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Exercise

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.

Instructions 1/2

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  • Run the code to transform the normalized counts.
  • Perform PCA by plotting PC1 vs PC2 using the DESeq2 plotPCA() function on the DESeq2 transformed counts object, vsd_smoc2 and specify the intgroup argument as the factor to color the plot.