Assuming you are using `princomp()`

, the PC scores are stored in `pca_res$scores`

so you can use these (e.g. `pca_res$scores[, 1]`

for PC1) and investigate any correlation (simple correlation may not do the trick though, see the answer here: https://stats.stackexchange.com/questions/115032).

If you're using `plotPCA()`

for the PCA data (that would be `pca_res <- plotPCA(obj, returnData = TRUE)`

, a similar approach should work since this returns the PC1 and PC2 scores as well.

`?DEseq2::plotPCA`

says "intgroup: interesting groups: a character vector of names in colData(x) to use for grouping". This serves to label the samples in the PCA plot by group (default value is "condition"). If there exists a column in `colData(obj)`

named "my_group" consisting of the values "G1", "G2" and "G3", setting `intgroup = "my_group"`

will result in labeling the samples into these 3 groups: