<p>Use the following R package for Gene Set Enrichment analysis of RNA-seq data: <a href="https://www.bioconductor.org/packages/release/bioc/html/SeqGSEA.html" rel="nofollow noreferrer">seqGSEA</a></p>

<p>There is another R package (<a href="https://bioconductor.org/packages/release/bioc/html/fgsea.html" rel="nofollow noreferrer">fgsea</a>) recently published called "Fast Gene Set Enrichment Analysis" by Alexey Sergushichev.</p>

CC BY-SA 3.0
2017-07-17T06:35:19.213Z
2017-12-06T22:31:01.777Z
2017-12-06T22:31:01.777Z
arup
<p>You seem to refer to the GSEA provided by the <a href="http://software.broadinstitute.org/gsea/index.jsp" rel="noreferrer">Broad institute</a>, (there are other GSEA algorithms). </p>

<p>1) You can provide whatever you wish, but if you want to know if those gene sets in which side of the ordered list are they, then provide all the list (of genes) you have.</p>

<p>2) GSEA analyize if the order of a given list distributes in certain way the elements of another (unordered) list. In your case you would need to compute the GSEA for each treatment vs control comparison.</p>

CC BY-SA 3.0
2017-07-15T15:46:52.880Z
2017-07-15T15:46:52.880Z
llrs
<p>GSEAPreranked is a limited version of GSEA. 
If your sample number is enough(more than 5,6 samples in each condition), you can use GSEA version. 
Moreover, you can ask this question to the gsea-help google group. </p>

CC BY-SA 3.0
2018-04-12T14:15:06.650Z
please add links to support your answer
Bioathlete
CC BY-SA 3.0
2018-04-11T15:08:30.363Z
2018-04-11T15:08:30.363Z
Dohoon
<p>According to the <a href="https://software.broadinstitute.org/cancer/software/gsea/wiki/index.php/FAQ" rel="nofollow noreferrer">GSEA documentation</a>:</p>

<blockquote>
  <p>we are recommending use of the GSEAPreranked tool for conducting gene
  set enrichment analysis of data derived from RNA-seq experiments</p>
</blockquote>

<p>Then more specifically to your question:</p>

<blockquote>
  <p>Based on your differential expression analysis, rank your features and
  capture your ranking in an RNK-formatted file. The ranking metric can
  be whatever measure of differential expression you choose from the
  output of your selected DE tool. For example, cuffdiff provides the
  (base 2) log of the fold change.</p>
</blockquote>

CC BY-SA 3.0
2018-04-13T00:42:26.610Z
2018-04-13T00:42:26.610Z
burger
<p>I'm currently learning about Gene Set Enrichment Analysis (<a href="http://software.broadinstitute.org/gsea/index.jsp" rel="nofollow noreferrer">GSEA</a>) in the hopes of using it in my analysis of differentially expressed genes, and I just had a few questions about the program, specifically about GSEAPreranked, which I need cleared up.</p>

<ol>
<li><p>On the ranked list needed for GSEA input, should the list include all genes, or only those that pass a certain threshold of significance (i.e. fold change higher than 2, p value less than 0.05, etc.)? Ideally I'd like to sort the genes by fold change alone as I don't trust my p values as much, so should I only include genes with high fold changes?</p></li>
<li><p>I am comparing multiple conditions of disease with different treatments. Am I correct that GSEA only compares two conditions? If this is the case should I run GSEA for each control/treatment comparison? Would this be conventional?</p></li>
</ol>

<gene><sequence-annotation><rna>
Basic questions about GSEA
# Basic questions about GSEA

(Asked by: username_0 on 2017-07-15)

I'm currently learning about Gene Set Enrichment Analysis ([GSEA](http://software.broadinstitute.org/gsea/index.jsp)) in the hopes of using it in my analysis of differentially expressed genes, and I just had a few questions about the program, specifically about GSEAPreranked, which I need cleared up.

1. On the ranked list needed for GSEA input, should the list include all genes, or only those that pass a certain threshold of significance (i.e. fold change higher than 2, p value less than 0.05, etc.)? Ideally I'd like to sort the genes by fold change alone as I don't trust my p values as much, so should I only include genes with high fold changes?
2. I am comparing multiple conditions of disease with different treatments. Am I correct that GSEA only compares two conditions? If this is the case should I run GSEA for each control/treatment comparison? Would this be conventional?

**Tags:** gene, sequence-annotation, rna

---

## 4 answers

### Answer by username_1 on 2017-07-17. Score: 6

Use the following R package for Gene Set Enrichment analysis of RNA-seq data: [seqGSEA](https://www.bioconductor.org/packages/release/bioc/html/SeqGSEA.html)

There is another R package ([fgsea](https://bioconductor.org/packages/release/bioc/html/fgsea.html)) recently published called "Fast Gene Set Enrichment Analysis" by Alexey Sergushichev.


---

### Answer by username_2 on 2017-07-15. Score: 6

You seem to refer to the GSEA provided by the [Broad institute](http://software.broadinstitute.org/gsea/index.jsp), (there are other GSEA algorithms).

1) You can provide whatever you wish, but if you want to know if those gene sets in which side of the ordered list are they, then provide all the list (of genes) you have.

2) GSEA analyize if the order of a given list distributes in certain way the elements of another (unordered) list. In your case you would need to compute the GSEA for each treatment vs control comparison.


---

### Answer by username_3 on 2018-04-11. Score: 1

GSEAPreranked is a limited version of GSEA.
If your sample number is enough(more than 5,6 samples in each condition), you can use GSEA version.
Moreover, you can ask this question to the gsea-help google group.

> **Comments:**
> **username_4**: please add links to support your answer  

---

### Answer by username_5 on 2018-04-13. Score: 1

According to the [GSEA documentation](https://software.broadinstitute.org/cancer/software/gsea/wiki/index.php/FAQ):

> we are recommending use of the GSEAPreranked tool for conducting gene
> set enrichment analysis of data derived from RNA-seq experiments

Then more specifically to your question:

> Based on your differential expression analysis, rank your features and
> capture your ranking in an RNK-formatted file. The ranking metric can
> be whatever measure of differential expression you choose from the
> output of your selected DE tool. For example, cuffdiff provides the
> (base 2) log of the fold change.


---

