Answers.Body: <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>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-12-19T03:58:20.637Z
Answers.LastActivityDate: 2018-12-19T03:58:20.637Z
Answers.UserDisplayName: burger
Body: <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>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-12-18T22:18:11.897Z
Comments.Text: 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?
Comments.UserDisplayName: llrs
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-12-19T00:10:49.677Z
Comments.Text: 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`
Comments.UserDisplayName: Nikita Vlasenko
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-12-19T10:27:47.863Z
Comments.Text: 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.
Comments.UserDisplayName: Peter Menzel
Tags: <scrnaseq><deseq2><pca>
Title: PCA plot shows big difference but not many differentially expressed genes are found
ThreadText: # 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:
  
  [![enter image description here](https://i.sstatic.net/gbQ1P.png)](https://i.sstatic.net/gbQ1P.png)
  
  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.  
  
  ---
  
  ## 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.
  
  
  ---
  
  