Answers.Body: <p>The first method (<code>resSig$log2FoldChange</code>) is for getting the most differentially expressed genes. These are the genes with the biggest differences between specific groups you pre-define.</p>
  
  <p>The second method (<code>rowVars(assay(rld))</code>) is for getting the highly variable genes. These are the genes most variable across all samples regardless of which samples they are.</p>
  
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Answers.CreationDate: 2018-04-13T00:38:42.683Z
Answers.LastActivityDate: 2018-04-13T00:38:42.683Z
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Body: <p>In the following <a href="https://f1000research.com/articles/4-1070/v2" rel="nofollow noreferrer">article</a>. There are two ways of getting highly variable genes:</p>
  
  <pre><code>resSig &lt;- subset(res, padj &lt; 0.1)
  head(resSig[ order(resSig$log2FoldChange, decreasing=TRUE), ])
  </code></pre>
  
  <p>Getting genes that are statistically significant, then ordering them by the <code>log2FoldChange</code> which is </p>
  
  <blockquote>
    <p>The column <em>log2FoldChange</em> is the effect size estimate. It tells us how much the gene’s expression seems to have changed due to treatment with dexamethasone in comparison to untreated samples.</p>
  </blockquote>
  
  <p>And the second one:</p>
  
  <pre><code>topVarGenes &lt;- head(order(rowVars(assay(rld)),decreasing=TRUE),20)
  </code></pre>
  
  <p>I am struggling to understand what are the differences between the two ways of getting the most variable genes. What is the difference?</p>
  
Tags: <rna-seq><deseq2>
Title: Highly variable genes analysis by DESeq2
ThreadText: # Highly variable genes analysis by DESeq2
  
  (Asked by: username_0 on 2018-04-12)
  
  In the following [article](https://f1000research.com/articles/4-1070/v2). There are two ways of getting highly variable genes:
  
  ```
  resSig <- subset(res, padj < 0.1)
  head(resSig[ order(resSig$log2FoldChange, decreasing=TRUE), ])
  
  ```
  
  Getting genes that are statistically significant, then ordering them by the `log2FoldChange` which is
  
  > The column *log2FoldChange* is the effect size estimate. It tells us how much the gene’s expression seems to have changed due to treatment with dexamethasone in comparison to untreated samples.
  
  And the second one:
  
  ```
  topVarGenes <- head(order(rowVars(assay(rld)),decreasing=TRUE),20)
  
  ```
  
  I am struggling to understand what are the differences between the two ways of getting the most variable genes. What is the difference?
  
  **Tags:** rna-seq, deseq2
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2018-04-13. Score: 4 | Accepted answer
  
  The first method (`resSig$log2FoldChange`) is for getting the most differentially expressed genes. These are the genes with the biggest differences between specific groups you pre-define.
  
  The second method (`rowVars(assay(rld))`) is for getting the highly variable genes. These are the genes most variable across all samples regardless of which samples they are.
  
  
  ---
  
  