Answers.Body: <p>A couple of years ago I wrote a blog post about <a href="https://b101nfo.blogspot.com/2017/03/gsea-in-bioconductor.html" rel="nofollow noreferrer">GSEA methods in Bioconductor</a>, in addition to these I would recommend <a href="https://bioconductor.org/packages/clusterProfiler/" rel="nofollow noreferrer">clusterProfiler</a> or <a href="https://bioconductor.org/packages/ReactomePA/" rel="nofollow noreferrer">ReactomePA</a>.  In most of them you need to provide the gene set/pathway database.<br>
  If you want to do gene ontology enrichment then I recomment the <a href="http://bioconductor.org/packages/topGO/" rel="nofollow noreferrer">topGO</a> package.
  In most, if not all, of these methods you just need an ordered list and a gene set source. </p>
  
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Answers.Body: <p>Try <a href="https://www.rdocumentation.org/packages/gProfileR/versions/0.6.7/topics/gprofiler" rel="nofollow noreferrer">gProfileR</a>. It is very intuitive and easy to use in R.</p>
  
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Body: <p>I have been using <a href="https://www.bioconductor.org/packages/release/bioc/html/edgeR.html" rel="nofollow noreferrer">EdgeR</a> from the <strong>Bioconductor</strong> package to perform differential expression analysis on bulk RNASeq data sets.</p>
  <p>This analysis gives me an output dataframe that has a list of all the genes, ranked by the their adjP values. This dataframe also contains the t-statistic and lfc values for each gene as well.</p>
  <p>I have recently been using <a href="https://maayanlab.cloud/Enrichr/" rel="nofollow noreferrer">enrichR</a> to perform gene set enrichment analysis on all the differentially expressed genes to determine what biological, molecular, transcriptional, etc pathways these genes are involved in. I really like the enrichR package for a few key reasons:</p>
  <p>1.) It has access to many different databases that I can call on with a simple vector of their names. Most of my data is murine RNASEq, so for those analyses, I can simply select the murine databases:</p>
  <pre><code>enrichR_dbs &lt;- c(&quot;WikiPathways_2019_Mouse&quot;, &quot;KEGG_2019_Mouse&quot;, &quot;GO_Biological_Process_2018&quot;, &quot;GO_Cellular_Component_2018&quot;, &quot;GO_Molecular_Function_2018&quot;, &quot;GWAS_Catalog_2019&quot;, &quot;TRRUST_Transcription_Factors_2019&quot;)
  </code></pre>
  <p>2.) It uses a single function, enrichR, to perform the enrichment. The arguments for that function are the list of gene symbols and the list of databases.</p>
  <pre><code>enrichr(as.character(Gene_Dataframe$SYMBOL[1:500]), enrichR_dbs)
  </code></pre>
  <p>Anyway, I am looking for another enrichment analysis because I want to be able to see if I get similar pathways for my genes when using another method/algorithm.</p>
  <p>Unfortunately, I can't seem to find another enrichment analysis( <a href="https://biodatamining.biomedcentral.com/articles/10.1186/s13040-018-0166-8" rel="nofollow noreferrer">competitive or self-contained</a>) that is both as user-friendly and that gives me easy access to murine databases (without requiring to convert my genes through changes in capitalization, like you need to with GSEA) as enrichR.</p>
  <p><strong>Does anyone have recommendations about enrichment analysis R packages that don't require too many more inputs than the dataframe I generated through my EdgeR analysis?</strong></p>
  <p>P.S. I'm ok with downloading databases if I need to. Though inclusion of a PDF with a tutorial would be fantastic, if possible.</p>
  
Tags: <go-enrichment>
Title: Alternative to enrichR for enrichment analysis?
ThreadText: # Alternative to enrichR for enrichment analysis?
  
  (Asked by: username_0 on 2019-07-16)
  
  I have been using [EdgeR](https://www.bioconductor.org/packages/release/bioc/html/edgeR.html) from the **Bioconductor** package to perform differential expression analysis on bulk RNASeq data sets.
  
  This analysis gives me an output dataframe that has a list of all the genes, ranked by the their adjP values. This dataframe also contains the t-statistic and lfc values for each gene as well.
  
  I have recently been using [enrichR](https://maayanlab.cloud/Enrichr/) to perform gene set enrichment analysis on all the differentially expressed genes to determine what biological, molecular, transcriptional, etc pathways these genes are involved in. I really like the enrichR package for a few key reasons:
  
  1.) It has access to many different databases that I can call on with a simple vector of their names. Most of my data is murine RNASEq, so for those analyses, I can simply select the murine databases:
  
  ```
  enrichR_dbs <- c("WikiPathways_2019_Mouse", "KEGG_2019_Mouse", "GO_Biological_Process_2018", "GO_Cellular_Component_2018", "GO_Molecular_Function_2018", "GWAS_Catalog_2019", "TRRUST_Transcription_Factors_2019")
  
  ```
  
  2.) It uses a single function, enrichR, to perform the enrichment. The arguments for that function are the list of gene symbols and the list of databases.
  
  ```
  enrichr(as.character(Gene_Dataframe$SYMBOL[1:500]), enrichR_dbs)
  
  ```
  
  Anyway, I am looking for another enrichment analysis because I want to be able to see if I get similar pathways for my genes when using another method/algorithm.
  
  Unfortunately, I can't seem to find another enrichment analysis( [competitive or self-contained](https://biodatamining.biomedcentral.com/articles/10.1186/s13040-018-0166-8)) that is both as user-friendly and that gives me easy access to murine databases (without requiring to convert my genes through changes in capitalization, like you need to with GSEA) as enrichR.
  
  **Does anyone have recommendations about enrichment analysis R packages that don't require too many more inputs than the dataframe I generated through my EdgeR analysis?**
  
  P.S. I'm ok with downloading databases if I need to. Though inclusion of a PDF with a tutorial would be fantastic, if possible.
  
  **Tags:** go-enrichment
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2019-07-16. Score: 3
  
  A couple of years ago I wrote a blog post about [GSEA methods in Bioconductor](https://b101nfo.blogspot.com/2017/03/gsea-in-bioconductor.html), in addition to these I would recommend [clusterProfiler](https://bioconductor.org/packages/clusterProfiler/) or [ReactomePA](https://bioconductor.org/packages/ReactomePA/). In most of them you need to provide the gene set/pathway database.  
  If you want to do gene ontology enrichment then I recomment the [topGO](http://bioconductor.org/packages/topGO/) package.
  In most, if not all, of these methods you just need an ordered list and a gene set source.
  
  
  ---
  
  ### Answer by username_2 on 2019-07-17. Score: 3
  
  Try [gProfileR](https://www.rdocumentation.org/packages/gProfileR/versions/0.6.7/topics/gprofiler). It is very intuitive and easy to use in R.
  
  
  ---
  
  