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OS-PCA: Orthogonal Smoothed Principal Component Analysis Applied to Metabolome Data
Principal component analysis (PCA) has been widely used in metabolomics. However, it is not always possible to detect phenotype-associated principal component (PC) scores. Previously, we proposed a smoothed PCA for samples acquired with a time course or rank order, but hypothesis testing to select s...
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/PMC7999099/ https://www.ncbi.nlm.nih.gov/pubmed/33807892 http://dx.doi.org/10.3390/metabo11030149 |
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author | Yamamoto, Hiroyuki Nakayama, Yasumune Tsugawa, Hiroshi |
author_facet | Yamamoto, Hiroyuki Nakayama, Yasumune Tsugawa, Hiroshi |
author_sort | Yamamoto, Hiroyuki |
collection | PubMed |
description | Principal component analysis (PCA) has been widely used in metabolomics. However, it is not always possible to detect phenotype-associated principal component (PC) scores. Previously, we proposed a smoothed PCA for samples acquired with a time course or rank order, but hypothesis testing to select significant metabolite candidates was not possible. Here, we modified the smoothed PCA as an orthogonal smoothed PCA (OS-PCA) so that statistical hypothesis testing in OS-PC loadings could be performed with the same PC projections provided by the smoothed PCA. Statistical hypothesis testing is especially useful in metabolomics because biological interpretations are made based on statistically significant metabolites. We applied the OS-PCA method to two real metabolome datasets, one for metabolic turnover analysis and the other for evaluating the taste of Japanese green tea. The OS-PCA successfully extracted similar PC scores as the smoothed PCA; these scores reflected the expected phenotypes. The significant metabolites that were selected using statistical hypothesis testing of OS-PC loading facilitated biological interpretations that were consistent with the results of our previous study. Our results suggest that OS-PCA combined with statistical hypothesis testing of OS-PC loading is a useful method for the analysis of metabolome data. |
format | Online Article Text |
id | pubmed-7999099 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79990992021-03-28 OS-PCA: Orthogonal Smoothed Principal Component Analysis Applied to Metabolome Data Yamamoto, Hiroyuki Nakayama, Yasumune Tsugawa, Hiroshi Metabolites Article Principal component analysis (PCA) has been widely used in metabolomics. However, it is not always possible to detect phenotype-associated principal component (PC) scores. Previously, we proposed a smoothed PCA for samples acquired with a time course or rank order, but hypothesis testing to select significant metabolite candidates was not possible. Here, we modified the smoothed PCA as an orthogonal smoothed PCA (OS-PCA) so that statistical hypothesis testing in OS-PC loadings could be performed with the same PC projections provided by the smoothed PCA. Statistical hypothesis testing is especially useful in metabolomics because biological interpretations are made based on statistically significant metabolites. We applied the OS-PCA method to two real metabolome datasets, one for metabolic turnover analysis and the other for evaluating the taste of Japanese green tea. The OS-PCA successfully extracted similar PC scores as the smoothed PCA; these scores reflected the expected phenotypes. The significant metabolites that were selected using statistical hypothesis testing of OS-PC loading facilitated biological interpretations that were consistent with the results of our previous study. Our results suggest that OS-PCA combined with statistical hypothesis testing of OS-PC loading is a useful method for the analysis of metabolome data. MDPI 2021-03-05 /pmc/articles/PMC7999099/ /pubmed/33807892 http://dx.doi.org/10.3390/metabo11030149 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 (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Yamamoto, Hiroyuki Nakayama, Yasumune Tsugawa, Hiroshi OS-PCA: Orthogonal Smoothed Principal Component Analysis Applied to Metabolome Data |
title | OS-PCA: Orthogonal Smoothed Principal Component Analysis Applied to Metabolome Data |
title_full | OS-PCA: Orthogonal Smoothed Principal Component Analysis Applied to Metabolome Data |
title_fullStr | OS-PCA: Orthogonal Smoothed Principal Component Analysis Applied to Metabolome Data |
title_full_unstemmed | OS-PCA: Orthogonal Smoothed Principal Component Analysis Applied to Metabolome Data |
title_short | OS-PCA: Orthogonal Smoothed Principal Component Analysis Applied to Metabolome Data |
title_sort | os-pca: orthogonal smoothed principal component analysis applied to metabolome data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7999099/ https://www.ncbi.nlm.nih.gov/pubmed/33807892 http://dx.doi.org/10.3390/metabo11030149 |
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