text: user3968: 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. 
  
  
  user4766: 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.
  
original_text: 3968: <p>I have a PCA plot from DESeq2's <code>plotPCA(vsd, intgroup=c("conditions"))</code> 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. </p>
  
  <p>The PCA plot I have is does not appear as I expect:</p>
  
  <p>1-Can I just remove a sample which I deem is the outlier and perform a reclustering of the rest on the PCA?</p>
  
  <p>2-What parameter can I use to declare that a point is an outlier rather than an in-group Biological variation?</p>
  
  <p>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.</p>
  
  <p>Thank you in advance for your kind hints. </p>
  
  <p><a href="https://i.stack.imgur.com/YWIiS.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/YWIiS.png" alt="PCA plot"></a></p>
   4766: <p>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.</p>
  
  <p>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 <code>intgroup</code>.</p>
  