DESeq factor design, and optimal number of samples

[Lugalbanda]

For the 1st question, I want to know if I should run a single factor or double factor. These samples are match normal/tumor from different patients so something like this

p1N p1T p2N p2T p3N p3T

do i have to run a double factor run like this:

------------condition---------patientNumber
------------<factor>--------- <factor>
p1N--------normal------------ 1
p1T----------tumor ------------1
p2N---------normal-------------2
p2T----------tumor--------------2
p3N--------normal-------------3
p3T---------tumor--------------3


or a single factor like this:

-------------Condition
-------------<factor>
p1N--------normal
p1T---------tumor
p2N--------normal
p2T---------tumor
p3N--------normal
p3T---------tumor


I suppose the question boils down to whether or not DESeq cares if samples come from different patients.

For the 2nd question, I have a thousand match normal/tumor from the same type of tissue but from different patients, should I run DESeq on all one thousand of them or is it fine to do a smaller amount like one hundred?

[dpryan]

If you have tumour/normal pairs, then you should include the pairing information

design = ~patientNumber + condition

DESeq2 (there's no reason to use DESeq) can get rather slow with 1000 samples. You'd be better with limma in that case.

[SylvainL]

Hi dpryan,

how slow for 1000 samples?

[dpryan]

@SylvainL: Good question and I don't know the answer to that. Perhaps Mike Love will pop by the thread and give a guesstimate.

[fanli]

It seems from the cancer literature that most/all studies using a paired tumor-normal design only have one sample from each (e.g. p1T, p1N) and not in replicate (e.g. p1Ta, p1Tb, p1Tc, p1Na, p1Nb, p1Nc). Is that appropriate, or would it be better to do things in triplicate? Say by splitting a tumor sample into 3 sections?

[SylvainL]

Your replicates are paired (p1T, p1N, p2T, p2N, etc...) so there is no need to split.