Normalizing and filtering

Differential Expression Analysis with limma in R

John Blischak

Instructor

Pre-processing steps

  • Log transform

  • Quantile normalize

  • Filter

Differential Expression Analysis with limma in R

Visualization

library(limma)

# Plot distribution of each sample
plotDensities(eset, legend = FALSE)

Differential Expression Analysis with limma in R

Log transform

100 - 1
.1 - .001

99
0.099

log(100) - log(1)

4.60517

log(.1) - log(.001)

4.60517

# Log tranform
exprs(eset) <- log(exprs(eset))
plotDensities(eset, legend = FALSE)

Differential Expression Analysis with limma in R

Quantile normalize

# Quantile normalize
exprs(eset) <- normalizeBetweenArrays(exprs(eset))

plotDensities(eset, legend = FALSE)

Differential Expression Analysis with limma in R

Filter genes

# View the normalized data
plotDensities(eset, legend = FALSE)
abline(v = 5)


# Create logical vector
keep <- rowMeans(exprs(eset)) > 5
# Filter the genes
eset <- eset[keep, ]
plotDensities(eset, legend = FALSE)

Differential Expression Analysis with limma in R

Let's practice!

Differential Expression Analysis with limma in R

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