This article is part of the supplement: Proceedings of the 2011 International Conference on Bioinformatics and Computational Biology (BIOCOMP'11)
Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data
1 Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, 1025 E. 7th Street, Bloomington, IN 47405, USA
2 Center for Clinical and Translational Sciences, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
3 Chem21 Group, Inc, 1780 Wilson Drive, Lake Forest, IL 60045, USA
4 Department of Statistical Science, Southern Methodist University, Dallas, TX 75275, USA
5 School of Mathematics, University of Manchester, Manchester, M13 9PL, UK
6 Neurochemistry Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
7 Rush University Cancer Center, Department of Internal Medicine and Biochemistry, Rush University Medical Center, Chicago, IL 60612, USA
BMC Medical Genomics 2013, 6(Suppl 1):S9 doi:10.1186/1755-8794-6-S1-S9Published: 23 January 2013
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.
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.
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.
R codes are available upon request.