Many two-colour hybridizations suffer from a dye bias that is both gene-specific and slide-specific. The former depends on the content of the nucleotide used for labeling; the latter depends on the labeling percentage. The slide-dependency was hitherto not recognized, and made addressing the artefact impossible. Given a reasonable number of dye-swapped pairs of hybridizations, or of same vs. same hybridizations, both the gene- and slide-biases can be estimated and corrected using the GASSCO method (Margaritis et al., Mol. Sys. Biol. 5:266 (2009), doi:10.1038/msb.2009.21)
Convenience function returning a subset of reporters that can be expected to be corrected reasonably well. Often, the logical AND of this set and that of maW(data.norm) == 1.0 is used. The resulting subset is passed as the application.subset-argument to dyebias.apply.correction.
dyebias.boxplot
(Package: dyebias) :
Creates boxplots of the reporters with the strongest dye bias
The aim of this routine is to show the magnitude of the dye bias across the data set, as well as the extent to which the GASSCO method could get rid of it. Typically, two boxplots would be shown, one before, one after dye bias correction. For esthetic reasons, the boxplots are usually ordered by the overal slide bias of the uncorrected data set. See also Margaritis et al. (2009), Fig. 1 and 3.
If you order genes by their iGSDB, and hybridizations by slide bias, the graphs of each gene should form a 'fan' out of the origin (see also dyebias.trendplot). This function gives measure of the extent to which this is true.
If you order genes by their iGSDB, and hybridizations by slide bias, the graphs of each gene should form a 'fan' out of the origin (see also dyebias.trendplot). This function plots the regression slope of each gene, ordered by iGSDB and slide bias. If the uncorrected total dye bias is indeed monotonous, an increasing trend should be visible.
dyebias.rgplot
(Package: dyebias) :
Produce scatterplots of the hybridization, with strongest dye
Plots the log_2(R) vs. log_2(G) (or alternatively M vs. A) signal of one slide, highlighting the reporters with the strongest red and green dye bias. Two lines indicate two-fold change. See also Margaritis et al. (2009), Fig. 1
dyebias.trendplot
(Package: dyebias) :
Creates a trend-plot of all reporters, binned by dye bias,
The aim of this routine is to show the monotonicity of the total dye bias in the (uncorrected) data set. This is to judge whether the total dye bias of one reporter in one hybridization indeed behaves as the product of an intrinsic gene specific dye bias (iGSDB) and a slide specific factor (the slide bias), which is at the heart of the GASSCO method.
providing 
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