Remove batch effects
In the previous exercise, you demonstrated that the effect of batch had a larger impact on the variation compared to the effect of treatment. Fortunately, the olfactory stem cell study was perfectly balanced, i.e. each treatment was included in all 4 of the batches. Thus you can remove the variation introduced by the batch processing to increase the signal-to-noise ratio.
This exercise is part of the course
Differential Expression Analysis with limma in R
Exercise instructions
The ExpressionSet object eset
with the olfactory stem cell data has been loaded in your workspace.
- Use
removeBatchEffect
to remove the effect of the 4 batches from the data. - Use
plotMDS
to plot the principal components. Label the samples by the treatment they received. - Re-visualize the principal components, labeling the samples by their batch.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Load package
library(limma)
# Remove the batch effect
exprs(eset) <- ___(eset, batch = ___)
# Plot principal components labeled by treatment
___(eset, labels = ___, gene.selection = ___)
# Plot principal components labeled by batch
___(eset, labels = ___, gene.selection = ___)