dyebias.monotonicityplot: Show the degree of monotonicity of the dye bias across the...
In dyebias: The GASSCO method for correcting for slide-dependent gene-specific dye bias

Description Usage Arguments Details Value Note Author(s) References See Also Examples

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.

This function has been depracated, as it is of limited use and takes too long to compute.

  dyebias.monotonicityplot(data,
                           iGSDBs,
                           dyebias.percentile = 5,
                           order = NULL,
                           output = NULL,
                           pch = 19, cex = 0.3, cex.lab = 1.4,
                           ylim = c(-0.2, 0.2),
                           xlab = "rank", ylab = "slope",
                           sub = NULL,
                           ...)

`data`	The `marrayNorm` to determine the degree of monotonicity for
`iGSDBs`	A data frame with intrinsic gene-specific dye biases, the same as that used in `dyebias.apply.correction`, probably returned by `dyebias.estimate.iGSDBs`; see there for documentation.
`dyebias.percentile`	The percentile of intrinsic gene specific dye biases (iGSDBs) for which to highlight the reporters. Default should suffice in almost all cases.
`order`	If `order==NULL`, the slides are sorted by increasing slide bias prior to boxplotting. This is typically done for data that is not yet dye bias corrected. (This order is also returned in the object returned). If `order!=NULL`, the slides are put into this order first. This is typically done for a dye bias-corrected data set, using the order of the uncorrected set.
`output`	Specifies the output. If `NULL`, the existing output device is used; if `output` is one of `"X11", "windows", "quartz"`, a new X11 (Unix)/windows (Windows)/quartz (Mac) device is created. If `output` is a string ending in one of `".pdf", ".png", ".eps", ".ps"` is given, a file of that name and type is created and closed afterwards.
`pch, cex, cex.lab, ylim, xlab, ylab`	As for `plot()`
`sub`	The subtitle. If `sub==NULL`, a string giving the tau and p-value of the Mann-Kendall test is used; otherwise as for `plot()`
`...`	Other arguments are passed on to `plot()`

The total dye bias appears to be the product of iGSDB and slide bias. In other words, it is monotonous (always increasing or always decreasing), both with respect to the intrinsic gene specific dye bias and with respect to the slide bias. This function orders genes by their iGSDB and the slides by slide bias. Subsequently a linear regression of each gene is done, with x being the slide bias rank (not the slide bias itself), and y being the M. The slopes of each linear regression line should be an increasing array of values, representing the 'fan' of lines. The array of slopes is plotted (versus the rank). Generally, a clear trend is visible for uncorrected hybridizations, and the trend has disappeared after dye bias correction.

The order of the slide bias is returned, for use in plotting the behaviour of the regression slopes in the corrected data set.

This function takes very long to compute, since it calculates regressions for each gene.

Philip Lijnzaad p.lijnzaad@umcutrecht.nl

Margaritis, T., Lijnzaad, P., van Leenen, D., Bouwmeester, D., Kemmeren, P., van Hooff, S.R and Holstege, F.C.P. (2009). Adaptable gene-specific dye bias correction for two-channel DNA microarrays. Molecular Systems Biology, 5:266, 2009. doi: 10.1038/msb.2009.21.

dyebias.monotonicity, dyebias.trendplot

  ## Not run: 
     options(stringsAsFactors = FALSE)

     library(dyebias)
     library(dyebiasexamples)
     data(data.raw)
     data(data.norm)

     ### obtain estimate for the iGSDBs:
     iGSDBs.estimated <- dyebias.estimate.iGSDBs(data.norm,
                                                 is.balanced=TRUE,
                                                 verbose=FALSE)

     ### choose the estimators and which spots to correct:
     estimator.subset <- dyebias.umcu.proper.estimators(maInfo(maGnames(data.norm)))

     application.subset <- maW(data.norm) == 1 &
                   dyebias.application.subset(data.raw=data.raw, use.background=TRUE)

     ### do the correction:
     correction <- dyebias.apply.correction(data.norm=data.norm,
                                            iGSDBs = iGSDBs.estimated,
                                            estimator.subset=estimator.subset,
                                            application.subset = application.subset,
                                            verbose=FALSE)

  layout(matrix(1:2, nrow=1,ncol=2))

  order <- dyebias.monotonicityplot(data=data.norm, 
                        iGSDBs=iGSDBs.estimated, # from e.g. dyebias.estimate.iGSDBs
                        order=NULL,              # i.e., order by increasing slide bias
                        output=NULL,
                        main="before correction"
                        )

  order <- dyebias.monotonicityplot(data=correction$data.corrected,
                        iGSDBs=iGSDBs.estimated,
                        order=order,             # order by the original slide bias
                        output=NULL,
                        main="after correction"
                       ) 
  
## End(Not run)