Identification of genes associated with altered gene expression and m6A profiles during hypoxia using tensor decomposition based unsupervised feature extraction

Abstract Although hypoxia is a critical factor that can drive the progression of various diseases, the mechanism underlying hypoxia itself remains unclear. Recently, m6A has been proposed as an important factor driving hypoxia. Despite successful analyses, potential genes were not selected with stat...

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Autores principales:	Sanjiban Sekhar Roy, Y.-H. Taguchi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c8353c34d1dc4d088b8856d3d3ddc044
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spelling	oai:doaj.org-article:c8353c34d1dc4d088b8856d3d3ddc0442021-12-02T16:55:54ZIdentification of genes associated with altered gene expression and m6A profiles during hypoxia using tensor decomposition based unsupervised feature extraction10.1038/s41598-021-87779-72045-2322https://doaj.org/article/c8353c34d1dc4d088b8856d3d3ddc0442021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87779-7https://doaj.org/toc/2045-2322Abstract Although hypoxia is a critical factor that can drive the progression of various diseases, the mechanism underlying hypoxia itself remains unclear. Recently, m6A has been proposed as an important factor driving hypoxia. Despite successful analyses, potential genes were not selected with statistical significance but were selected based solely on fold changes. Because the number of genes is large while the number of samples is small, it was impossible to select genes using conventional feature selection methods with statistical significance. In this study, we applied the recently proposed principal component analysis (PCA), tensor decomposition (TD), and kernel tensor decomposition (KTD)-based unsupervised feature extraction (FE) to a hypoxia data set. We found that PCA, TD, and KTD-based unsupervised FE could successfully identify a limited number of genes associated with altered gene expression and m6A profiles, as well as the enrichment of hypoxia-related biological terms, with improved statistical significance.Sanjiban Sekhar RoyY.-H. TaguchiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sanjiban Sekhar Roy Y.-H. Taguchi Identification of genes associated with altered gene expression and m6A profiles during hypoxia using tensor decomposition based unsupervised feature extraction
description	Abstract Although hypoxia is a critical factor that can drive the progression of various diseases, the mechanism underlying hypoxia itself remains unclear. Recently, m6A has been proposed as an important factor driving hypoxia. Despite successful analyses, potential genes were not selected with statistical significance but were selected based solely on fold changes. Because the number of genes is large while the number of samples is small, it was impossible to select genes using conventional feature selection methods with statistical significance. In this study, we applied the recently proposed principal component analysis (PCA), tensor decomposition (TD), and kernel tensor decomposition (KTD)-based unsupervised feature extraction (FE) to a hypoxia data set. We found that PCA, TD, and KTD-based unsupervised FE could successfully identify a limited number of genes associated with altered gene expression and m6A profiles, as well as the enrichment of hypoxia-related biological terms, with improved statistical significance.
format	article
author	Sanjiban Sekhar Roy Y.-H. Taguchi
author_facet	Sanjiban Sekhar Roy Y.-H. Taguchi
author_sort	Sanjiban Sekhar Roy
title	Identification of genes associated with altered gene expression and m6A profiles during hypoxia using tensor decomposition based unsupervised feature extraction
title_short	Identification of genes associated with altered gene expression and m6A profiles during hypoxia using tensor decomposition based unsupervised feature extraction
title_full	Identification of genes associated with altered gene expression and m6A profiles during hypoxia using tensor decomposition based unsupervised feature extraction
title_fullStr	Identification of genes associated with altered gene expression and m6A profiles during hypoxia using tensor decomposition based unsupervised feature extraction
title_full_unstemmed	Identification of genes associated with altered gene expression and m6A profiles during hypoxia using tensor decomposition based unsupervised feature extraction
title_sort	identification of genes associated with altered gene expression and m6a profiles during hypoxia using tensor decomposition based unsupervised feature extraction
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c8353c34d1dc4d088b8856d3d3ddc044
work_keys_str_mv	AT sanjibansekharroy identificationofgenesassociatedwithalteredgeneexpressionandm6aprofilesduringhypoxiausingtensordecompositionbasedunsupervisedfeatureextraction AT yhtaguchi identificationofgenesassociatedwithalteredgeneexpressionandm6aprofilesduringhypoxiausingtensordecompositionbasedunsupervisedfeatureextraction
_version_	1718382827497586688

Identification of genes associated with altered gene expression and m6A profiles during hypoxia using tensor decomposition based unsupervised feature extraction

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