<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>

CC BY-SA 4.0
2020-03-23T18:49:55.090Z
2020-03-23T18:49:55.090Z
Phoenix Mu
<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.sstatic.net/YWIiS.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/YWIiS.png" alt="PCA plot"></a></p>

<rna-seq><deseq2>
How do I differentiate outliers from in-group variations in a DESeq2 PCA plot of 9 samples distributed into 3 conditions?
# How do I differentiate outliers from in-group variations in a DESeq2 PCA plot of 9 samples distributed into 3 conditions?

(Asked by: username_0 on 2020-03-19)

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.

[![PCA plot](https://i.sstatic.net/YWIiS.png)](https://i.sstatic.net/YWIiS.png)

**Tags:** rna-seq, deseq2

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## 1 answers

### Answer by username_1 on 2020-03-23. Score: 1

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`.


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