Code snippet from topGO Reference Manual (page 4):
sampleGOdata <- new ("topGOdata", description = "Simple session", ontology = "BP", allGenes = geneList, geneSel = topDiffGenes, nodeSize = 10, annot = annFUN.db, affyLib = affyLib)
I tried
ontology=c("BP", "MF", "CC")
But, that is not allowed. Any other syntax to specify all three in one go ?
3 answers
One of the Kindle Touch updates included a "Time-to-Read" feature. It seems to be based loosely on how fast I turn the page. I usually turn back to the cover when I start reading a new book and flip back to the start of the introduction or first chapter. So the status usually says
1 min left in chapter
for a chapter or two. I'm guessing the algorithm has determined that I have super-human reading speed. Once I prove I don't, it becomes fairly accurate.
But other times I sit on a page for several minutes when distracted by other things. I would have assumed the calculation would show me taking much longer to finish chapters after doing that. However, the reading time seems to be stable and fairly accurate after the initial weirdness.
How does the algorithm work and why does each book have its own estimate?
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I think it is not possible, as the topology method is based precisely on the differences on the structure of each sub ontology. But why do you want to do this?
At the moment, my interest is to filter Genes that are over-represented based on enriched GO terms, not the GO class. After analyzing for BP, MF and CC separately, I could combine the enriched GO terms. My question is to find a way to do all three analysis in one go.
The structure of the GO data, makes such a combination difficult: it is designed to have separate structure. However, I would recommend to run the over-representative analysis and then see which genes map to which GO found over-represented in your analysis. That way you could further filter your genes based on how much they are related to the significant GO terms. Another approach would be to filter by a function, location and process of your interest and see which genes are left. I think that the first approach will be easier and more robust, but depending on your study you might be want to pursue the second one.