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:
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 :)