Boxplots from summary data

Description

The standard boxplot function has been extended to work with the ExpressionSetIllumina class. Moreover, it generates graphics using the ggplot2 package and can incorporate user-defined factors into the plots.

Details

Extra factors can be added to the plots provided they are present in either the phenoData or featureData or the object.

Value

A ggplot object is produced and displayed on screen

Author(s)

Mark Dunning

Examples

    if(require(beadarrayExampleData)){

	data(exampleSummaryData)
	
	subset <- channel(exampleSummaryData, "G")[,1:8]

	boxplot(subset)

	boxplot(subset, what="nObservations")


	###You can use columns from the featureData in the plots. Here we will use the control-type

	head(fData(subset))

	table(fData(subset)[,"Status"])

	boxplot(subset, probeFactor = "Status")

	###Similarly, we group samples according to colums in phenoData

	pData(subset)


	boxplot(subset, SampleGroup = "SampleFac")

	##Both sample and probe factors can be combined into the same plot

	boxplot(subset, SampleGroup = "SampleFac", probeFactor = "Status")

	##Suppose we have found differentially expressed genes between experimental conditions and want to plot their response. This can be done by first subsetting the ExpressionSetIllumina object and then using the probeFactor and SampleGroup accordingly
      
	if(require(illuminaHumanv3.db)){
	
	   ids <- unlist(mget("ALB", revmap(illuminaHumanv3SYMBOL)))
	   subset2 <- subset[ids,]

	   boxplot(subset2, SampleGroup = "SampleFac")
	   boxplot(subset2, SampleGroup = "SampleFac", probeFactor = "IlluminaID")
	}

    }

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)

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'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

> library(beadarray)
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 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

Loading required package: Biobase
Welcome to Bioconductor

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

Loading required package: ggplot2
Welcome to beadarray version 2.22.2
beadarray versions >= 2.0.0 are substantial updates from beadarray 1.16.0 and earlier. Please see package vignette for details
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/beadarray/boxplot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: boxplot-methods
> ### Title: Boxplots from summary data
> ### Aliases: boxplot,ExpressionSetIllumina-method
> ### Keywords: methods
> 
> ### ** Examples
> 
>     if(require(beadarrayExampleData)){
+ 
+ 	data(exampleSummaryData)
+ 	
+ 	subset <- channel(exampleSummaryData, "G")[,1:8]
+ 
+ 	boxplot(subset)
+ 
+ 	boxplot(subset, what="nObservations")
+ 
+ 
+ 	###You can use columns from the featureData in the plots. Here we will use the control-type
+ 
+ 	head(fData(subset))
+ 
+ 	table(fData(subset)[,"Status"])
+ 
+ 	boxplot(subset, probeFactor = "Status")
+ 
+ 	###Similarly, we group samples according to colums in phenoData
+ 
+ 	pData(subset)
+ 
+ 
+ 	boxplot(subset, SampleGroup = "SampleFac")
+ 
+ 	##Both sample and probe factors can be combined into the same plot
+ 
+ 	boxplot(subset, SampleGroup = "SampleFac", probeFactor = "Status")
+ 
+ 	##Suppose we have found differentially expressed genes between experimental conditions and want to plot their response. This can be done by first subsetting the ExpressionSetIllumina object and then using the probeFactor and SampleGroup accordingly
+       
+ 	if(require(illuminaHumanv3.db)){
+ 	
+ 	   ids <- unlist(mget("ALB", revmap(illuminaHumanv3SYMBOL)))
+ 	   subset2 <- subset[ids,]
+ 
+ 	   boxplot(subset2, SampleGroup = "SampleFac")
+ 	   boxplot(subset2, SampleGroup = "SampleFac", probeFactor = "IlluminaID")
+ 	}
+ 
+     }
Loading required package: beadarrayExampleData
Loading required package: illuminaHumanv3.db
Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: IRanges
Loading required package: S4Vectors

Attaching package: 'S4Vectors'

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

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: org.Hs.eg.db


> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
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