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.

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Differential Expression Analysis with limma in R

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Övningsinstruktioner

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.

Interaktiv övning med praktiskt arbete

Testa den här övningen genom att slutföra den här exempelkoden.

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 = ___)

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