text: user4876: I have been using EdgeR 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 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) 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.
  
  user48: A couple of years ago I wrote a blog post about GSEA methods in Bioconductor, in addition to these I would recommend clusterProfiler or 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 package.
  In most, if not all, of these methods you just need an ordered list and a gene set source. 
  
  user678: Try gProfileR. It is very intuitive and easy to use in R.
  
original_text: 4876: <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>
   48: <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>
   678: <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>
  