<p>You can't do a GSEA on a set alone because the differentiating statistic (or, occasionally, the rank) is used to calculate the enrichment score that is produced by the GSEA algorithm.</p>
<p>The other gene list type tools that I'm aware of (that my biologist colleagues use) are <a href="https://david.ncifcrf.gov/tools.jsp" rel="nofollow noreferrer">DAVID</a> and <a href="http://pantherdb.org/" rel="nofollow noreferrer">PANTHER</a>. But those are general discovery algorithms, not for comparing specific lists.</p>

CC BY-SA 4.0
2023-07-10T02:54:11.137Z
thank you , is there a way to rank the genes not based on expression data ? Or a tool that calculates the enrichment not using the   GSEA  algorithm?
Eliza R
CC BY-SA 4.0
2023-07-10T20:12:39.650Z
@ElizaR Sounds a bit like a pathway analysis (e.g. see gprofiler). Perhaps a hypergeometric test
Chris_Rands
CC BY-SA 4.0
2023-07-09T22:17:14.600Z
2023-07-10T21:23:59.913Z
2023-07-10T21:23:59.913Z
gringer
<p>I have been looking all over the web to find some answers to my problem but unfortunately, I was unsuccessful. I wish to determine whether an a priori defined set of genes in my case genes associated with Epidermolysis bullosa shows statistically significant, concordant differences between two biological states (e.g. phenotypes).</p>
<p>I have a list of genes from my own experiment and a predefined list of genes associated with Epidermolysis bullosa, and want to find out if the genes of Epidermolysis bullosa are overrepresented in my list.</p>
<p>Here is an article about GSEA, which is related to what I want to do:</p>
<p><a href="https://www.pnas.org/doi/epdf/10.1073/pnas.0506580102" rel="nofollow noreferrer">https://www.pnas.org/doi/epdf/10.1073/pnas.0506580102</a>
<a href="https://i.sstatic.net/xRqZF.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/xRqZF.png" alt="enter image description here" /></a></p>
<p>so I have the following data:
I'm researching patients with severe or mild disease; in this research, I have found a list of  200 genes after conducting a SKAT test in R (<a href="https://www.rdocumentation.org/packages/SKAT/versions/2.2.4/topics/SKAT" rel="nofollow noreferrer">https://www.rdocumentation.org/packages/SKAT/versions/2.2.4/topics/SKAT</a>) with a p-value &lt;0.05 ( which I presume can cause a severe disease instead of mild  ) I want to focus on this 200 significant genes that can perhaps affect the disease level, this genes can be  -&gt; the L list as described in the article above.
Also, the S list of predefined genes ( as is called in the article above )is associated with Epidermolysis bullosa disease. and I want to know if the genes from the Epidermolysis bullosa are overrepresented in my L list.
After reading the article and many more resources, I understood that to perform GSEA  (<a href="https://www.gsea-msigdb.org/gsea/index.jsp" rel="nofollow noreferrer">https://www.gsea-msigdb.org/gsea/index.jsp</a>), I had to have gene expression data which  i don't have .</p>
<p>so I wish to understand if there is a tool that could do a gene set comparison without having the expression data whether it's in R or Python that uses my gene list and predefined list ( in my case associated with Epidermolysis bullosa)  ,thank you :)</p>

<gene><r><gsea><go-enrichment>
Gene Set Comparison Without Expression Data
# Gene Set Comparison Without Expression Data

(Asked by: username_0 on 2023-07-08)

I have been looking all over the web to find some answers to my problem but unfortunately, I was unsuccessful. I wish to determine whether an a priori defined set of genes in my case genes associated with Epidermolysis bullosa shows statistically significant, concordant differences between two biological states (e.g. phenotypes).

I have a list of genes from my own experiment and a predefined list of genes associated with Epidermolysis bullosa, and want to find out if the genes of Epidermolysis bullosa are overrepresented in my list.

Here is an article about GSEA, which is related to what I want to do:

<https://www.pnas.org/doi/epdf/10.1073/pnas.0506580102>
[![enter image description here](https://i.sstatic.net/xRqZF.png)](https://i.sstatic.net/xRqZF.png)

so I have the following data:
I'm researching patients with severe or mild disease; in this research, I have found a list of 200 genes after conducting a SKAT test in R (<https://www.rdocumentation.org/packages/SKAT/versions/2.2.4/topics/SKAT>) with a p-value <0.05 ( which I presume can cause a severe disease instead of mild ) I want to focus on this 200 significant genes that can perhaps affect the disease level, this genes can be -> the L list as described in the article above.
Also, the S list of predefined genes ( as is called in the article above )is associated with Epidermolysis bullosa disease. and I want to know if the genes from the Epidermolysis bullosa are overrepresented in my L list.
After reading the article and many more resources, I understood that to perform GSEA (<https://www.gsea-msigdb.org/gsea/index.jsp>), I had to have gene expression data which i don't have .

so I wish to understand if there is a tool that could do a gene set comparison without having the expression data whether it's in R or Python that uses my gene list and predefined list ( in my case associated with Epidermolysis bullosa) ,thank you :)

**Tags:** gene, r, gsea, go-enrichment

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## 1 answers

### Answer by username_1 on 2023-07-09. Score: 1

You can't do a GSEA on a set alone because the differentiating statistic (or, occasionally, the rank) is used to calculate the enrichment score that is produced by the GSEA algorithm.

The other gene list type tools that I'm aware of (that my biologist colleagues use) are [DAVID](https://david.ncifcrf.gov/tools.jsp) and [PANTHER](http://pantherdb.org/). But those are general discovery algorithms, not for comparing specific lists.

> **Comments:**
> **username_0**: thank you , is there a way to rank the genes not based on expression data ? Or a tool that calculates the enrichment not using the   GSEA  algorithm?  
> **username_2**: @ElizaR Sounds a bit like a pathway analysis (e.g. see gprofiler). Perhaps a hypergeometric test  

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