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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.

Este exercício faz parte do curso

Differential Expression Analysis with limma in R

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Instruções do exercício

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.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

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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