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

View Course

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 = ___)

Edit and Run Code