ArticleBMC medical genomics2013
Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data.
Article in BMC medical genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Rare variants in the endocytic pathway are associated with Alzheimer's disease, its related phenotypes, and functional consequences.PLoS genetics · 2021Article
- Data-Driven-Based Approach to Identifying Differentially Methylated Regions Using Modified 1D Ising Model.BioMed research international · 2018Article
- Distinct DNA methylation profiles in bone and blood of osteoporotic and healthy postmenopausal women.Epigenetics · 2017Article
- PLMET: A Novel Pseudolikelihood-Based EM Test for Homogeneity in Generalilzed Exponential Tilt Mixture Models.Journal of the American Statistical Association · 2017Article
- Simultaneous statistical inference for epigenetic data.PloS one · 2015Article
- Detecting differentially methylated loci for multiple treatments based on high-throughput methylation data.BMC bioinformatics · 2014Article
- Epigenetic analysis of neurocognitive development at 1 year of age in a community-based pregnancy cohort.Behavior genetics · 2014Article
- A new statistical approach to combining p-values using gamma distribution and its application to genome-wide association study.BMC bioinformatics · 2014Article
- Age-adjusted nonparametric detection of differential DNA methylation with case-control designs.BMC bioinformatics · 2013Article
- The current trend of genomics research for human diseases.BMC medical genomics · 2013Article
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Authors and funding
7 authors.
Funding
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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. AVAILABILITY: R codes are available upon request.
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