Question: What's your preferred pathway enrichment analysis tool after DEG analysis and why?
2
gravatar for unawaz
3 months ago by
unawaz40
Australia
unawaz40 wrote:

So firstly, I'm completely aware that this type of question has been asked multiple times (I know this since I've been scrolling over these type of questions for the past 2 days), but I'm actually more interested in knowing the reasons as to why some people prefer

I've performed differential expression analysis using DESeq2 and I want to see which Gene ontology terms, KEGG pathway terms etc are enriched in my data set. I've initially tried using clusterProfileR, but I keep getting 3 enriched terms for all my differentially expressed genes using enrichGO(). I also know that some input in clusterProfileR requires you to put logFC values, so I wasn't sure if that was for ALL the genes analysed, or just the differentially expressed genes.

I've also used goseq but my main issue with that is the GO terms are too broad.

I also only have about 300 DEGs, so I'm not really sure if this sort of analysis is best performed when you have a myriad of DEGs, or can be done with a small number.

Anyway, looking forward to hearing people's responses :)

rna-seq gene ontology • 312 views
ADD COMMENTlink modified 3 months ago by Jean-Karim Heriche18k • written 3 months ago by unawaz40

Check out our GO_MWU: great power, no need to spit the data into DEGs / non-DEGs (the test is ranks-based so it can use any measure according to which the genes can be ranked), intuitive graphical representation of results. https://github.com/z0on/GO_MWU - Misha

ADD REPLYlink written 8 weeks ago by matz0
3
gravatar for i.sudbery
3 months ago by
i.sudbery4.3k
Sheffield, UK
i.sudbery4.3k wrote:

I'm a big user of GOseq, which allows to control for the fact that longer and more highly expressed genes are more likely to be found to be differential, if long or highly expressed genes are not evenly distributed between pathways/categories, then this can bias your enriched pathways.

I also like the GSEA-like algorithms, because you do not have to set an artificial limit on what you consider significant. The version of this where you rank on significance suffers from the same gene-length bias that traditional GO tests suffer, but this may be lessened by ranking on some suitably strunken logFC metric. cameraPR from limma is a good example of this.

Finally we've used SPIA before, which is a pathway enrichment tool that takes the topology of the network into account. Its a great idea, let down by the quality of the pathway annotations it runs on.

ADD COMMENTlink written 3 months ago by i.sudbery4.3k
1
gravatar for Devon Ryan
3 months ago by
Devon Ryan89k
Freiburg, Germany
Devon Ryan89k wrote:

Essentially every free tool is using the same set of databases and quite similar algorithms (there's a smallish set to choose from with a few tweaks here and there), so it's unsurprising that you get similar results regardless of which tool you use. To be frank, if you want different results you need to use a different database. We're pretty happy with IPA in this regard. It can be rather pricey, but if you can go in with multiple labs on a license then it becomes more feasible.

ADD COMMENTlink written 3 months ago by Devon Ryan89k
0
gravatar for EagleEye
3 months ago by
EagleEye6.2k
Sweden
EagleEye6.2k wrote:

I use Gene Set Clustering based on Functional annotation (GeneSCF)

ADD COMMENTlink written 3 months ago by EagleEye6.2k
0
gravatar for Jean-Karim Heriche
3 months ago by
EMBL Heidelberg, Germany
Jean-Karim Heriche18k wrote:

An alternative approach: Finding New Order in Biological Functions from the Network Structure of Gene Annotations

ADD COMMENTlink written 3 months ago by Jean-Karim Heriche18k
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