Detecting methylation signatures in neurodegenerative disease by density-based clustering of applications with reducing noise

Abstract There have been numerous genetic and epigenetic datasets generated for the study of complex disease including neurodegenerative disease. However, analysis of such data often suffers from detecting the outliers of the samples, which subsequently affects the extraction of the true biological...

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Autores principales:	Saurav Mallik, Zhongming Zhao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/cac7be4a73e04b479ecf1dc9a2db5591
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spelling	oai:doaj.org-article:cac7be4a73e04b479ecf1dc9a2db55912021-12-02T13:34:00ZDetecting methylation signatures in neurodegenerative disease by density-based clustering of applications with reducing noise10.1038/s41598-020-78463-32045-2322https://doaj.org/article/cac7be4a73e04b479ecf1dc9a2db55912020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78463-3https://doaj.org/toc/2045-2322Abstract There have been numerous genetic and epigenetic datasets generated for the study of complex disease including neurodegenerative disease. However, analysis of such data often suffers from detecting the outliers of the samples, which subsequently affects the extraction of the true biological signals involved in the disease. To address this critical issue, we developed a novel framework for identifying methylation signatures using consecutive adaptation of a well-known outlier detection algorithm, density based clustering of applications with reducing noise (DBSCAN) followed by hierarchical clustering. We applied the framework to two representative neurodegenerative diseases, Alzheimer’s disease (AD) and Down syndrome (DS), using DNA methylation datasets from public sources (Gene Expression Omnibus, GEO accession ID: GSE74486). We first applied DBSCAN algorithm to eliminate outliers, and then used Limma statistical method to determine differentially methylated genes. Next, hierarchical clustering technique was applied to detect gene modules. Our analysis identified a methylation signature comprising 21 genes for AD and a methylation signature comprising 89 genes for DS, respectively. Our evaluation indicated that these two signatures could lead to high classification accuracy values (92% and 70%) for these two diseases. In summary, this framework will be useful to better detect outlier-free genetic and epigenetic signatures in various complex diseases and their developmental stages.Saurav MallikZhongming ZhaoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-14 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Saurav Mallik Zhongming Zhao Detecting methylation signatures in neurodegenerative disease by density-based clustering of applications with reducing noise
description	Abstract There have been numerous genetic and epigenetic datasets generated for the study of complex disease including neurodegenerative disease. However, analysis of such data often suffers from detecting the outliers of the samples, which subsequently affects the extraction of the true biological signals involved in the disease. To address this critical issue, we developed a novel framework for identifying methylation signatures using consecutive adaptation of a well-known outlier detection algorithm, density based clustering of applications with reducing noise (DBSCAN) followed by hierarchical clustering. We applied the framework to two representative neurodegenerative diseases, Alzheimer’s disease (AD) and Down syndrome (DS), using DNA methylation datasets from public sources (Gene Expression Omnibus, GEO accession ID: GSE74486). We first applied DBSCAN algorithm to eliminate outliers, and then used Limma statistical method to determine differentially methylated genes. Next, hierarchical clustering technique was applied to detect gene modules. Our analysis identified a methylation signature comprising 21 genes for AD and a methylation signature comprising 89 genes for DS, respectively. Our evaluation indicated that these two signatures could lead to high classification accuracy values (92% and 70%) for these two diseases. In summary, this framework will be useful to better detect outlier-free genetic and epigenetic signatures in various complex diseases and their developmental stages.
format	article
author	Saurav Mallik Zhongming Zhao
author_facet	Saurav Mallik Zhongming Zhao
author_sort	Saurav Mallik
title	Detecting methylation signatures in neurodegenerative disease by density-based clustering of applications with reducing noise
title_short	Detecting methylation signatures in neurodegenerative disease by density-based clustering of applications with reducing noise
title_full	Detecting methylation signatures in neurodegenerative disease by density-based clustering of applications with reducing noise
title_fullStr	Detecting methylation signatures in neurodegenerative disease by density-based clustering of applications with reducing noise
title_full_unstemmed	Detecting methylation signatures in neurodegenerative disease by density-based clustering of applications with reducing noise
title_sort	detecting methylation signatures in neurodegenerative disease by density-based clustering of applications with reducing noise
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/cac7be4a73e04b479ecf1dc9a2db5591
work_keys_str_mv	AT sauravmallik detectingmethylationsignaturesinneurodegenerativediseasebydensitybasedclusteringofapplicationswithreducingnoise AT zhongmingzhao detectingmethylationsignaturesinneurodegenerativediseasebydensitybasedclusteringofapplicationswithreducingnoise
_version_	1718392821433499648

Detecting methylation signatures in neurodegenerative disease by density-based clustering of applications with reducing noise

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