Hi all,
I've noticed many people use Venn diagrams in RNA seq analyses for identifying genes which are commonly differentially expressed in two or more datasets. An example of this would be the following:
Two datasets where in each a comparison is made between a control and some treatment X. The first dataset was generated from say liver tissue and the second from brain tissue. Differentially expressed genes are generated for each comparison, say we are interested in only upregulated genes. So we have:
- Liver: Control vs Treatment X --> A set A of upregulated genes
- Brain: Control vs Treatment X --> A set B of upregulated genes
Then authors make claims about a set C of genes commonly differentially expressed (in this case, upregulated) in the two (and thus also about genes not commonly upregulated in the two) based on:
C = intersection(A, B)
I'm a bit skeptical about this. Given that the two datasets were analysed independently and each is assigned with an uncertainty value (e.g. an FDR rate of 10%), wouldn't claims about their fusion essentially carry an uncertainty greater than the individual ones?
If so, how would one go about generating the set C properly?
Thanks!
I'm not sure what the best way would be to properly generate set C. Treatment may be the same, but tissue and brain controls each may display different patterns of gene expression and different response to the treatment. Given the complications, authors seem to have performed differential expression analysis and examine the genes in two different settings. I could be wrong, but I don't think there is a problem with just mentioning that some genes are found to be overlapping (either as up/down-regulated) using a venn diagram.
With that said, I wonder if this link mentioning differential meta-analysis could offer you some assistance on this matter.
Thanks for the reply. I'll have a look at the link:)