<p>All that plots like this are telling you is that there are some genes that contribute more to the variability seen between your various samples than others. In an ideal world these genes will also be differentially expressed between your groups, but since we don't live in an ideal world that might not be the case. In general, the longer the line and the closer it is to the X axis the more it contributes to the overall variation (since it's contributing more the PC1, which is 53.5% of the variation in your data).</p>

<p>Since you've asked in the comments for a brief "results and description" section style summary of such a PCA I'll include something brief below. Note that one should not over-interpret PCA plots. The axes are (mathematically) orthogonal and don't necessarily correspond to anything biologically relevant.</p>

<blockquote>
  <p>We performed a PCA on the variance-stabilized counts to check for batch effects and overall clustering of the data. As can be seen, the "3T" and "5T" groups cluster together along the first principal component, while the "0T" and "1T" samples cluster on the opposite side. This suggests that these groups are more similar to each other, as further evidenced by ...</p>
</blockquote>

<p>Assuming you had used the <code>vst()</code> function in DESeq2 (I note you've tagged your post with RNAseq, so perhaps you're using DESeq2) then something like that above would be a reasonable description.</p>

CC BY-SA 4.0
2018-07-23T05:39:20.943Z
Thanks Dr. Ryan, May I ask you if I can have a example of clear description for PCA results.  I mean how can I put this kind of results in to words when writing an R&D. I have never seen clear interpretation of PCA results in papers I have gone through.
Dendrobium
CC BY-SA 4.0
2018-07-23T06:44:52.857Z
This ends up being rather specific to the topic. In general PCAs are just used for QC to see if there's something odd going on (e.g., an outlier sample or things clustering oddly). I'll update my answer with an description that vaguely fits your image, but be aware that it's best not to over-interpret PCA plots.
Devon Ryan
CC BY-SA 4.0
2018-07-23T10:11:32.377Z
Thanks heaps Dr. Ryan, This helps. I appreciate your kind help. No above PCA is from targeted proteomics data of the sample.
Dendrobium
CC BY-SA 4.0
2018-07-22T18:44:26.423Z
2018-07-23T06:50:04.617Z
2018-07-23T06:50:04.617Z
Devon Ryan
<p>How can I interpret the PCA results statistically for biological data?</p>

<p>I have used FactoMineR and factoextra libraries for PCA</p>

<p>Scripts used:</p>

<p>library(FactoMineR)</p>

<pre><code>res.PCA = PCA(df, scale.unit=TRUE, ncp=4, graph=F )
par(mfrow=c(1,2))
plot.PCA(res.PCA, axes=c(1, 2), choix="ind")
plot.PCA(res.PCA, axes=c(1, 2), choix="var")
dimdesc(res.PCA, axes=c(1,2))

library("factoextra")
fviz_pca_var(res.PCA, arrowsize = 1, labelsize = 3, repel = TRUE, col.var = "contrib", gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"))
</code></pre>

<p>Here is my PCA output of differnt KO genotypes); how can I explain/interpret the PCA output statistically and if possible biologically: esp, how this <b>colors /contrib explain</b></p>

<p><a href="https://i.sstatic.net/tAo03.png" rel="noreferrer"><img src="https://i.sstatic.net/tAo03.png" alt="enter image description here"></a></p>

CC BY-SA 4.0
2018-07-22T15:56:44.063Z
Oncidium, did you check it with the wikipedia page on pca or with the help page of the function? Also what kind of data have you used as input (this would help with the biological intepretation)?
llrs
CC BY-SA 4.0
2018-07-23T10:25:35.303Z
@Llopis, This PCA is from targeted proteomics data, sorry I have tagges rna-seq, it should be proteomics.
Dendrobium
<r><pca><proteomics>
How to interpret PCA output statistically and biologically?
# How to interpret PCA output statistically and biologically?

(Asked by: username_0 on 2018-07-22)

How can I interpret the PCA results statistically for biological data?
I have used FactoMineR and factoextra libraries for PCA
Scripts used:
library(FactoMineR)
res.PCA = PCA(df, scale.unit=TRUE, ncp=4, graph=F )
par(mfrow=c(1,2))
plot.PCA(res.PCA, axes=c(1, 2), choix="ind")
plot.PCA(res.PCA, axes=c(1, 2), choix="var")
dimdesc(res.PCA, axes=c(1,2))

library("factoextra")
fviz_pca_var(res.PCA, arrowsize = 1, labelsize = 3, repel = TRUE, col.var = "contrib", gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"))

Here is my PCA output of differnt KO genotypes); how can I explain/interpret the PCA output statistically and if possible biologically: esp, how this colors /contrib explain

**Tags:** r, pca, proteomics
> **Comments:**
> **username_1**: Oncidium, did you check it with the wikipedia page on pca or with the help page of the function? Also what kind of data have you used as input (this would help with the biological intepretation)?  
> **username_0**: @Llopis, This PCA is from targeted proteomics data, sorry I have tagges rna-seq, it should be proteomics.  

---

## 1 answers

### Answer by username_2 on 2018-07-22. Score: 5 | Accepted answer

All that plots like this are telling you is that there are some genes that contribute more to the variability seen between your various samples than others. In an ideal world these genes will also be differentially expressed between your groups, but since we don't live in an ideal world that might not be the case. In general, the longer the line and the closer it is to the X axis the more it contributes to the overall variation (since it's contributing more the PC1, which is 53.5% of the variation in your data).

Since you've asked in the comments for a brief "results and description" section style summary of such a PCA I'll include something brief below. Note that one should not over-interpret PCA plots. The axes are (mathematically) orthogonal and don't necessarily correspond to anything biologically relevant.

> We performed a PCA on the variance-stabilized counts to check for batch effects and overall clustering of the data. As can be seen, the "3T" and "5T" groups cluster together along the first principal component, while the "0T" and "1T" samples cluster on the opposite side. This suggests that these groups are more similar to each other, as further evidenced by ...

Assuming you had used the `vst()` function in DESeq2 (I note you've tagged your post with RNAseq, so perhaps you're using DESeq2) then something like that above would be a reasonable description.

> **Comments:**
> **username_0**: Thanks Dr. Ryan, May I ask you if I can have a example of clear description for PCA results.  I mean how can I put this kind of results in to words when writing an R&D. I have never seen clear interpretation of PCA results in papers I have gone through.  
> **username_2**: This ends up being rather specific to the topic. In general PCAs are just used for QC to see if there's something odd going on (e.g., an outlier sample or things clustering oddly). I'll update my answer with an description that vaguely fits your image, but be aware that it's best not to over-interpret PCA plots.  
> **username_0**: Thanks heaps Dr. Ryan, This helps. I appreciate your kind help. No above PCA is from targeted proteomics data of the sample.  

---

