Answers.Body: <p>GSEA is most commonly used for gene <em>differential</em> expression, rather than gene expression, where genes are ranked based on the degree to which their expression differs between conditions.</p>
  <p>The score that a gene contributes to the total enrichment score is based on this differential expression, with upregulated genes increasing the score, and downregulated genes decreasing the score. The overall enrichment score can be used to indicate whether the pathway as a whole (or more precisely, whether the matched genes in the pathway) is upregulated or downregulated in the dataset.</p>
  <p>However, as you point out, GSEA is similar to a correlation analysis in that it does not provide a complete picture of what's going on under the hood. You've got to know what you're feeding into it to be able to properly interpret its output.</p>
  <p>When I use GSEA, I prefer to calculate enrichment scores for upregulated and downregulated genes separately, which helps a bit in situations where the signal from upregulated genes can swamp out the signal from downregulated genes (and vice versa). The R package <code>fgsea</code> has a usage parameter <code>scoreType</code> that can be set to <code>pos</code> and <code>neg</code> for doing this.</p>
  <p>There's no universal set of sets that should be used for all GSEA. I have seen lots of gene sets with names like &quot;Downregulated in colorectal cancer&quot;, or &quot;Upregulated in cellular proliferation&quot;; it sounds like those are the types of sets that you want to use, rather than the more general curated sets that include all involved genes.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-12-07T06:34:41.347Z
Answers.LastActivityDate: 2024-12-07T06:34:41.347Z
Answers.UserDisplayName: gringer
Body: <p>I've been using pathway analysis methods like <strong>GSEA</strong> and <strong>GSVA</strong>, which evaluate pathway activity based on gene expression levels. However, these methods seem to assume that the more highly expressed a gene is, the more it contributes to pathway activation. This seems to oversimplify how many biological pathways work, where genes within the same pathway can have different activation requirements:</p>
  <ul>
  <li>Some genes (e.g., activators) need to be upregulated for a pathway to be active.</li>
  <li>Others (e.g., inhibitors or regulatory genes) need to be downregulated or remain at baseline levels for proper activation.</li>
  </ul>
  <p>Given this, I’m wondering:</p>
  <ol>
  <li>Are there any methods or modifications to GSEA or GSVA that account for the fact that some genes in a pathway may need to be downregulated or have specific expected activation levels for the pathway to function properly?</li>
  <li>Alternatively, are there tools that incorporate the biological roles of genes (activators vs. inhibitors) into pathway analysis, rather than just relying on overall expression levels?</li>
  </ol>
  <p>Also:</p>
  <ol>
  <li>Do public databases like KEGG contain information on the gene role within a pathway? For example, this gene should be highly expressed, or lowly expressed?
  I’m curious how others approach this issue and whether there are tools or methods that explicitly address it. Any insights or suggestions would be greatly appreciated!</li>
  </ol>
  
Tags: <gsea><pathway><go-enrichment>
Title: How Can Pathway Analysis Methods Account for Genes with Different Activation Roles?
ThreadText: # How Can Pathway Analysis Methods Account for Genes with Different Activation Roles?
  
  (Asked by: username_0 on 2024-12-06)
  
  I've been using pathway analysis methods like **GSEA** and **GSVA**, which evaluate pathway activity based on gene expression levels. However, these methods seem to assume that the more highly expressed a gene is, the more it contributes to pathway activation. This seems to oversimplify how many biological pathways work, where genes within the same pathway can have different activation requirements:
  
  * Some genes (e.g., activators) need to be upregulated for a pathway to be active.
  * Others (e.g., inhibitors or regulatory genes) need to be downregulated or remain at baseline levels for proper activation.
  
  Given this, I’m wondering:
  
  1. Are there any methods or modifications to GSEA or GSVA that account for the fact that some genes in a pathway may need to be downregulated or have specific expected activation levels for the pathway to function properly?
  2. Alternatively, are there tools that incorporate the biological roles of genes (activators vs. inhibitors) into pathway analysis, rather than just relying on overall expression levels?
  
  Also:
  
  1. Do public databases like KEGG contain information on the gene role within a pathway? For example, this gene should be highly expressed, or lowly expressed?
     I’m curious how others approach this issue and whether there are tools or methods that explicitly address it. Any insights or suggestions would be greatly appreciated!
  
  **Tags:** gsea, pathway, go-enrichment
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-12-07. Score: 0 | Accepted answer
  
  GSEA is most commonly used for gene *differential* expression, rather than gene expression, where genes are ranked based on the degree to which their expression differs between conditions.
  
  The score that a gene contributes to the total enrichment score is based on this differential expression, with upregulated genes increasing the score, and downregulated genes decreasing the score. The overall enrichment score can be used to indicate whether the pathway as a whole (or more precisely, whether the matched genes in the pathway) is upregulated or downregulated in the dataset.
  
  However, as you point out, GSEA is similar to a correlation analysis in that it does not provide a complete picture of what's going on under the hood. You've got to know what you're feeding into it to be able to properly interpret its output.
  
  When I use GSEA, I prefer to calculate enrichment scores for upregulated and downregulated genes separately, which helps a bit in situations where the signal from upregulated genes can swamp out the signal from downregulated genes (and vice versa). The R package `fgsea` has a usage parameter `scoreType` that can be set to `pos` and `neg` for doing this.
  
  There's no universal set of sets that should be used for all GSEA. I have seen lots of gene sets with names like "Downregulated in colorectal cancer", or "Upregulated in cellular proliferation"; it sounds like those are the types of sets that you want to use, rather than the more general curated sets that include all involved genes.
  
  
  ---
  
  