Answers.Body: <p>I tinkered with a program <a href="https://www.nr.no/en/projects/software-genomics" rel="nofollow noreferrer">GSEA-SNP</a> quite a few years back, which claims that it does a similar ranking procedure with SNPs. It carries out its procedure by first linking SNPs with genes, then running an algorithm similar to GSEA. There's a bit more detail in the <a href="https://academic.oup.com/bioinformatics/article/24/23/2784/180339" rel="nofollow noreferrer">paper</a>.</p>
  
  <p>Unfortunately this is a space I've got a bit further away from in recent years, so I don't know if anyone has carried out the legwork to generalise the algorithm for sets of any type where each item within each set fits a continuous (but not necessarily normal) distribution.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-01-05T19:06:18.180Z
Answers.Text: This is a step in the right direction and better than I have been able to find, so thank you!  I haven't been able to find a non GWAS version of these tools, but these versions might work for my application. Thanks!
Answers.UserDisplayName: Reilstein
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-01-03T23:13:31.910Z
Answers.LastActivityDate: 2018-01-03T23:18:48.097Z
Answers.LastEditDate: 2018-01-03T23:18:48.097Z
Answers.UserDisplayName: gringer
Answers.Body: <p>You can use GSEA-like methods for any type of data. The basis of the test is to analyze if there is a preference for a group (a gene set) in the ranking of the other variable. </p>
  
  <p>So you first need to find where the functions you are interested are in the genome (To associate a range/position with a given function). Then using this association you can test if the ranking of the locus is associated with a specific function that is in a given area. </p>
  
  <p>I haven't seen anything like that implemented but you could do it yourself using biomaRt to retrieve data from where a gene (or a function) is located and fgsea to test which functions are associated with your score. </p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-01-07T15:24:08.747Z
Answers.LastActivityDate: 2018-01-07T15:24:08.747Z
Answers.UserDisplayName: llrs
Body: <p>As the question states, I am interested in an analysis similar to Gene Set Enrichment Analysis (ranked gene sets) but focused on locus-level data instead of genes. </p>
  
  <p>To explain in greater detail: I have a set of genomic coordinates from DNA methylation data that are scored based on their contribution to components from a non-negative matrix factorization. I am interested in better understanding the biological underpinnings of what these NMF components represent.  I know I can use Locus Overlap Analysis tools like GREAT or LOLA to tell me what GO/Functions are enriched in a particular BED file, but this type of analysis doesn't take into account the scores/rank of the loci in the BED file as would be done in an analysis akin to GSEA. </p>
  
  <p>Does anyone know of any tools/methods for analyzing functional enrichment while taking advantage of feature/locus ranks/scores with locus-level data?</p>
  
  <p>I know I probably didn't explain this very clearly, so please let me know if I can clarify anything for you.  Thanks</p>
  
Tags: <gsea><go-enrichment><functional-annotation>
Title: Is there anything similar to GSEA for locus-based (instead of of gene-based) data?
ThreadText: # Is there anything similar to GSEA for locus-based (instead of of gene-based) data?
  
  (Asked by: username_0 on 2018-01-03)
  
  As the question states, I am interested in an analysis similar to Gene Set Enrichment Analysis (ranked gene sets) but focused on locus-level data instead of genes.
  
  To explain in greater detail: I have a set of genomic coordinates from DNA methylation data that are scored based on their contribution to components from a non-negative matrix factorization. I am interested in better understanding the biological underpinnings of what these NMF components represent. I know I can use Locus Overlap Analysis tools like GREAT or LOLA to tell me what GO/Functions are enriched in a particular BED file, but this type of analysis doesn't take into account the scores/rank of the loci in the BED file as would be done in an analysis akin to GSEA.
  
  Does anyone know of any tools/methods for analyzing functional enrichment while taking advantage of feature/locus ranks/scores with locus-level data?
  
  I know I probably didn't explain this very clearly, so please let me know if I can clarify anything for you. Thanks
  
  **Tags:** gsea, go-enrichment, functional-annotation
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2018-01-03. Score: 2
  
  I tinkered with a program [GSEA-SNP](https://www.nr.no/en/projects/software-genomics) quite a few years back, which claims that it does a similar ranking procedure with SNPs. It carries out its procedure by first linking SNPs with genes, then running an algorithm similar to GSEA. There's a bit more detail in the [paper](https://academic.oup.com/bioinformatics/article/24/23/2784/180339).
  
  Unfortunately this is a space I've got a bit further away from in recent years, so I don't know if anyone has carried out the legwork to generalise the algorithm for sets of any type where each item within each set fits a continuous (but not necessarily normal) distribution.
  
  > **Comments:**
  > **username_0**: This is a step in the right direction and better than I have been able to find, so thank you!  I haven't been able to find a non GWAS version of these tools, but these versions might work for my application. Thanks!  
  
  ---
  
  ### Answer by username_2 on 2018-01-07. Score: 0
  
  You can use GSEA-like methods for any type of data. The basis of the test is to analyze if there is a preference for a group (a gene set) in the ranking of the other variable.
  
  So you first need to find where the functions you are interested are in the genome (To associate a range/position with a given function). Then using this association you can test if the ranking of the locus is associated with a specific function that is in a given area.
  
  I haven't seen anything like that implemented but you could do it yourself using biomaRt to retrieve data from where a gene (or a function) is located and fgsea to test which functions are associated with your score.
  
  
  ---
  
  