A statistical framework for Illumina DNA methylation arrays

Pei Fen Kuan, Sijian Wang, Xin Zhou, Haitao Chu

Research output: Contribution to journalArticlepeer-review

68 Scopus citations

Abstract

Motivation: The Illumina BeadArray is a popular platform for profiling DNA methylation, an important epigenetic event associated with gene silencing and chromosomal instability. However, current approaches rely on an arbitrary detection P-value cutoff for excluding probes and samples from subsequent analysis as a quality control step, which results in missing observations and information loss. It is desirable to have an approach that incorporates the whole data, but accounts for the different quality of individual observations. Results: We first investigate and propose a statistical framework for removing the source of biases in Illumina Methylation BeadArray based on several positive control samples. We then introduce a weighted model-based clustering called LumiWCluster for Illumina BeadArray that weights each observation according to the detection P-values systematically and avoids discarding subsets of the data. LumiWCluster allows for discovery of distinct methylation patterns and automatic selection of informative CpG loci. We demonstrate the advantages of LumiWCluster on two publicly available Illumina GoldenGate Methylation datasets (ovarian cancer and hepatocellular carcinoma).

Original languageEnglish (US)
Article numberbtq553
Pages (from-to)2849-2855
Number of pages7
JournalBioinformatics
Volume26
Issue number22
DOIs
StatePublished - Nov 2010

Bibliographical note

Funding Information:
Funding: NCI (grants 5-P30-CA16086-34 and 1R21CA134368-01A1 to P.K. and H.C., in parts); Susan G. Komen Foundation (grant KG081397 to P.K. and H.C., in parts).

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