plotMDS: Create double-labeled MDS plot from (merged) ExpressionSet
In inSilicoMerging: Collection of Merging Techniques for Gene Expression Data

Create Multidimensional Scaling (MDS) plot from ExpressionSet. Very similar to Principal Component Analysis (PCA) plots all samples are plotted in a two-dimensional space where both axis represent the two principle axis of expression variation. In this plot each sample can be labeled with a color and with a symbol.

1	plotMDS(eset, colLabel, symLabel, legend=TRUE, file=NULL, ...)

`eset`	ExpressionSet object.
`colLabel`	colname in pData(eset) to retrieve information for the labeling of samples with a color. All samples with the same value in pData(eset)[,colLabel] will share the same color.
`symLabel`	colname in pData(eset) to retrieve information for the labeling of samples with a symbol. All samples with the same value in pData(eset)[,symLabel] will share the same symbol.
`legend`	If TRUE a legend will be provided next to the MDS plot for both colLabel and symlabel.
`file`	If defined, the resulting plot will be stored as a pdf file instead of shown interactively.
`...`	Additional parameters for the 'plot' function (e.g. 'main').

# retrieve two datasets:
library(inSilicoDb);
InSilicoLogin("rpackage_tester@insilicodb.com", "5c4d0b231e5cba4a0bc54783b385cc9a");
eset1 = getDataset("GSE18842", "GPL570", norm="FRMA", features="gene");
eset2 = getDataset("GSE31547", "GPL96",  norm="FRMA", features="gene");
esets = list(eset1,eset2);

# merge them using no additional merging technique and the 'COMBAT' method:
library(inSilicoMerging)
eset_FRMA = merge(esets);
eset_COMBAT = merge(esets, method="COMBAT");

# check available annotations:
colnames(pData(eset_FRMA))
table(pData(eset_FRMA)[,"Disease"]);
table(pData(eset_FRMA)[,"Study"]);

# Visual inspection of the two merged datasets through an MDS plot
plotMDS(eset_FRMA, colLabel="Disease", symLabel="Study")
plotMDS(eset_COMBAT, colLabel="Disease", symLabel="Study")