Question: What is the "state of the art" in gene set analysis in 2019?
2
gravatar for Istvan Albert
11 days ago by
Istvan Albert ♦♦ 81k
University Park, USA
Istvan Albert ♦♦ 81k wrote:

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?

go enrichment pathway • 137 views
ADD COMMENTlink modified 11 days ago • written 11 days ago by Istvan Albert ♦♦ 81k

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.

ADD REPLYlink written 11 days ago by igor8.3k
3
gravatar for Angel
11 days ago by
Angel3.5k
Angel3.5k wrote:

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

ADD COMMENTlink modified 11 days ago • written 11 days ago by Angel3.5k
1

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

https://github.com/YuLab-SMU/clusterProfiler

ADD REPLYlink modified 11 days ago • written 11 days ago by Istvan Albert ♦♦ 81k

I do agree with you that clusterprofiler does a bit considering demanding efforts to reshaping input data; But I liked that part one can look at pathways and GO terms between conditions comparatively (also defining up and down regulation genes in each condition)

ADD REPLYlink written 11 days ago by Angel3.5k
2
gravatar for ATpoint
11 days ago by
ATpoint23k
Germany
ATpoint23k wrote:

I found gprofiler useful as it combines access to various sources such as GO, KEGG, reactome and WP with a GUI to conveniently explore data. Input is a list of gene names.

ADD COMMENTlink modified 11 days ago • written 11 days ago by ATpoint23k
1

They (the Avi Mayan' lab) have also built Enrichr, Clustergrammer, and other tools:

http://amp.pharm.mssm.edu/Enrichr/

https://clustergrammer.readthedocs.io/getting_started.html

ADD REPLYlink modified 11 days ago • written 11 days ago by Istvan Albert ♦♦ 81k
1

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.

ADD REPLYlink written 11 days ago by jared.andrews073.1k
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