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.
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.