Fold changes for different gene expression datasets?

Question

[Nitin]

Hello,

I have 3 gene expression datasets with different conditions. I would like to know is there any rule that we should apply same fold change for all 3 conditions. For example: Now I used 2 Fold change (FC) for the first dataset, for other datasets I used 1.5 FC. Because when I applied FC 2 for these datasets I got very less number of genes. So I decided to apply FC 1.5. Now my question should I also apply 1.5 for the first dataset? or can I leave it as it is. Can anybody give some suggestions on this?

Thanks,
Sai

[Devon Ryan]

I hope you're filtering by adjusted p-value first.

[Nitin]

Yes I am using adjusted P-value.

Answer 1

[Antonio R. Franco]

You should interpreter your data, trying to see if you get something meaningful for you

For example. Our group has analyze the DE in a plant, and with a Fold Change higher than 2, we ended with about 1800 DE genes. Many of these genes could explain the biology of the treatment we did, and we got many answers to our knowledge

But we did the same with a bacteria that was treated with and without an iron scavenger. Fold Changes were not higher that 1.7 in the best case, but the list of DE genes was full of iron transporters being induced only when iron was missing. And this is meaningful for us..

Answer 2

[Devon Ryan]

Which if any threshold to apply should depend on (A) the underlying biology, (B) what the results will be used for and (C) how many replicates you have, since more replicates allows you to more reliably see smaller changes.