<p>You only have 4 samples total. I think it would be difficult to not have the PCA show big differences between the groups with so few points.</p>

<p>On the other hand, for differential expression, it is hard to get something to be statistically significant with only 2 replicates.</p>

CC BY-SA 4.0
2018-12-19T03:58:20.637Z
2018-12-19T03:58:20.637Z
burger
<p>I got a <code>PCA</code> plot of <code>bulk RNA-seq</code> experiment that looks the following way:</p>

<p><a href="https://i.sstatic.net/gbQ1P.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/gbQ1P.png" alt="enter image description here"></a></p>

<p>It was generated by the following code:</p>

<pre><code>pcaData &lt;- plotPCA(rld_sva, intgroup=c("Group"), returnData=TRUE)
percentVar &lt;- round(100 * attr(pcaData, "percentVar"))
ggplot(pcaData, aes(PC1, PC2, color=Group)) +
  geom_point(size=3) +
  xlab(paste0("PC1: ",percentVar[1],"% variance")) +
  ylab(paste0("PC2: ",percentVar[2],"% variance")) + 
  coord_fixed()
</code></pre>

<p>First <code>sva</code> correction was run to correct for batch effects and then <code>rlog</code> transformed values were plugged in to <code>plotPCA</code> function.</p>

<p>The first issue that catches the eye is that <code>100%</code> of the variance is explained by just 2 dimensions. I am not sure what can one say about the data in this case. The second issue is that I get only around <code>20</code> differentially expressed genes by using <code>DESeq2</code> analysis (<code>log2FoldChange &gt; 1</code>, <code>p_adj &lt; 0.05</code>). I know that we can not directly state that if there is a large difference on <code>PCA</code> there will be present a plenty of differentially expressed genes, but why is it not the case? Simple logic tells me that <code>pca</code> shows the difference between the samples in their gene expression, so I would expect seeing a plenty of differentially expressed genes.</p>

CC BY-SA 4.0
2018-12-18T22:18:11.897Z
Here each point is a sample or a cell? As you mention explaining 100% of variance in two dimensions would mean that an error happened somewhere. What have you done to find the root of this problem? And what is your question? If it is possible, if it is normal to happen something like this or something else?
llrs
CC BY-SA 4.0
2018-12-19T00:10:49.677Z
Sample. The question is why so much difference in pca does not lead to many differentially expressed genes. Second, minor question, is why the data is `2D`, possible reasons for that. Sorry, I made a mistake, it is `bulk RNA seq`
Nikita Vlasenko
CC BY-SA 4.0
2018-12-19T10:27:47.863Z
Your two IgG replicates are separated a lot in the PCA plot, compared with the two other  replicates, which likely means that there is too much disagreement within the IgG group in order for DESeq to arrive at low enough P-values for most of the differentially expressed genes. Adding one more IgG replicate would probably help a lot.
Peter Menzel
<scrnaseq><deseq2><pca>
PCA plot shows big difference but not many differentially expressed genes are found
# PCA plot shows big difference but not many differentially expressed genes are found

(Asked by: username_0 on 2018-12-18)

I got a PCA plot of bulk RNA-seq experiment that looks the following way:

It was generated by the following code:
pcaData <- plotPCA(rld_sva, intgroup=c("Group"), returnData=TRUE)
percentVar <- round(100 * attr(pcaData, "percentVar"))
ggplot(pcaData, aes(PC1, PC2, color=Group)) +
  geom_point(size=3) +
  xlab(paste0("PC1: ",percentVar[1],"% variance")) +
  ylab(paste0("PC2: ",percentVar[2],"% variance")) + 
  coord_fixed()

First sva correction was run to correct for batch effects and then rlog transformed values were plugged in to plotPCA function.
The first issue that catches the eye is that 100% of the variance is explained by just 2 dimensions. I am not sure what can one say about the data in this case. The second issue is that I get only around 20 differentially expressed genes by using DESeq2 analysis (log2FoldChange > 1, p_adj < 0.05). I know that we can not directly state that if there is a large difference on PCA there will be present a plenty of differentially expressed genes, but why is it not the case? Simple logic tells me that pca shows the difference between the samples in their gene expression, so I would expect seeing a plenty of differentially expressed genes.

**Tags:** scrnaseq, deseq2, pca
> **Comments:**
> **username_1**: Here each point is a sample or a cell? As you mention explaining 100% of variance in two dimensions would mean that an error happened somewhere. What have you done to find the root of this problem? And what is your question? If it is possible, if it is normal to happen something like this or something else?  
> **username_0**: Sample. The question is why so much difference in pca does not lead to many differentially expressed genes. Second, minor question, is why the data is `2D`, possible reasons for that. Sorry, I made a mistake, it is `bulk RNA seq`  
> **username_2**: Your two IgG replicates are separated a lot in the PCA plot, compared with the two other  replicates, which likely means that there is too much disagreement within the IgG group in order for DESeq to arrive at low enough P-values for most of the differentially expressed genes. Adding one more IgG replicate would probably help a lot.  

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## 1 answers

### Answer by username_3 on 2018-12-19. Score: 7 | Accepted answer

You only have 4 samples total. I think it would be difficult to not have the PCA show big differences between the groups with so few points.

On the other hand, for differential expression, it is hard to get something to be statistically significant with only 2 replicates.


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