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Last data update: 2014.03.03

R: Creates boxplots of the reporters with the strongest dye bias

dyebias.boxplot

R Documentation

Creates boxplots of the reporters with the strongest dye bias

Description

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.

Usage

dyebias.boxplot(data, iGSDBs, dyebias.percentile=5,
                application.subset=TRUE, order, output=NULL,
                ylim=c(-4,4), ...)

Arguments

`data`	The `marrayNorm` object to boxplot.
`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.
`application.subset`	The set of reporters that was eligible for dye bias correction; same argument as for `dyebias.apply.correction`.
`order`	If `order==FALSE`, no ordering of slides prior to boxplotting takes place. 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 an `order!=NULL`, the slides are put this order before boxplotting. This is typically done for a dye bias-corrected data set, using the order of the uncorrected set. (See also Fig. 3 in the paper).
`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`	As for `boxplot()`
`...`	Other arguments (such as `main`, etc.) are passed on to `boxplot()`.

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.

Examples


                                       

  ylim <- c(-1, 1)

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

  order <- dyebias.boxplot(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.boxplot(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
                        )

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(dyebias)
Loading required package: marray
Loading required package: limma
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following object is masked from 'package:limma':

    plotMA

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/dyebias/dyebias.boxplot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: dyebias.boxplot
> ### Title: Creates boxplots of the reporters with the strongest dye bias
> ### Aliases: dyebias.boxplot
> ### Keywords: hplot misc
> 
> ### ** Examples
> 
> 
>   ## Don't show: 
>      options(stringsAsFactors = FALSE)
> 
>      library(dyebias)
>      library(dyebiasexamples)
Loading required package: GEOquery
Setting options('download.file.method.GEOquery'='auto')
Setting options('GEOquery.inmemory.gpl'=FALSE)
>      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)
> 
>   
> ## End(Don't show)                                     
> 
>   ylim <- c(-1, 1)
> 
>   layout(matrix(1:2, nrow=1,ncol=2))
> 
>   order <- dyebias.boxplot(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.boxplot(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
+                         )
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>