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A Distribution-Free Convolution Model For Background Correction Of Oligonucleotide Microarray Data, Zhongxue Chen, Monnie Mcgee, Qingzhong Liu, Megan Kong, Youping Deng, Richard H. Scheuermann
A Distribution-Free Convolution Model For Background Correction Of Oligonucleotide Microarray Data, Zhongxue Chen, Monnie Mcgee, Qingzhong Liu, Megan Kong, Youping Deng, Richard H. Scheuermann
Faculty Publications
Introduction
Affymetrix GeneChip® high-density oligonucleotide arrays are widely used in biological and medical research because of production reproducibility, which facilitates the comparison of results between experiment runs. In order to obtain high-level classification and cluster analysis that can be trusted, it is important to perform various pre-processing steps on the probe-level data to control for variability in sample processing and array hybridization. Many proposed preprocessing methods are parametric, in that they assume that the background noise generated by microarray data is a random sample from a statistical distribution, typically a normal distribution. The quality of the final results depends …