%0 Journal Article
%T Preprocessing differential methylation hybridization microarray data
%A Shuying Sun
%A Yi-Wen Huang
%A Pearlly S Yan
%A Tim HM Huang
%A Shili Lin
%J BioData Mining
%D 2011
%I BioMed Central
%R 10.1186/1756-0381-4-13
%X We study 20 different preprocessing methods, which are the combination of five background correction methods and four normalization methods. In order to compare these 20 methods, we evaluate their performance of identifying known methylated and un-methylated housekeeping genes based on two statistics. Comparison details are illustrated using breast cancer cell line and ovarian cancer patient methylation microarray data. Our comparison results show that different background correction methods perform similarly; however, four normalization methods perform very differently. In particular, all three different LOESS normalization methods perform better than the one without any normalization.It is necessary to do within-array normalization, and the two LOESS normalization methods based on specific DMH internal control probes produce more stable and relatively better results than the global LOESS normalization method.Microarray technology has been used extensively in genetic and epigenetic studies over the last ten years. Several microarray platforms are available including the single-channel Affymetrix oligonucleotide arrays, the two-color (or two-channel) cDNA arrays, and Agilent two color arrays. In the two-color use, which is the focus of this paper, two samples (or target genes) are labeled using two different fluorophores (usually a red fluorescent dye, Cy5, and a green fluorescent dye, Cy3) and hybridized simultaneously onto each probe (or spot) of the array (or chip). Then the arrays are laser-scanned and images are processed to obtain the data for analysis [1]. In general, the log ratio Cy5 over Cy3 at each probe is used as a measurement. With this microarray technology, studying thousands of genes simultaneously becomes possible. For example, gene expression, copy number variation, and methylation patterns have been widely studied using microarray technologies. However, due to some experimental artifacts, random noise and systematic variation do exist in such hig
%U http://www.biodatamining.org/content/4/1/13