Hi there,

My question is rather theoretical and regards the wording used to describe a multi-factor design in the vignette of Deseq2 package. For example in the following design:

```
~genotype + condition
```

The vignette mentions that the `condition`

effect represents the overall effect controlling for differences due to `genotype`

.

However when I start reading some books about multi-factor design and interaction, including here one book written by Michael one of the authors of `DESeq2`

I get the following from this book named Data Analysis for The Life Sciences.

```
X<-model.matrix(~type+leg, data=spider)
colnames(X)
"(Intercept)" "typepush" "legL2""legL3""legL4"
```

So here they point out a model with one factor `type`

with two level `push`

and `pull`

and another factor `leg`

with 4 levels `leg1`

`leg2`

`leg3`

and `leg4`

. Then they go ahead and make the following affirmation about the model `~type+leg`

" In the previous linear model, we assumed that the push vs. pull effect was the same for all of the legpairs"

So if the `push`

`pull`

effect assumption is the same for all legpairs, how can an additive model control for differences in the first term ?

For example:

```
~genotype + condition
```

`condition`

effect controls for differences due to `genotype`

but `~type+leg`

assumes that level differences are the same for all `leg`

levels.

So generalizing what we can say about this model ?

`~ factor1 + factor2`

Does the `factor2`

effect control for differences in `factor1`

or does the levels of `factor1`

are assumed to be the same for all levels of `factor2`

??

Thanks.