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