dyebias.trendplot {dyebias} | R Documentation

## Creates a trend-plot of all reporters, binned by dye bias, with all slides ordered by slide bias.

### Description

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.

Showing the total dye bias of all reporters is too overwhelming, therefore the medians of the total dye bias after binning by intrinsic gene specific dye bias (as given in `dyebias$dyebias`) are plotted.

### Usage

    dyebias.trendplot(data, iGSDBs, dyebias.percentile=5,
                      application.subset=TRUE, n.bins=20, order,
                      output=NULL, ylim=c(-1,1), cex=0.3, lty=1, lwd=1,
                      type="median", main="dye bias trend plot",
                      xlab="slide bias rank", ylab="M", sub=NULL, ...)

### Arguments

`data` | The `marrayNorm` to trendplot.
`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.
`application.subset` | The set of reporters that was eligible for dye bias correction; same argument as for `dyebias.apply.correction`.
`n.bins` | The number of bins into which to classify the reporters, based on their intrinsic gene-specific dye bias. The median of each bin is plotted.
`type` | What to print for each bin and hybridization. Valid values are: medianPlot the bin-median: the median of all genes in that bin and hybridization meanPlot the mean of all genes in that bin and hybridization genePlot the single gene that has the lowest squared distance to the bin-median over all hybridizations median.genePlot the single gene that has the median squared distance to the bin-medians over all hybridizations worst.genePlot the single gene that correlates worst with the bin-median over all hybridizations
`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 as a value. If `order!=NULL`, the slides are put into this order before trendplotting. 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.
`ylim, lty, lwd, main, sub, cex, xlab, ylab` | As for `matplot()`
`...` | Other arguments are passed on to `matplot()`.

### Value

The order obtained, for use in a later call to this same function.

### Author(s)

Philip Lijnzaad p.lijnzaad@umcutrecht.nl

### References

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.

### See Also

`dyebias.estimate.iGSDBs`, `dyebias.apply.correction`, `dyebias.rgplot`, `dyebias.maplot`, `dyebias.monotonicity` `dyebias.monotonicityplot`

### Examples

      ## show trend plots of uncorrected and corrected next to each other:
      ylim <- c(-0.6, 0.6)

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

      order <- dyebias.trendplot(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",
                            ylim=ylim
                            )

      order <- dyebias.trendplot(data=correction$data.corrected, # from dyebias.apply.correction
                            iGSDBs=iGSDBs.estimated,
                            order=order,             # order by the original slide bias
                            output=NULL,
                            main="after correction",
                            ylim=ylim
                           )

[Package dyebias version 1.48.0 Index]