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Open Access Methodology article

Two-stage normalization using background intensities in cDNA microarray data

Dankyu Yoon1, Sung-Gon Yi2, Ju-Han Kim3 and Taesung Park2*

Author Affiliations

1 Program in Bioinformatics, Seoul National University, San56-l, Shin Lim-Dong, Kwan Ak-Ku, Seoul 151-747, Republic of Korea

2 Department of Statistics, College of Natural Science, Seoul National University, San56-l, Shin Lim-Dong, Kwan Ak-Ku, Seoul 151-747, Republic of Korea

3 SNUBI: Seoul National University Biomedical Informatics, Seoul National University School of Medicine, 28 Yongon-dong Chongno-gu, Seoul 110-799, Republic of Korea

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BMC Bioinformatics 2004, 5:97  doi:10.1186/1471-2105-5-97

Published: 21 July 2004

Abstract

Background

In the microarray experiment, many undesirable systematic variations are commonly observed. Normalization is the process of removing such variation that affects the measured gene expression levels. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization. One major source of variation is the background intensities. Recently, some methods have been employed for correcting the background intensities. However, all these methods focus on defining signal intensities appropriately from foreground and background intensities in the image analysis. Although a number of normalization methods have been proposed, no systematic methods have been proposed using the background intensities in the normalization process.

Results

In this paper, we propose a two-stage method adjusting for the effect of background intensities in the normalization process. The first stage fits a regression model to adjust for the effect of background intensities and the second stage applies the usual normalization method such as a nonlinear LOWESS method to the background-adjusted intensities. In order to carry out the two-stage normalization method, we consider nine different background measures and investigate their performances in normalization. The performance of two-stage normalization is compared to those of global median normalization as well as intensity dependent nonlinear LOWESS normalization. We use the variability among the replicated slides to compare performance of normalization methods.

Conclusions

For the selected background measures, the proposed two-stage normalization method performs better than global or intensity dependent nonlinear LOWESS normalization method. Especially, when there is a strong relationship between the background intensity and the signal intensity, the proposed method performs much better. Regardless of background correction methods used in the image analysis, the proposed two-stage normalization method can be applicable as long as both signal intensity and background intensity are available.