### Data

``````library(DESeq2)
library(gplots)
library(RColorBrewer)
library(limma)
library(tidyverse)``````

### Challenge 1

1. Use the `DESeq2` function `rlog` to transform the count data. This function also normalises for library size.
2. Plot the count distribution boxplots with this data How has this effected the count distributions?
``````rlogcounts <- rlog(countdata)

statusCol <- as.numeric(factor(sampleinfo\$Status)) + 1
# Check distributions of samples using boxplots
boxplot(rlogcounts,
xlab="",
ylab="Log2(Counts)",
las=2,
col=statusCol)
# Let's add a blue horizontal line that corresponds to the median logCPM
abline(h=median(as.matrix(logcounts)), col="blue")``````

### Challenge 2

Plot the biased and unbiased MA plots both samples side by side to see the before and after normalisation.

``````par(mfrow=c(2,2))
plotMA(logcounts, array = 7, main="MCL1.LA - Raw")
abline(h=0,col="grey")
plotMA(logNormalizedCounts, array = 7, main="MCL1.LA - Normalised")
abline(h=0,col="grey")
plotMA(logcounts, array = 11, main="MCL1.LE - Raw")
abline(h=0,col="grey")
plotMA(logNormalizedCounts, array = 11, main="MCL1.LE - Normalised")
abline(h=0,col="grey")``````