PCA analysis
To continue with the quality assessment of our samples, in the first part of this exercise, we will perform PCA to look how our samples cluster and whether our condition of interest corresponds with the principal components explaining the most variation in the data. In the second part, we will answer questions about the PCA plot.
To assess the similarity of the smoc2 samples using PCA, we need to transform the normalized counts then perform the PCA analysis. Assume all libraries have been loaded, the DESeq2 object created, and the size factors have been stored in the DESeq2 object, dds_smoc2.
This exercise is part of the course
RNA-Seq with Bioconductor in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Transform the normalized counts
vsd_smoc2 <- vst(dds_smoc2, blind = TRUE)
# Plot the PCA of PC1 and PC2
___(___, intgroup=___)