Filter genes

Now that the data have been log-transformed and quantile-normalized, you need to remove the lowly expressed genes that are not relevant to the system being studied.

This exercise is part of the course

Differential Expression Analysis with limma in R

View Course

Exercise instructions

The ExpressionSet object eset_norm with the normalized Populus data has been loaded in your workspace.

Use plotDensities to visualize the distribution of gene expression levels for each sample. Disable the legend.
Use rowMeans to determine which genes have a mean expression level greater than 5. Name this logical vector keep.
Filter the genes (i.e. rows) of the ExpressionSet object with the logical vector keep and re-visualize.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

library(limma)

# Create new ExpressionSet to store filtered data
eset <- eset_norm

# View the normalized gene expression levels
___(eset, legend = ___); abline(v = 5)

# Determine the genes with mean expression level greater than 5
keep <- ___(exprs(eset)) > ___
sum(keep)

# Filter the genes
eset <- eset[___]
___(eset, legend = ___)

Edit and Run Code