boxplot-methods {beadarray} | R Documentation |
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.
Extra factors can be added to the plots provided they are present in either the phenoData
or featureData
or the object.
A ggplot
object is produced and displayed on screen
Mark Dunning
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") } }