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Microarray data normalization and transformation

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Abstract

Underlying every microarray experiment is an experimental question that one would like to address. Finding a useful and satisfactory answer relies on careful experimental design and the use of a variety of data-mining tools to explore the relationships between genes or reveal patterns of expression. While other sections of this issue deal with these lofty issues, this review focuses on the much more mundane but indispensable tasks of 'normalizing' data from individual hybridizations to make meaningful comparisons of expression levels, and of 'transforming' them to select genes for further analysis and data mining.

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Figure 1: An R-I plot displays the log2(Ri/Gi) ratio for each element on the array as a function of the log10(Ri*Gi) product intensities and can reveal systematic intensity-dependent effects in the measured log2(ratio) values.
Figure 2: Application of local (pen group) lowess can correct for both systematic variation as a function of intensity and spatial variation between spotting pens on a DNA microarray.
Figure 3: The use of replicates can help eliminate questionable or inconsistent data from further analysis.
Figure 4: Local variation as a function of intensity can be used to identify differentially expressed genes by calculating an intensity-dependent Z-score.

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Acknowledgements

The work presented here evolved from looking at a large body of data and would have been much less useful without the contributions of Norman H. Lee, Renae L. Malek, Priti Hegde, Ivana Yang, Shuibang Wang, Yonghong Wang, Simon Kwong, Heenam Kim, Wei Liang, Vasily Sharov, John Braisted, Alex Saeed, Joseph White, Jerry Li, Renee Gaspard, Erik Snesrud, Yan Yu, Emily Chen, Jeremy Hasseman, Bryan Frank, Lara Linford, Linda Moy, Tara Vantoai, Gary Churchill and Roger Bumgarner. J.Q. is supported by grants from the US National Science Foundation, the National Heart, Lung, and Blood Institute, and the National Cancer Institute. The MIDAS software system used for the normalization and data filtering presented here is freely available as either executable or source code from http://www.tigr.org/software, along with the MADAM data-management system, the Spotfinder image-processing software, and the MeV clustering and data-mining tool.

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  1. The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, 20850, Maryland, USA

    John Quackenbush

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  1. John Quackenbush
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Quackenbush, J. Microarray data normalization and transformation. Nat Genet 32 (Suppl 4), 496–501 (2002). https://doi.org/10.1038/ng1032

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