Hello,

The question I would like to ask is : Using edgeR's GLM methode nested interaction between two explaining variable, one continous variable (unlinked to genotype), the second genotype, is this interaction affected by the compositional biases ?

The logic of my reasoning is as follow : Any potential compositional biases (such as differing sequences, slightly different length and normalisation), should not be affected differently by the continuous variable.

Are these assumption correct ?

experimental design

```
row Condition Species
1 0 0
2 0 1
3 0 0
4 0 1
...
10 1.5 1
11 1.5 0
12 1.5 1
```

Thank you for your speedy answer.

As clarification, my question is more about the edgeR assumptions being broken in this situation. From my understanding, edgeR's process assumes the equality of such things as CG and length of genes (my use of compositional biases was indeed wrong). In this situation, varying species are being compared these assumption are thus somewhat broken.

However, in the case of the analysis of an interaction (as described in my initial question), my reasoning is that such biases does not influence the outcome : Since the CG and length does not vary between one of the explaining variable, such biases's influence should not change based on that explaining variable. By this logic, the analysis of that interaction is not affected by those biases.

P.S. The design matrix provide is indeed incomplete as there is three value for the condition. However I don't think this has any bearing on the answer.

Thank you.