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




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BMC Medical Genomics

, 6:S9

First Online: 23 January 2013

Abstract

BackgroundIt 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.

ResultsIn 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.

ConclusionsDue 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.

AvailabilityR codes are available upon request.

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Author: Zhongxue Chen - Hanwen Huang - Jianzhong Liu - Hon Keung Tony Ng - Saralees Nadarajah - Xudong Huang - Youping Deng

Source: https://link.springer.com/







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