**0**wrote:

Hi,

I have a quick doubt about my design matrix and if the contrasts I'm making are correct. I have an experimental design with two factors, genotype and treatment. I have 13 genotypes (control and 12 mutants) and two treatments (A being control and B the actual treatment).

The groups made are the genoytpe*treatment interaction: wtA, wtB, mut1A, mut1B...mut12A,mut12B.

I created my design matrix without an intercept :

> design <- model.matrix(~0+Group) #Then change the names to remove the "Group" part of the name

And so, I'm interested in the genotype by treatment interaction. What is the correct formulation of my contrasts to do this? I've tried different notations with different results:

> cont.matrix <- makeContrasts(

"mut1_a"=(mut1B-mut1A)-(wtA-wtB),

"mut1_b"=(mut1B-mut1A)-(wtB-wtA),

"mut1_c"=(mut1B-wtA),

levels=design)

Then the fit, etc:

> fit <- lmFit(v,design)

> fit2 <- contrasts.fit(fit, cont.matrix)

> fit2 <- eBayes(fit2)

> results <- decideTests(fit2,lfc = 1,p.value = 0.01)

> summary(results)

mut1_a mut1_b mut1_c

-1 530 0 108

0 18504 19881 19465

1 847 0 308

I get more DE genes using the first approach which I belive is the correct one,when I check my contrast matrix all the factors that belong to control treatment (mutA wtA) are -1, while those in treatment are +1 (mutB,wtB).

Repeating this for every mutant (mut2...mut12) I get very similar results, 0 DE genes for the second-type approach, more for the first one and less for the third one.

Do you have any insight to if my reasoning is correct?

Thank you!

**24k**• written 2.5 years ago by joelrome88 •

**0**

You need to make a

limmatag, otherwise the maintainers won't get informed.24k