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Old 11-22-2015, 10:32 PM   #21
frymor
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Quote:
Originally Posted by dpryan View Post
No worries about being picky, this is important to get correct

But suppose the effect due to knocking out some gene is dependent upon stimulation (e.g., the stimulation activates a pathway that we've partly inactivated due to the knockout). We then would have what's referred to as an interaction between the stimulation and the condition. So we would no longer expect the knockout and stimulated group to have a value of 6, but something else entirely. The resulting interaction coefficient tells you the fold change from what you would expect if the knockout and stimulation effect don't interfere with each other (or have a synergistic effect). It could well be the case that instead of this group having a value of 6 we instead see no change from the baseline WT unstimulated group, so the value is 1, meaning that the fit coefficient is 1/6. Interactions always describe the additional change on top of what would be expected if your various conditions acted independently.
Hi Devon,

you mentioned, being picky can be a virtue, so I'm being picky again.

I was wondering about my first question for the comparison between the knock-out and the stimulation. I wanted to find the deregulated genes independently from the stimulation effect. What I did was a Wald test between the contrasts KO and wildtype. But with this comparison, I have taken both the stimulated and the non-stimulated samples in one go.
Does it make sense to do it this way, or is it better to take only the not stimulated samples.

I think that for the genes affected by the stimulation, the not-stimulated vs. stimulated samples might cancel each other, but it is a risk.
Taking all samples though gives me a higher statistical power (six instead of only three samples).

But this might also give me genes changing due to the combination of knock-out and stimulation, which I don't want to have in the first question.

What do you think is a better way of doing this analysis?


thanks
Assa
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Old 11-23-2015, 01:09 AM   #22
dpryan
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Yes, you'll get slightly better results taking everything at once and using the contrast like you did. The reason for this is simply due to the additional samples being used in dispersion estimation. If you include the interaction term in the design then you won't have that affecting things.
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Old 02-18-2019, 02:20 PM   #23
EVELE
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Hi,
Sorry for the resurrection of such old dialogues but I am quite confused.
I was thinking of doing a time-course analysis (as Assa did) having only 1 condition , 4 time points and 3 replicates for each time point. But what Ryan says here "not to use replicates as a factor" is contradictory to the answer that Michael Love gives in this link 4 months ago:
https://support.bioconductor.org/p/113630/

Should I finally consider replicates as a factor in my model or better to forget them?

Thanks for your answer in advance,
Eva

Last edited by EVELE; 02-18-2019 at 02:34 PM. Reason: First time I sent only the first sentence of my message
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