When exporting TopTags in EdgeR, you of course get LogFC which have a sign associated with them and that refers to which group has higher expression but it doesn't really tell you anything about in which group the gene is significantly up-regulated in.
An example would be a LogFC of 3.5. This would mean it's higher in one of my groups (adults in my dataset) and a reasonable deduction would be that the expression would be up-regulated in adults. However, it can also mean that the gene is downregulated in my other group, larvae. And when I graph out the expression trajectories of all my genes, most of the time, the former explanation is correct, but more than once it has been the latter: downregulation in the other life stage. Considering I have hundreds of genes that I don't have the time to graph, is there a way in EdgeR to actually determine which genes are up-regulated and not just which ones have higher LogFC's in a comparison?
Thanks
Do you have more groups than just adult and larva? With only 2 groups, there is no difference between "up-regulated in group A relative to group B" and "down-regulated in group B relative to group A".
Nope, just two groups. Perhaps what I did not express very well is no expressional activation in one group and down regulation in the other looks like upregulation in the first group when it isn't as all comparisons are adult - larvae. Therefore a positive LogFC just means adults have higher expression not that the expression up-regulated. Is that more clear?
I think there's something missing from your description. You say that all comparisons are adult - larvae, but if there's only two groups, then there can only be one comparison. And for this comparison, if you have a positive log-fold change for adults - larvae, then "adults having higher expression" is the same as "the expression is upregulated in adults compared to larvae".
But if larvae down regulate the gene, it will appear positive in the comparison. I've seen it multiple times
even if the expression in adults remains unchanged from constitutive levels, which I have as part of my data set as a control
Please edit your question to give a concrete example with counts/CPMs for each sample; this is going nowhere.