<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>

CC BY-SA 3.0
2018-01-05T19:06:18.180Z
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!
Reilstein
CC BY-SA 3.0
2018-01-03T23:13:31.910Z
2018-01-03T23:18:48.097Z
2018-01-03T23:18:48.097Z
gringer
<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>

CC BY-SA 3.0
2018-01-07T15:24:08.747Z
2018-01-07T15:24:08.747Z
llrs
<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>

<gsea><go-enrichment><functional-annotation>
Is there anything similar to GSEA for locus-based (instead of of gene-based) data?
# 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

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## 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!  

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### 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.


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