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Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data

BACKGROUND: It is well known that DNA methylation, as an epigenetic factor, has an important effect on gene expression and disease development. Detecting differentially methylated loci under different conditions, such as cancer types or treatments, is of great interest in current research as it is i...

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Autores principales: Chen, Zhongxue, Huang, Hanwen, Liu, Jianzhong, Tony Ng, Hon Keung, Nadarajah, Saralees, Huang, Xudong, Deng, Youping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3552689/
https://www.ncbi.nlm.nih.gov/pubmed/23369576
http://dx.doi.org/10.1186/1755-8794-6-S1-S9
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author Chen, Zhongxue
Huang, Hanwen
Liu, Jianzhong
Tony Ng, Hon Keung
Nadarajah, Saralees
Huang, Xudong
Deng, Youping
author_facet Chen, Zhongxue
Huang, Hanwen
Liu, Jianzhong
Tony Ng, Hon Keung
Nadarajah, Saralees
Huang, Xudong
Deng, Youping
author_sort Chen, Zhongxue
collection PubMed
description BACKGROUND: It is well known that DNA methylation, as an epigenetic factor, has an important effect on gene expression and disease development. Detecting differentially methylated loci under different conditions, such as cancer types or treatments, is of great interest in current research as it is important in cancer diagnosis and classification. However, inappropriate testing approaches can result in large false positives and/or false negatives. Appropriate and powerful statistical methods are desirable but very limited in the literature. RESULTS: In this paper, we propose a nonparametric method to detect differentially methylated loci under multiple conditions for Illumina Array Methylation data. We compare the new method with other methods using simulated and real data. Our study shows that the proposed one outperforms other methods considered in this paper. CONCLUSIONS: Due to the unique feature of the Illumina Array Methylation data, commonly used statistical tests will lose power or give misleading results. Therefore, appropriate statistical methods are crucial for this type of data. Powerful statistical approaches remain to be developed. AVAILABILITY: R codes are available upon request.
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spelling pubmed-35526892013-01-28 Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data Chen, Zhongxue Huang, Hanwen Liu, Jianzhong Tony Ng, Hon Keung Nadarajah, Saralees Huang, Xudong Deng, Youping BMC Med Genomics Research BACKGROUND: It is well known that DNA methylation, as an epigenetic factor, has an important effect on gene expression and disease development. Detecting differentially methylated loci under different conditions, such as cancer types or treatments, is of great interest in current research as it is important in cancer diagnosis and classification. However, inappropriate testing approaches can result in large false positives and/or false negatives. Appropriate and powerful statistical methods are desirable but very limited in the literature. RESULTS: In this paper, we propose a nonparametric method to detect differentially methylated loci under multiple conditions for Illumina Array Methylation data. We compare the new method with other methods using simulated and real data. Our study shows that the proposed one outperforms other methods considered in this paper. CONCLUSIONS: Due to the unique feature of the Illumina Array Methylation data, commonly used statistical tests will lose power or give misleading results. Therefore, appropriate statistical methods are crucial for this type of data. Powerful statistical approaches remain to be developed. AVAILABILITY: R codes are available upon request. BioMed Central 2013-01-23 /pmc/articles/PMC3552689/ /pubmed/23369576 http://dx.doi.org/10.1186/1755-8794-6-S1-S9 Text en Copyright ©2013 Chen et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Chen, Zhongxue
Huang, Hanwen
Liu, Jianzhong
Tony Ng, Hon Keung
Nadarajah, Saralees
Huang, Xudong
Deng, Youping
Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data
title Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data
title_full Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data
title_fullStr Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data
title_full_unstemmed Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data
title_short Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data
title_sort detecting differentially methylated loci for illumina array methylation data based on human ovarian cancer data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3552689/
https://www.ncbi.nlm.nih.gov/pubmed/23369576
http://dx.doi.org/10.1186/1755-8794-6-S1-S9
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