ArticleBMC bioinformatics2009
Identifying differentially methylated genes using mixed effect and generalized least square models.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- HMPL: A Pipeline for Identifying Hemimethylation Patterns by Comparing Two Samples.Cancer informatics · 2015Article
- A novel method for identification and quantification of consistently differentially methylated regions.PloS one · 2014Article
- Statistical approaches for the analysis of DNA methylation microarray data.Human genetics · 2011Review
- A beta-mixture model for dimensionality reduction, sample classification and analysis.BMC bioinformatics · 2011Article
- Preprocessing differential methylation hybridization microarray data.BioData mining · 2011Article
- Identifying hypermethylated CpG islands using a quantile regression model.BMC bioinformatics · 2011Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundDNA methylation plays an important role in the process of tumorigenesis. Identifying differentially methylated genes or CpG islands (CGIs) associated with genes between two tumor subtypes is thus an important biological question. The methylation status of all CGIs in the whole genome can be assayed with differential methylation hybridization (DMH) microarrays. However, patient samples or cell lines are heterogeneous, so their methylation pattern may be very different. In addition, neighboring probes at each CGI are correlated. How these factors affect the analysis of DMH data is unknown.
resultsWe propose a new method for identifying differentially methylated (DM) genes by identifying the associated DM CGI(s). At each CGI, we implement four different mixed effect and generalized least square models to identify DM genes between two groups. We compare four models with a simple least square regression model to study the impact of incorporating random effects and correlations.
conclusionsWe demonstrate that the inclusion (or exclusion) of random effects and the choice of correlation structures can significantly affect the results of the data analysis. We also assess the false discovery rate of different models using CGIs associated with housekeeping genes.
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Registered trials
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