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Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation (2002)
| Citations: | 716 - 9 self |
BibTeX
@MISC{Yang02normalizationfor,
author = {Yee Hwa Yang and Sandrine Dudoit and Percy Luu and Vivian Peng},
title = {Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation},
year = {2002}
}
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Abstract
There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is often used to force the distribution of the intensity log ratios to have a median of zero for each slide. However, such global normalization approaches are not adequate in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments. The selection of appropriate controls for normalization is discussed and a novel set of controls (microarray sample pool, MSP) is introduced to aid in intensity-dependent normalization. Lastly, to allow for comparisons of expression levels across slides, a robust method based on maximum likelihood estimation is proposed to adjust for scale differences among slides.
Keyphrases
cdna microarray data multiple slide systematic variation robust composite method dye bias cdna microarray experiment intensity log ratio systematic variation normalization method scale difference appropriate control robust method spot overall intensity novel set maximum likelihood estimation intensity-dependent normalization fluorescent dye expression level robust local regression spatial location spatial dependence different type term normalization refers constant adjustment global normalization approach many source microarray sample pool measured gene expression level
