I see what looks like an outlier sample in my PCA plot. What should I do? Should I exclude the sample from the analysis?

Modified on Thu, 8 Oct, 2020 at 5:55 PM

A PCA plot is a great way to find outliers. First and foremost, it gives you a starting point to try and figure out — why is that sample an outlier? That's the important question. Is it because there's something biologically unusual going on in the sample? Did one sample did not respond to the treatment and therefore looks similar to controls? Was the sample mislabeled? Or is it because something went wrong with the sample preparation, ex., the sample was accidentally left out overnight? 


PCA plot with a potentially mislabeled sample


The best answer we can give you is that while a PCA plot should be used as a starting point to try and figure out what’s going on, it doesn’t definitively tell you whether to exclude that sample from the analysis.


If you do determine that it is a bad sample and an extreme outlier, you can remove the sample and rerun the differential expression analysis again without that sample. Remember, you can run as many analyses on your data as you’d like at no additional charge.

Was this article helpful?

That’s Great!

Thank you for your feedback

Sorry! We couldn't be helpful

Thank you for your feedback

Let us know how can we improve this article!

Select at least one of the reasons
CAPTCHA verification is required.

Feedback sent

We appreciate your effort and will try to fix the article