Data-driven detection of subtype-specific differentially expressed genes

Abstract Among multiple subtypes of tissue or cell, subtype-specific differentially-expressed genes (SDEGs) are defined as being most-upregulated in only one subtype but not in any other. Detecting SDEGs plays a critical role in the molecular characterization and deconvolution of multicellular compl...

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Autores principales:	Lulu Chen, Yingzhou Lu, Chiung-Ting Wu, Robert Clarke, Guoqiang Yu, Jennifer E. Van Eyk, David M. Herrington, Yue Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d7b2495d65de403294df9998bc8b969d
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spelling	oai:doaj.org-article:d7b2495d65de403294df9998bc8b969d2021-12-02T15:23:02ZData-driven detection of subtype-specific differentially expressed genes10.1038/s41598-020-79704-12045-2322https://doaj.org/article/d7b2495d65de403294df9998bc8b969d2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79704-1https://doaj.org/toc/2045-2322Abstract Among multiple subtypes of tissue or cell, subtype-specific differentially-expressed genes (SDEGs) are defined as being most-upregulated in only one subtype but not in any other. Detecting SDEGs plays a critical role in the molecular characterization and deconvolution of multicellular complex tissues. Classic differential analysis assumes a null hypothesis whose test statistic is not subtype-specific, thus can produce a high false positive rate and/or lower detection power. Here we first introduce a One-Versus-Everyone Fold Change (OVE-FC) test for detecting SDEGs. We then propose a scaled test statistic (OVE-sFC) for assessing the statistical significance of SDEGs that applies a mixture null distribution model and a tailored permutation test. The OVE-FC/sFC test was validated on both type 1 error rate and detection power using extensive simulation data sets generated from real gene expression profiles of purified subtype samples. The OVE-FC/sFC test was then applied to two benchmark gene expression data sets of purified subtype samples and detected many known or previously unknown SDEGs. Subsequent supervised deconvolution results on synthesized bulk expression data, obtained using the SDEGs detected from the independent purified expression data by the OVE-FC/sFC test, showed superior performance in deconvolution accuracy when compared with popular peer methods.Lulu ChenYingzhou LuChiung-Ting WuRobert ClarkeGuoqiang YuJennifer E. Van EykDavid M. HerringtonYue WangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lulu Chen Yingzhou Lu Chiung-Ting Wu Robert Clarke Guoqiang Yu Jennifer E. Van Eyk David M. Herrington Yue Wang Data-driven detection of subtype-specific differentially expressed genes
description	Abstract Among multiple subtypes of tissue or cell, subtype-specific differentially-expressed genes (SDEGs) are defined as being most-upregulated in only one subtype but not in any other. Detecting SDEGs plays a critical role in the molecular characterization and deconvolution of multicellular complex tissues. Classic differential analysis assumes a null hypothesis whose test statistic is not subtype-specific, thus can produce a high false positive rate and/or lower detection power. Here we first introduce a One-Versus-Everyone Fold Change (OVE-FC) test for detecting SDEGs. We then propose a scaled test statistic (OVE-sFC) for assessing the statistical significance of SDEGs that applies a mixture null distribution model and a tailored permutation test. The OVE-FC/sFC test was validated on both type 1 error rate and detection power using extensive simulation data sets generated from real gene expression profiles of purified subtype samples. The OVE-FC/sFC test was then applied to two benchmark gene expression data sets of purified subtype samples and detected many known or previously unknown SDEGs. Subsequent supervised deconvolution results on synthesized bulk expression data, obtained using the SDEGs detected from the independent purified expression data by the OVE-FC/sFC test, showed superior performance in deconvolution accuracy when compared with popular peer methods.
format	article
author	Lulu Chen Yingzhou Lu Chiung-Ting Wu Robert Clarke Guoqiang Yu Jennifer E. Van Eyk David M. Herrington Yue Wang
author_facet	Lulu Chen Yingzhou Lu Chiung-Ting Wu Robert Clarke Guoqiang Yu Jennifer E. Van Eyk David M. Herrington Yue Wang
author_sort	Lulu Chen
title	Data-driven detection of subtype-specific differentially expressed genes
title_short	Data-driven detection of subtype-specific differentially expressed genes
title_full	Data-driven detection of subtype-specific differentially expressed genes
title_fullStr	Data-driven detection of subtype-specific differentially expressed genes
title_full_unstemmed	Data-driven detection of subtype-specific differentially expressed genes
title_sort	data-driven detection of subtype-specific differentially expressed genes
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d7b2495d65de403294df9998bc8b969d
work_keys_str_mv	AT luluchen datadrivendetectionofsubtypespecificdifferentiallyexpressedgenes AT yingzhoulu datadrivendetectionofsubtypespecificdifferentiallyexpressedgenes AT chiungtingwu datadrivendetectionofsubtypespecificdifferentiallyexpressedgenes AT robertclarke datadrivendetectionofsubtypespecificdifferentiallyexpressedgenes AT guoqiangyu datadrivendetectionofsubtypespecificdifferentiallyexpressedgenes AT jenniferevaneyk datadrivendetectionofsubtypespecificdifferentiallyexpressedgenes AT davidmherrington datadrivendetectionofsubtypespecificdifferentiallyexpressedgenes AT yuewang datadrivendetectionofsubtypespecificdifferentiallyexpressedgenes
_version_	1718387355211005952

Data-driven detection of subtype-specific differentially expressed genes

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