Normalize

Raw gene expression data is messy, especially since many genes will not be relevant for the system you are studying. After receiving a new data set, the first step is to visualize the data and perform the necessary pre-processing steps.

This exercise is part of the course

Differential Expression Analysis with limma in R

View Course

Exercise instructions

The ExpressionSet object eset_raw with the raw Populus data has been loaded in your workspace.

Use plotDensities to visualize the distribution of gene expression levels for each sample. Disable the legend.
Log transform the measurements and re-visualize.
Quantile normalize the measurements with normalizeBetweenArrays 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 normalized data
eset_norm <- eset_raw

# View the distribution of the raw data
___(eset_norm, legend = ___)

# Log tranform
exprs(eset_norm) <- ___(exprs(eset_norm))
___(eset_norm, legend = ___)

# Quantile normalize
exprs(eset_norm) <- ___(exprs(eset_norm))
___(eset_norm, legend = ___)

Edit and Run Code