Gene ontology analysis
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2.6 years ago
lapis44 ▴ 20

Hi all,

I recently put a list of 90 differentially expressed genes into DAVID bioinformatics software for gene ontology enrichment analysis. I received an output of about 10 pathway terms, each for which the following information was provided:

  • Number of genes in that pathway term
  • Benjamini value
  • Example genes

Only one of these pathway terms had a Benjamini value < 0.05 (reached significance). Still, I would like to present all pathway terms in a presentation while also listing the Benjamini values for each. My question is: How should I refer to these pathway terms. Can I say they are enriched categories? Would that be accurate since I am not saying they are "significantly" enriched categories? Or, would it still be inaccurate because it implies significance? What other words should I use then to describe all these pathway terms (including both Benjamini <0.05 and >0.05)?

Thank you for sharing your knowledge!

ontology go enrichment • 1.0k views
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2.6 years ago
EagleEye 6.8k

Hello,

If you think presenting those pathways make sense for your study, you can relax the criteria.

Example:

1) You can use p-value instead of FDR/Benjamini

2) Even then if you don't get, you can also go upto p-value < 0.1 (10%)

3) Otherwise, you might be using very stringent criteria during differential expression analysis (might missing out important genes). Reduce the stringency and try pathway analysis with bigger list of genes. This might bring your important pathways to be significant.

If you are interested in batch analysis (simultaneously doing pathway analysis for multiple lists filtered with different criteria), try out GeneSCF.

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2.6 years ago
harish ▴ 320

Generally speaking, you can call these over-representation tests. You can represent these as a Scatter-plot (dot-plot) as well, ordered on the basis of p/qvalues and ratio of genes in your study in that pathway over the number of total genes in that pathway.

See reference image under dotplot section here in clusterProfiler.

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