A Multivariate Method for Normalization in Affymetrix Oligonucleotide Microarray Experiments

10.6339/JDS.2010.08(4).632

Zhi-De Fang；Xiao-Hu Li；Liz-He Xu

Affymetrix GeneChip ； normalization ； oligonucleotide ； partial least squares regression

Journal of Data Science

8卷4期（2010 / 10 / 01）

505 - 519

英文

Affymetrix high-density oligonucleotide microarray makes it possible to simultaneously measure, and thus compare the expression profiles of hundreds of thousands of genes in living cells. Genes differentially expressed in different conditions are very important to both basic and medical research. However, before detecting these differentially expressed genes from a vast number of candidates, it is necessary to normalize the microarray data due to the significant variation caused by non-biological factors. During the last few years, normalization methods based on probe level or probeset level intensities were proposed in the literature. These methods were motivated by different purposes. In this paper, we propose a multivariate normalization method, based on partial least squares regression, aiming to equalize the central tendency, reduce and equalize the variation of the probe level intensities in any probeset across the replicated arrays. By so doing, we hope that one can precisely estimate the gene expression indexes.