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Improving the Identification of Differentially Expressed Genes in cDNA Microarray Experiments

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Abstract

The identification of differentially expressed genes in DNA microarray experiments has led to promising results in DNA array analysis. The identification as well as many other methods in cDNA array analysis rely on correct calculations of differential colour intensity. It is shown that the calculation of logarithms of the ratio of the two color intensities (LogRatio) has several disadvantages. The effects of numerical instabilities and rounding errors are demonstrated on published data. As an alternative to LogRatio calculation, relative differences (RelDiff) are proposed. The stability against numerical and rounding errors of RelDiffs are demonstrated to be much better than for LogRatios. RelDiff values are linearly proportional to LogRatios for the range where genes are not differentially expressed. Relative differences map differential expression to a finite range. For most subsequent analysis this is a big advantage, in particular for the search of expression patterns. It has been reported that the variance of intensity measurements is a nonlinear function on intensity. This effect can be explained by an additive measurement error with constant variance. Applying the logarithm to such intensity measurements introduces the presumed nonlinear dependence. Thus in many cases no complicated variance stabilization transformation using nonlinear functions on the LogRatio expression values is necessary.

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References

  • HAIN, T. and ULTSCH, A. (2003): MDEAT-A new databionic evaluation and analysis tool to identify the virulence regulon of Listeria monocytogenes as a model system, Proc. European Conference on Prokaryotic Genomes, Göttingen, 2003.

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  • PARMIGIANI, G. et al. (2003): The Analysis of Gene Expression Data. Springer, New York.

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© 2005 Springer-Verlag Berlin · Heidelberg

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Ultsch, A. (2005). Improving the Identification of Differentially Expressed Genes in cDNA Microarray Experiments. In: Weihs, C., Gaul, W. (eds) Classification — the Ubiquitous Challenge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28084-7_43

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