Repository scale classification and decomposition of tandem mass spectral data

Abstract Various studies have shown associations between molecular features and phenotypes of biological samples. These studies, however, focus on a single phenotype per study and are not applicable to repository scale metabolomics data. Here we report MetSummarizer, a method for predicting (i) the...

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Main Authors:	Mihir Mongia, Hosein Mohimani
Format:	article
Language:	EN
Published:	Nature Portfolio 2021
Subjects:	Medicine R Science Q
Online Access:	https://doaj.org/article/b3083b32aaad4218b19b5e83e205179d
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spelling	oai:doaj.org-article:b3083b32aaad4218b19b5e83e205179d2021-12-02T15:51:14ZRepository scale classification and decomposition of tandem mass spectral data10.1038/s41598-021-87796-62045-2322https://doaj.org/article/b3083b32aaad4218b19b5e83e205179d2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87796-6https://doaj.org/toc/2045-2322Abstract Various studies have shown associations between molecular features and phenotypes of biological samples. These studies, however, focus on a single phenotype per study and are not applicable to repository scale metabolomics data. Here we report MetSummarizer, a method for predicting (i) the biological phenotypes of environmental and host-oriented samples, and (ii) the raw ingredient composition of complex mixtures. We show that the aggregation of various metabolomic datasets can improve the accuracy of predictions. Since these datasets have been collected using different standards at various laboratories, in order to get unbiased results it is crucial to detect and discard standard-specific features during the classification step. We further report high accuracy in prediction of the raw ingredient composition of complex foods from the Global Foodomics Project.Mihir MongiaHosein MohimaniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mihir Mongia Hosein Mohimani Repository scale classification and decomposition of tandem mass spectral data
description	Abstract Various studies have shown associations between molecular features and phenotypes of biological samples. These studies, however, focus on a single phenotype per study and are not applicable to repository scale metabolomics data. Here we report MetSummarizer, a method for predicting (i) the biological phenotypes of environmental and host-oriented samples, and (ii) the raw ingredient composition of complex mixtures. We show that the aggregation of various metabolomic datasets can improve the accuracy of predictions. Since these datasets have been collected using different standards at various laboratories, in order to get unbiased results it is crucial to detect and discard standard-specific features during the classification step. We further report high accuracy in prediction of the raw ingredient composition of complex foods from the Global Foodomics Project.
format	article
author	Mihir Mongia Hosein Mohimani
author_facet	Mihir Mongia Hosein Mohimani
author_sort	Mihir Mongia
title	Repository scale classification and decomposition of tandem mass spectral data
title_short	Repository scale classification and decomposition of tandem mass spectral data
title_full	Repository scale classification and decomposition of tandem mass spectral data
title_fullStr	Repository scale classification and decomposition of tandem mass spectral data
title_full_unstemmed	Repository scale classification and decomposition of tandem mass spectral data
title_sort	repository scale classification and decomposition of tandem mass spectral data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b3083b32aaad4218b19b5e83e205179d
work_keys_str_mv	AT mihirmongia repositoryscaleclassificationanddecompositionoftandemmassspectraldata AT hoseinmohimani repositoryscaleclassificationanddecompositionoftandemmassspectraldata
_version_	1718385653930000384

Repository scale classification and decomposition of tandem mass spectral data

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