Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation

TLDR

cDNA microarray experiments suffer from systematic variation, such as dye‑labeling efficiency differences, and conventional global normalization methods that force median‑zero log‑ratio distributions are inadequate when dye biases depend on spot intensity or spatial location. The study proposes robust local‑regression normalization methods that adjust for intensity‑ and spatial‑dependent dye biases across various cDNA microarray experiments. The authors introduce a microarray sample pool (MSP) as a novel control for intensity‑dependent normalization and propose a maximum‑likelihood method to adjust slide‑scale differences for cross‑slide comparisons. The study introduces the MSP control set and a maximum‑likelihood scale‑adjustment method, improving intensity‑dependent normalization and cross‑slide comparability.

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.