Answers.Body: <p>If using DESeq2 with GSEA, I'd recommend ranking by shrunk log2FC values.</p>
  
  <p>It'd also be worth considering ranking positive and negative associations separately, because the standard GSEA algorithm doesn't cross at the zero point when associations change from positive to negative.</p>
  
  <p>You shouldn't be using p-values to rank anything. A p-value gives information about the reliability of a result, but not its importance. See point #5 of this article about p-values:</p>
  
  <p><a href="https://amstat.tandfonline.com/doi/full/10.1080/00031305.2016.1154108#_i31" rel="nofollow noreferrer">https://amstat.tandfonline.com/doi/full/10.1080/00031305.2016.1154108#_i31</a></p>
  
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Answers.CreationDate: 2020-06-03T21:40:03.257Z
Answers.LastActivityDate: 2020-06-05T21:31:42.020Z
Answers.LastEditDate: 2020-06-05T21:31:42.020Z
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Body: <p>I am using fgsea in r to calculate and plot a bunch of GSEA graphs. </p>
  
  <p>My question is whether it is acceptable to use the Wald statistic from DeSeq2 to rank the gene list?</p>
  
  <p>I have seen in the GSEA application that the signal to noise ratio is used but is the Wald statistic sufficient?</p>
  
  <p>If not then is there an easyish way to calculate the signal to noise ratio in R given a normalised count matrix?</p>
  
  <p>Thanks in advance.</p>
  
Tags: <r><deseq2><gsea>
Title: Ranking metric in GSEA
ThreadText: # Ranking metric in GSEA
  
  (Asked by: username_0 on 2020-06-02)
  
  I am using fgsea in r to calculate and plot a bunch of GSEA graphs.
  
  My question is whether it is acceptable to use the Wald statistic from DeSeq2 to rank the gene list?
  
  I have seen in the GSEA application that the signal to noise ratio is used but is the Wald statistic sufficient?
  
  If not then is there an easyish way to calculate the signal to noise ratio in R given a normalised count matrix?
  
  Thanks in advance.
  
  **Tags:** r, deseq2, gsea
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2020-06-03. Score: 3
  
  If using DESeq2 with GSEA, I'd recommend ranking by shrunk log2FC values.
  
  It'd also be worth considering ranking positive and negative associations separately, because the standard GSEA algorithm doesn't cross at the zero point when associations change from positive to negative.
  
  You shouldn't be using p-values to rank anything. A p-value gives information about the reliability of a result, but not its importance. See point #5 of this article about p-values:
  
  <https://amstat.tandfonline.com/doi/full/10.1080/00031305.2016.1154108#_i31>
  
  
  ---
  
  