Join us for
RADAR: AI Edition
Learn R Programming
lumi (version 2.24.0)

plot-methods: Plot of a ExpressionSet object

Description

Creating quality control plots of a ExpressionSet object

Usage

"plot"(x, what = c("density", "boxplot", "pair", "MAplot", "sampleRelation", "outlier", "cv"), main, ...)

Arguments

x
a ExpressionSet object returned by lumiQ
what
one of the six kinds of QC plots
main
the title of the QC plot
...
additional parameters for the corresponding QC plots

Details

The parameter "what" of plot function controls the type of QC plots, which includes:
  • density: the density plot of the chips, see hist-methods
  • boxplot: box plot of the chip intensities, see boxplot-methods
  • pair: the correlation among chips, plot as a hierarchical tree, see pairs-methods
  • MAplot: the MAplot between chips, see MAplot-methods
  • sampleRelation: plot the sample relations. See plotSampleRelation
  • outlier: detect the outliers based on the sample distance to the center. See detectOutlier
  • cv: the density plot of the coefficients of variance of the chips. See estimateLumiCV

See Also

LumiBatch-class, hist-methods, boxplot-methods, MAplot-methods, pairs-methods, plotSampleRelation, estimateLumiCV, detectOutlier

Examples

Run this code

## load example data
data(example.lumi)

## Quality control estimation
lumi.Q <- lumiQ(example.lumi)

## summary
summary(lumi.Q)

## plot the density
plot(lumi.Q, what='density')

## plot the pairwise sample correlation
plot(lumi.Q, what='pair')

## plot the pairwise MAplot
plot(lumi.Q, what='MAplot')

## sample relations
plot(lumi.Q, what='sampleRelation', method='mds', color=c('100US', '95US:5P', '100US', '95US:5P'))

## detect outlier based on the distance to the mean profile
plot(lumi.Q, what='outlier')

## Density plot of coefficient of variance
plot(lumi.Q, what='cv')

Run the code above in your browser using DataLab