1. Conclusion
Great job! You performed an entire differential expression analysis. Let's review what you've learned in this course.
2. Pre-processing features
You learned how to visualize the distribution of gene expression levels with `plotDensities`, and prepare the raw measurements with log transformation, quantile normalization, and filtering.
3. Visualize single genes with boxplot
You learned how to visualize single genes using the R plotting function `boxplot`. Furthermore, to do this, you took advantage of the subsetting capabilities of the Bioconductor S4 class ExpressionSet, which stores all the information on an experiment in a coordinated fashion.
4. Check sources of variation
You also learned how to use principal components analysis with `plotMDS` to determine if the main sources of variation in the data set are driven by the variables of interest. If they're instead correlated with technical variables, you learned how to use `removeBatchEffect` to remove this technical variation.
5. Flexibly testing various study designs
For testing, you learned how to use the versatile group-means parametrization to fit a single coefficient per study group and construct interpretable contrasts that are easier to read and understand by you and others compared to more traditional modelling approaches.
6. Histograms of p-values
As a quality control metric, you learned to inspect the distribution of p-values to ensure that null results, like the response of Top2b null mice to doxorubicin, had a uniform distribution, whereas strong results like the response of wild type mice were enriched for low p-values.
7. Volcano plots
To assess the magnitude of the gene expression changes and highlight top genes, you learned to create volcano plots and compare the ranges of the axes between the contrasts.
8. Test for enrichment of gene sets
Lastly, you learned to gain a systems-level perspective of your results by testing for enrichment of differentially expressed genes in databases of known gene sets like KEGG pathways and Gene Ontology categories.
9. Congrats on completing the course!
Great job completing the course. Now you are prepared to analyze your own gene expression data.