Identifying hypermethylated CpG islands using a quantile regression model |
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| Authors: | Shuying Sun Zhengyi Chen Pearlly S Yan Yi-Wen Huang Tim HM Huang Shili Lin |
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| Institution: | (1) Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, USA;(2) Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA;(3) Department of Statistics, Case Western Reserve University, Cleveland, Ohio, USA;(4) Human Cancer Genetics Program, The Ohio State University, Columbus, Ohio, USA;(5) Department of Statistics, The Ohio State University, Columbus, Ohio, USA |
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| Abstract: | 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. In this
paper, we attempt to determine which quantile level should be used to identify hypermethylated CGIs and their associated genes. |
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