# How do I differentiate outliers from in-group variations in a DESeq2 PCA plot of 9 samples distributed into 3 conditions?

I have a PCA plot from DESeq2's plotPCA(vsd, intgroup=c("conditions")) function. I have 9 samples distributed in to 3 groups of 3 biological replicates each. My reason for trying to visualise all on the same PCA plot is to see that samples from the same group (condition) cluster together and to see a separation between the groups.

The PCA plot I have is does not appear as I expect:

1-Can I just remove a sample which I deem is the outlier and perform a reclustering of the rest on the PCA?

2-What parameter can I use to declare that a point is an outlier rather than an in-group Biological variation?

3-I also wish to find out if it is allowed to plot more than two groups of sample at a time on a PCA plot.

As for using more than one groups when plotting PCA, I think you can just try passing a vector with many groups to the argument intgroup.