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 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.


BACKGROUND
R codes are available upon request.
Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, 1025 E, 7th Street, Bloomington, IN 47405, USA. zc3@indiana.edu
Zhongxue
Z
Chen
Hanwen
H
Huang
Jianzhong
J
Liu
Hon Keung
HK
Tony Ng
Saralees
S
Nadarajah
Xudong
X
Huang
Youping
Y
Deng
D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D019175:DNA Methylation; D005260:Female; D056426:Genetic Loci; D006801:Humans; D008875:Middle Aged; D020411:Oligonucleotide Array Sequence Analysis; D010051:Ovarian Neoplasms / Q000235:genetics* / Q000378:metabolism