1. Inspect the results
Now you need to inspect the differential expression results.
2. Inspect the results
In the previous exercises, you confirmed that doxorubicin had a large effect on the wild type mice, disregulating thousands of genes; whereas it had no impact on the Top2b null mice. This supports the original hypothesis that doxorubicin exerts its effect via Top2b. Note that although there were many significant genes for the interaction term as well, this appears to be mainly driven by the effect of doxorubicin in the wild type mice, as evidenced by the large overlap between the two contrasts in the Venn diagram.
Next you will need to further inspect these results.
3. Histograms of p-values
First you'll confirm that you properly modeled the experiment by plotting the histogram of p-values for each contrast. You obtain the p-values for every gene using the limma function `topTable`. While in the last chapter you did this for an experiment with only one contrast, for the doxorubicin experiment you'll need to specify which of the three contrasts you want to extract results for using the argument `coef`.
Recall that a null result should have a uniform distribution of p-values. Which of the three contrasts in the doxorubicin study do you expect to have a uniform distribution of p-values?
4. Volcano plot
Next you'll visualize the magnitude of differential expression and highlight a few of the top genes using the limma function `volcanoplot`. Recall that the x-axis is the log-fold change in expression between the contrasted groups of samples, and the y-axis is the log-odds of differential expression.
Similar to `topTable`, you will need to specify the contrast of interest to visualize in each plot. Pay special attention to the range of the x- and y-axes for each of the three contrasts. Do they correspond to the number of differentially expressed genes identified for each one?
5. Testing for KEGG enrichment
Lastly, you'll test for pathway-level changes in response to doxorucubin treatment using the limma function `kegga` and `topKEGG`. These will identify KEGG pathways that are enriched for differentially expressed genes more than expected by chance.
Like the other limma functions, you will need to specify the contrast to test. Since the contrast testing for differences in the Top2b null mice had zero differentially expressed genes, you will skip this one.
Also, you'll need to change the species for retrieving the pathways from the database from _Homo sapiens_ to _Mus musculus_, the scientific name for the house mouse.
6. Let's practice!
Now it's your turn to inspect the results of the doxorubicin study.