text: user16902: I have done DE analysis using SCANpy on my single cell data and I have compared each cluster versus all the other clusters. One cluster seems particularly interesting so I wanted to do pathway analysis.
  I have already tried to do the pathway analysis but I got confused. I initially followed the instructions from here (https://nbisweden.github.io/workshop-scRNAseq/labs/compiled/scanpy/scanpy_05_dge.html) and did the enrichment and then I also wanted to do GSEA so I used gseapy.prerank. However, then I was trying to find a way to visualize the network so I ended up going to the gseapy documentation.
  The steps of the enrichment analysis seemed the same but then I got confused with the GSEA portion. The first tutorial I found used gseapy.prerank but the second one uses gp.gsea. I was trying to understand the difference and it seems that since I already have the DE analysis I should be using the gseapy.prerank right bit I am not sure.
  I was wondering if doing the analysis trough KEGG would be easier.
  Thank you
  
  Gene set enrichment analysis (GSEA) is a commonly used algorithm for characterizing gene expression changes. However, the currently available tools used to perform GSEA have a limited ability to analyze large datasets, which is particularly problematic for the analysis of single-cell data. To overcome this limitation, we developed a GSEA package in Python (GSEApy), which could efficiently analyze large single-cell datasets.
  
  
  user104: An easy way to do an enrichment analysis in scanpy is using scanpy.queries.enrich(). This relies on gprofiler, which uses KEGG, GO and other ontologies
  
original_text: 16902: <p>I have done DE analysis using <a href="https://scanpy.readthedocs.io/en/stable/" rel="nofollow noreferrer">SCANpy</a> on my single cell data and I have compared each cluster versus all the other clusters. One cluster seems particularly interesting so I wanted to do pathway analysis.</p>
  <p>I have already tried to do the pathway analysis but I got confused. I initially followed the instructions from here (<a href="https://nbisweden.github.io/workshop-scRNAseq/labs/compiled/scanpy/scanpy_05_dge.html" rel="nofollow noreferrer">https://nbisweden.github.io/workshop-scRNAseq/labs/compiled/scanpy/scanpy_05_dge.html</a>) and did the enrichment and then I also wanted to do GSEA so I used gseapy.prerank. However, then I was trying to find a way to visualize the network so I ended up going to the gseapy documentation.</p>
  <p>The steps of the enrichment analysis seemed the same but then I got confused with the GSEA portion. The first tutorial I found used gseapy.prerank but the second one uses gp.gsea. I was trying to understand the difference and it seems that since I already have the DE analysis I should be using the gseapy.prerank right bit I am not sure.</p>
  <p>I was wondering if doing the analysis trough KEGG would be easier.</p>
  <p>Thank you</p>
  <blockquote>
  <p>Gene set enrichment analysis (GSEA) is a commonly used algorithm for characterizing gene expression changes. However, the currently available tools used to perform GSEA have a limited ability to analyze large datasets, which is particularly problematic for the analysis of single-cell data. To overcome this limitation, we developed a GSEA package in Python (GSEApy), which could efficiently analyze large single-cell datasets.</p>
  </blockquote>
   104: <p>An easy way to do an enrichment analysis in scanpy is using <a href="https://scanpy.readthedocs.io/en/stable/generated/scanpy.queries.enrich.html" rel="nofollow noreferrer"><code>scanpy.queries.enrich()</code></a>. This relies on <a href="https://pypi.org/project/gprofiler-official/#description" rel="nofollow noreferrer">gprofiler</a>, which uses KEGG, GO and other ontologies</p>
  