Hi all,
I know this has been explained numerous times and for various scenarios (some of which I feel are more complicated than what I'm attempting to achieve here), but I struggle to understand how to properly design my analysis..
So what I have is one cell line, infected with either control or shRNA A and B targeting single gene, in the presence or absence of over-expressed gene X at day 2 and day7. So basically:
> colData.d sample conditionX knockdown shRNA replicate timepoint 1 1 ctr ntc ntc rep1 day2 2 2 ctr ntc ntc rep2 day2 3 3 ctr ntc ntc rep3 day2 4 4 ctr knockdown shRNA-A rep1 day2 5 5 ctr knockdown shRNA-A rep2 day2 6 6 ctr knockdown shRNA-B rep1 day2 7 7 ctr knockdown shRNA-B rep2 day2 8 8 geneX ntc ntc rep1 day2 9 9 geneX ntc ntc rep2 day2 10 10 geneX knockdown shRNA-A rep1 day2 11 11 geneX knockdown shRNA-A rep2 day2 12 12 geneX knockdown shRNA-B rep1 day2 13 13 geneX knockdown shRNA-B rep2 day2 14 14 ctr ntc ntc rep1 day7 15 15 ctr ntc ntc rep2 day7 16 16 ctr knockdown shRNA-A rep1 day7 17 17 ctr knockdown shRNA-A rep2 day7 18 18 ctr knockdown shRNA-B rep1 day7 19 19 ctr knockdown shRNA-B rep2 day7 20 20 geneX ntc ntc rep1 day7 21 21 geneX ntc ntc rep2 day7 22 22 geneX knockdown shRNA-A rep1 day7 23 23 geneX knockdown shRNA-A rep2 day7 24 24 geneX knockdown shRNA-B rep1 day7 25 25 geneX knockdown shRNA-B rep2 day7
What I basically would like to know is the effect of knockdown in ctr or geneX cells at different time points. So far I basically created new factor that combines conditionX with timepoint (condition) and analyzed the "knockdown" effect. But I feel it might be better to control for differences between the shRNA A and B on the knockdown effect.. however when I that with e.g. design= ~shRNA+knockdown, I get " model matrix is not full rank" error.. ok, now if I understood correctly I should try treating this as a nested scenario? Is that correct?
Thank you so much for any suggestions on how to approach this..
Jan
Thank you so much. Yes, perhaps I'm just sort of confused what's the best approach to extract the DE information..
Bottom line, both my shRNA A and B target the same gene - so I think it's safe to assume that the DE genes that are common for both shRNA A and B will be likely specific effects, as opposed to changes seen e.g. with shRNA-A, but not with shRNA-B, which could indicate potential off-target effect. I thought initially it would be a good idea to treat both as one "knockdown" condition - which would just treat all shRNA conditions as 4 replicates - and extract genes this way.
But then I realized that even in case of specific effects - they can differ between both shRNAs due to, for instance, knockdown level etc, and perhaps it would be a good idea to account for this somehow to increase the sensitivity of DE discovery? Maybe still treating them all as one knockdown condition but with sort of "batch" effect that comes from using two different shRNAs for knockdown? So that's what I was trying to come up with.
Alternatively, as you suggested, I can also do individual shRNAs and pull out common genes.. What do you think would be best?
Thanks again!
I would get separate effects, and then you can have A specific and B specific, by combining the three factors into one new variable called 'condition' and using ~0 + condition. Finally, you can just look at those genes where both have a small adjusted p-value, if you want to find a consistent set. This is perhaps better than averaging if you want to require that both are significant.
Ok, I'll try that.Thank you!