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d = dist(t(exprs(BSData.quantile)))
plot(hclust(d)
</pre>
==Differential Expression Analysis==
*Normalised data can be analysed using the limma package for statistical differences
*First define groups for each treatment. If a samplesheet was provided correctly and had this information:
<pre>samples = pData(BSData)$Sample_Group</pre>
*Otherwise define these groups manually in the order that they were entered (check by looking at pData(BSData)
<pre>samples = c("Control", "Control", "Treatment1", "Treatment1, "Treatment2"...)
*Next the groups are used to set up a statistical design:
<pre>
library(limma)
samples = as.factor(samples)
design = model.matrix(~0 + samples)
colnames(design) = levels(samples)
fit = lmFit(exprs(BSData.quantile), design)
</pre>
*Now set up contrast matrices to define how you want the data analyses. For example you may want to compare some treatments to a control, as well as between some treaments. See the limma user guide for more information about specific analyses. When defining the contrast matrix use the sample group names as defined above.
<pre>
cont.matrix = makeContrasts(Treatment1vsControl = Treatment1 - Control, Treatment2vsControl = Treatment2 - Control, Treatment1vsTreatment2 = Treatment1 - Treatment2, levels = design)
fit.cont = contrasts.fit(fit, cont.matrix)
ebFit = eBayes(fit.cont)
</pre>
