text: user2005: 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.
  
  user35: 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.
  
original_text: 2005: <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.stack.imgur.com/gbQ1P.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/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>
   35: <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>
  