I am trying to catch up with the latest development in the gene set enrichment analysis and I am looking for recommendations describing practices and approaches that I might have missed. Perhaps older methods are still the best.
Here is the question:
Suppose you ran an analysis pipeline that produced a set of genes or transcripts (for example a set of differentially expressed transcripts across two conditions)
What is the tool or methodology of your choice to help you make sense of the role and function of your set?
Actually I like cluster profiler because we can look at a comparative view of pathways or GO terms in experimental conditions
https://yulab-smu.github.io/clusterProfiler-book/chapter12.html
Also I like GOplot circus for nice visualization of DAVID results
https://wencke.github.io/#display-of-the-relationship-between-genes-and-terms-gochord
The clusterprofiler seems like a quite sophisticated tool - a bit overcomplicated in that it is not clear what data goes into it, so one has devote quite the effort to get their data in the right shape and form - but after that looks like it does quite a bit
They (the Avi Mayan' lab) have also built Enrichr, Clustergrammer, and other tools:
Enrichr is about as easy as it gets in terms of use. I've used both it and clusterProfiler (/ReactomePA/DOSE) quite a bit. clusterProfiler is definitely a bit more annoying to set up, but the visualizations are quite good for more complicated analyses.
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Based on personal experience, most of the packages are just elaborate wrappers for
phyper()(at least in the R universe). So much depends on the actual reference pathways.