Dear all,

We are currently analyzing RNA-seq data using DEseq and would like to 
ask for your advice regarding the possibility to treat a variable as a 
"random effect" rather than a "fixed effect".

In our experiment, we are working with clonal individuals (aphids). We 
are working with three different clones. Each of these clones is fed 
with two different diets (treatment).We have two biological replicates 
for each condition. Hence, we have a total of 12 RNAseq samples (3 
different clones x two diet treatment x 2 replicates). We are mainly 
interested in identifying genes that are differentially expressed in 
response to the diet.

Currently, we are running GLM models in DESeq:

*Model0 <- fitNbinomGLMs( cds, count ~diet * cloneID )*

We then test for the significance of each variable (i.e. cloneID, diet, 
and the interaction diet:cloneID) by comparing the model including the 
variable of interest to the model estimated without this variable.

To test for the effect of the diet:

*Model1 <- fitNbinomGLMs( cds, count ~cloneID)*

*Model2<-fitNbinomGLMs( cds, count ~cloneID + diet )*

*Diet_effect <- nbinomGLMTest(Model2, Model1)*

*Diet_effect_adjusted< p.adjust(Diet_effect, method="BH" )*

To test for the effect of the interaction:

*Model0 <- fitNbinomGLMs( cds, count ~diet * cloneID ).*

*Model3 <- fitNbinomGLMs( cds, count ~diet + cloneID ).*

*Interaction_effect<- nbinomGLMTest(Model0, Model3)*

*Interaction_effect _adjusted <- p.adjust(Interaction_effect, method="BH" )*

For genes with a significant interaction between diet and CloneID, we do 
not interpret the effect of each variable (i.e. diet and CloneID).

We are however not entirely satisfied by our model, because we think it 
would be better to treat the variable cloneID as a random effect. Is it 
possible to conduct such mixed-effects analysis within DEseq?

Thank you very much in advance.
Akiko

-- 
Akiko Sugio, Ph.D.
INRA, UMR Institut de Génétique, Environnement et Protection des Plantes (IGEPP)
Domaine de la Motte, 35653 Le Rheu cedex - France
tel: 00 33 (0) 2 23 48 51 53
akiko.sugio@rennes.inra.fr


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