Hi, im newbee for analysing gen expression data set and hope someone have som advise.
Im working on mice liver gen expression (mRNA) data sets generated with EdgeR - now I want to do some gen enrichment analysis - but is confused as to which methods are most robust and reliable (and to some extent user-friendly).
I have tried EnrichR, ROntoTools and GSEA - out of these, I think GSEA is the most difficult to understand.
So - what do you use? Do you have any tips for me?
Hildegunn Dahl
If you want a user-friendly online software for gene-ontology enrichment analysis, you can use shinyGO: http://bioinformatics.sdstate.edu/go/
Also, for more robust and reliable analysis and consistency (because you are using edgeR for DGE analyisis), I recommend using clusterProfiler package (Bioconductor) on R: https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
Find a detailed tutorial that explains the whole process here: https://hbctraining.github.io/DGE_workshop_salmon/lessons/functional_analysis_2019.html#:~:text=The%20output%20of%20RNA%2Dseq,differentially%20expressed%20genes%20(DEGs).&text=identify%20genes'%20involvement%20in%20novel,together%20based%20on%20similar%20trends
I think one excellent option is the GSEA software from the Broad Institute https://www.gsea-msigdb.org/gsea/index.jsp. It doesn't require code and it can assess positive and negative enrichment, which I think is relevant in many cases. Plus, you can make a custom gene list and analyze enrichment, and make a GO network to canalize with cytoscape.
Hei! Stumbled across this :
Article CirGO: An alternative circular way of visualising gene ontology terms
will give it a try myself but the idea seems tempting in case one wants to give a short macroscopic snapshot (especially when comparing several time points and cellular fractions).
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