Hello,
So, I'm utilizing the gene ontology enrichment analysis on a list of proteins and specifically for cellular components
The tool is present on this site: http://geneontology.org/
Now, my question is: If I want to try and compare between the different sets, is it better to look at the p-values or the fold enrichment. Bear in mind that all of these sets have passed the "significance" value.
I also noticed that the bigger the set is, the more significant the result because I suppose you have a higher "n". So, I supposed looking fold enrichment is more interesting? I'm not sure.
Thank you.
The two are somehow related but as you pointed out, the larger the set the more significant the results tend to be. This is a consequence of the statistics, for the same fold enrichment, the p-values are smaller for larger sets. In general larger GOs are quite general and often not very informative , therefore, it is reasonable to consider the fold enrichment once the significant terms have been identified. A valid and visually appealing alternative is to use tools like revigo, that for instance uses a semantic similarity measure to place GO terms in a novel and more informative coordinate system.
Btw better to look at corrected p-values than p-values for the multiple testing problem. And better to use some stat taking into account the correlated nature of gene ontologies.
I am also interested on if and how you are performing the propagation from the annotation of a gene to the parent nodes. and see my question:
https://www.researchgate.net/post/When_performing_GSEA_is_it_better_to_take_GO_annotations_as_they_are_or_propagate_them_to_parents
Hello Matteo,
Thank you for your answer. It was very informative. I will try using the revigo tool!
I will try and explain in more details what I'm doing because I think then that you could offer better help.
So, the main thing that I did was use a prediction framework for virus-host protein interactions.
This framework gave me a list of host proteins which was predicted to interact with my viral protein.
Since, this is an in-silico study. I'am more interested in checking for enrichment of certain sets than to check for an interaction with individual proteins. In more biological terms, I just want to have an idea on which part of the cellular component will my protein interact.
Also, I have another question, since we are going into details.
So, currently, the enrichment data show that my protein interacts on everything that is considered as cytoskeletal. So, I have 5-8 enrichment in sets that contains such related genes.
What is usually the cut-off for enrichment? I have set with an enrichment of 8 and set with an enrichment of 2. The set with an enrichment of 2 has a p-value lower than the set with an enrichment of 8. Although, both are significant. In that case, on which value do I look? (I will also check the corrected p-value)
In addition, I also have enrichment in the gene set for nuclear proteins. My viral protein is not present in the nucleus. Could that be problematic for my analysis?
The protein prediction interaction could surely still be valid. But, my viral protein doesn't enter into the nucleus to allow for that interaction to occur.
I will check your question, and try to help.
Thank you Matteo!
Bingo plugin for cytoscape uses Hypergeometric distribution test as a significance enrichment test. I am not sure about fold changes, but this test would certainly help. Please have a look at the paper.
https://academic.oup.com/bioinformatics/article/21/16/3448/216306/BiNGO-a-Cytoscape-plugin-to-assess
The "fold-enrichment" will vary more for smaller sets.
I don't see a good way to compare different sets. I would use the p-value to get a crude ordering, and focus on the top-ranked sets to get an idea about the biological meaning. I would not compare the set like "set A is more significant than set B" or "set C has a highe fold-enrichment like set A" etc. These things are usually biologically entirely irrelevant. Doing this is more reading tea leaves than doing biology.
with respect to comparing different sets, i suggest you use DAVID data base for the GO terms of each set. in that case the results came out using p-values.
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