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.

Thank you in advance for your kind hints.
Since you have so few points here, it is hard to tell which are outliers by just looking at the PCA plot.
I suggest you use correlation heatmap for your gene expression and check whether there are obvious outliers.

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.