Normalisation: correcting systematic biases, e.g., differential dye labelling
Variance Stablization Normalization (vsn)
For each dye and microarray, the background fluorescence and a factor reflecting overall brightness are inferred to make the signals identical for this subset of non-differentially expressed genes. A necessary assumption is that more than half the genes will not be differentially expressed. The method we employ is closely based on the work published by Huber et al. (2002).
For further information on this method please refer to Huber W., von Heydebreck A., Sultman H., Poustka A., and Vingron M. (2002). Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18(Suppl.1), S96-S104. PMID 12169536 (abstract).
Normalisation (VSN) of an entire replica group
Key
M = channel 2 - channel 1
A = (channel 2 + channel 1) / 2
Loess and Quantile Normalization (lquant)
Loess and Quantile normalization are employed to perform intra-microarray global loess normalisation of the M-values, followed by inter-microarray quantile normalisation. These normalisation methods are implemented in the Bioconductor limma (Linear Models for Microarray Data) software package.
Loess normalisation assumes that the bulk of probes on the microarray are not differentially expressed. Loess does not assume equal numbers of up- and/or down-regulated genes or that differential expression is symmetric about zero. For further information on this method please refer to Yang, D., Dudoit, S., Luiu, P., Lin, D.M., Peng, V., Ngai, J., Speed, T.P. (2002). Noamlization for cDNA microarray data: a robust composite method for addressing single and multiple slide systematic variation. Nucleic Acids Research 30(4), e15. PMID 11842121 (abstract).
The aim of quantile normalisation is to ensure that all the intensity distributions on each array are identical and involves an initial microarray-specific centering of the data, with the centred data being subsequently ordered from lowest to highest. A distribution is then calculated, whereby the lowest value is the average of the lowest expressed gene on each of the arrays. This calculation is repeated for each subsequent order of intensity values up to the average value of the highs from each of the microarrays. Each measurement on each microarray is then replaced with the corresponding average value in the distribution. For further (technical) information, please see Bolstad, B.M., Irizarry, R.A., Astrand, M., Speed, T.P. (2003). A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2), 185-93. PMID 16646809 (abstract).
Quantile Normalization (quant)
Performs an inter-microarray quantile normalisation that does not correct intra-microarray biases. This normalisation method is implemented in the Bioconductor limma software package. For further (technical) information, please see Bolstad, B.M., Irizarry, R.A., Astrand, M., Speed, T.P. (2003). A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2), 185-93. PMID 12538238 (abstract).
