I have been using MEV for a while, but I'm now face with new needs that I'm not familiar
I was using one class T test for the experimental design below using two color array and Agilent slides and protocols:
WT grown medium A x WT grown medium B during 2 hours
WT grown medium A x WT grown medium B during 4 hours
WT grown medium A x WT grown medium B during 6 hours
I have duplicate for each array (6 hybridization) and using the one class T test I got 3,700 differentially expressed genes
I have two questions:
Do you think the way I entered the data for analysis is correct. We know two replicates are not enough.
The other question is:
While performing the one class t test I used “p-value based on t distribution” in the menu p-value parameter and “just alpha no correction” in the menu p-value/false discovery correction. Therefore I don’t have information about the FDR.
How should I perform this analysis in order to have de FDR in the one class t-test?
Do I have to choose “p-value based on permutation”? How can I manage the permutation while setting the test?
Which is more stringent: “Bonferoni correction” or “adjusted Bonferoni correction”?
How can I set a FDR of 5% for example? Is it possible?
Sorry for so many questions.
I hope you could help me
First of all, I think you want a time-course analysis rather than a one-sided t-test for this dataset. I recommend checking out the BETR module or the time-course functionality in the LIMMA module.
Of course two replicates isn't ideal. But it's what you have; I would try using one of the appropriate packages and see how you do.
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