# Exercises: Testing for differential expression I

Go to parent Introduction to R/Bioconductor for analysis of microarray data#Training Units

## Contents

## Estrogen: Effect of time

Identify genes that are differentially expressed between short and long incubation. Display the DE genes in an MA-plot or volcano plot.

## Atxn1

We want to compare expression between KOs and WTs in the E-MEXP-886 experiment. If you have not done so before, generate an expression set with the RMA expression values. Define a design matrix comparing the two groups of samples, and fit corresponding linear models to the expression data.

- How many probesets are significant at the unadjusted significance level of 5%? How many are significant after using the Bonferroni correction, and how many after FDR correction?
- Is the Ataxin 1 gene itself among the top regulated probesets? If not, where can we find it?

## Golub two-way comparison

Load the data set `Golub_Merge`

from the package `golubEsets`

. Identify probesets that are differentially expressed between the ALL and AML forms of leukemia. Generate an MA-plot and a volcanoplot for the results. Make sure that the expression data is log-transformed before you start the analysis.

## Effect of expression measure on DE

Re-run the estrogen data analysis twice, once with MAS5 expression values instead of RMA values, once with log2-transformed MAS5 values, and compare the results with the previous analysis.