidates for metabolomic fingerprinting than the asymmetric and correlation based ones. The fingerprint analysis based on the BUB and HD coefficients and qualitative metabolomic data performed equally well as the quantitative metabolomic profile analysis. CONCLUSION: Fingerprint analysis based on the qualitative metabolomic profiles and binary similarity measures proved to be a reliable way in finding the same/similar patterns in metabolomic data as that extracted from quantitative data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11306-018-1327-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-58468572018-03-20 Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles Rácz, Anita Andrić, Filip Bajusz, Dávid Héberger, Károly Metabolomics Original Article INTRODUCTION: Contemporary metabolomic fingerprinting is based on multiple spectrometric and chromatographic signals, used either alone or combined with structural and chemical information of metabolic markers at the qualitative and semiquantitative level. However, signal shifting, convolution, and matrix effects may compromise metabolomic patterns. Recent increase in the use of qualitative metabolomic data, described by the presence (1) or absence (0) of particular metabolites, demonstrates great potential in the field of metabolomic profiling and fingerprint analysis. OBJECTIVES: The aim of this study is a comprehensive evaluation of binary similarity measures for the elucidation of patterns among samples of different botanical origin and various metabolomic profiles. METHODS: Nine qualitative metabolomic data sets covering a wide range of natural products and metabolomic profiles were applied to assess 44 binary similarity measures for the fingerprinting of plant extracts and natural products. The measures were analyzed by the novel sum of ranking differences method (SRD), searching for the most promising candidates. RESULTS: Baroni-Urbani–Buser (BUB) and Hawkins–Dotson (HD) similarity coefficients were selected as the best measures by SRD and analysis of variance (ANOVA), while Dice (Di1), Yule, Russel-Rao, and Consonni-Todeschini 3 ranked the worst. ANOVA revealed that concordantly and intermediately symmetric similarity coefficients are better candidates for metabolomic fingerprinting than the asymmetric and correlation based ones. The fingerprint analysis based on the BUB and HD coefficients and qualitative metabolomic data performed equally well as the quantitative metabolomic profile analysis. CONCLUSION: Fingerprint analysis based on the qualitative metabolomic profiles and binary similarity measures proved to be a reliable way in finding the same/similar patterns in metabolomic data as that extracted from quantitative data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11306-018-1327-y) contains supplementary material, which is available to authorized users. Springer US 2018-01-31 2018 /pmc/articles/PMC5846857/ /pubmed/29568246 http://dx.doi.org/10.1007/s11306-018-1327-y Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Article
Rácz, Anita
Andrić, Filip
Bajusz, Dávid
Héberger, Károly
Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles
title Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles
title_full Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles
title_fullStr Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles
title_full_unstemmed Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles
title_short Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles
title_sort binary similarity measures for fingerprint analysis of qualitative metabolomic profiles
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5846857/
https://www.ncbi.nlm.nih.gov/pubmed/29568246
http://dx.doi.org/10.1007/s11306-018-1327-y
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