**0**wrote:

Dear all,

I would like to use Limma package to perform differential expression analysis on our RNA seq data, because of the flexibly of linear model.

However, as I increase the complexity of the model or the question , i am having troubles to interpret our model.

We have **3 factors** and **1 continuous factor:**

**Age** : Young and Old

**Genotype**: Transgenic and Wildtype

**Strain**: Strain A and Strain B

**Transcription factor expression **(continuous).

We are interested in

1. What is the age effect on the RNA expression

2. what is age and genotype interaction effect on the RNA expression

3. is there a difference between Strain A and Strain B in age*genotype effects.

4. Does the expression level of the transcription factor have an effect on RNA expression.

For the first 3 questions, I fitted a 3 way factorial model:

**Age*Genotype*Strain **

For the 4th question, i fitted an ANCOVA type of model:

**Age+genotype+Age:genotype+Transcription_Factor_Expression**

- I noticed that when fitting a 3 way factorial model, topTable outputs are different when i can the order of the factors. Does this mean limma uses a type I model, and the interpretation of the model will depends on which factor put in first as the main effect?
- When fitting the Transcription factor expression into the model, then i notice the order of the model doesn’t matter anymore. Does this mean limma will now treat them as a type III model when a continuous factor is fitted?

In addition, the transcription factor expression is probably not independent from the age*genotype effect,so if i fit the model ** Age+genotype+Age:genotype+Transcription_Factor_Expression, **

can I interpret the **Transcription_Factor expression** as the effect explained on top of the age*genotype effect instead of the the effect separated from age*genotype effect?

Thank you,

Ashley

**37k**• written 3.3 years ago by ashley.lu01 •

**0**