Identifying hypermethylated CpG islands using a quantile regression model

Background: DNA methylation has been shown to play an important role in the silencing of tumor suppressor genes in various tumor types. In order to have a system-wide understanding of the methylation changes that occur in tumors, we have developed a differential methylation hybridization (DMH) protocol that can simultaneously assay the methylation status of all known CpG islands (CGIs) using microarray technologies. A large percentage of signals obtained from microarrays can be attributed to various measurable and unmeasurable confounding factors unrelated to the biological question at hand. In order to correct the bias due to noise, we first implemented a quantile regression model, with a quantile level equal to 75%, to identify hypermethylated CGIs in an earlier work. As a proof of concept, we applied this model to methylation microarray data generated from breast cancer cell lines. However, we were unsure whether 75% was the best quantile level for identifying hypermethylated CGIs....

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Shuying Sun, Zhengyi Chen, Pearlly Yan, Yi-Wen Huang, Tim Hui-Ming Huang, Shili Lin
• Keywords: Artificial Intelligence, BMCBI 2011, Methylation, Quantile Levels, Quantile Regression Model