RNA-Seq with Bioconductor in R

In this chapter we explore what we can do with RNA-Seq data and why it is exciting. We learn about the different steps and considerations involved in an RNA-Seq workflow.

In this chapter, we perform quality control on the RNA-Seq count data using heatmaps and principal component analysis. We explore the similarity of the samples to each other and determine whether there are any sample outliers.

In this chapter, we execute the differential expression analysis, generate results and identify the differentially expressed genes.

In this final chapter we explore the differential expression results using visualizations, such as heatmaps and volcano plots. We also review the steps in the analysis and summarize the differential expression workflow with DESeq2.