I am having difficulty analyzing GO pathway results due to a large list of redundant or similar GO terms. These terms have been generated from significant differentially expressed genes in different cell types across two conditions. I have already set filter parameters such as p-adjusted value ≤ 0.05 to select pathways of interest.
Thanks
Reducing the number of GO terms while preserving the data's significance can be achieved by applying semantic similarity measures to group similar GO terms into more general terms. Here are some possible methods:
Yes. I have used the first method as a part of my analysis. Thank you for clarification and providing additional information.
A meaningful and practical way out is aggregating GO terms into more general, still coherent, terms. Then, you have less terms, still preserving your enrichment results.
This can be achieved using REVIGO (http://revigo.irb.hr/).
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