Hi everyone, Hi Michael,

I am using DESeq2 to analyze small RNA sequencing results, and I am very uncertain about the results I am getting for a particular design, so I would really appreciate some help.

The following is my coldata and my design

```
head(colDesign)
Age BMI
sample_01 17.0 23.03
sample_02 16.1 23.66
sample_03 10.4 18.56
sample_04 15.3 39.57
sample_05 14.5 25.19
sample_06 18.1 18.88
dds=DESeqDataSetFromMatrix(countData = cts_bmi,
colData = colDesign,
design = ~ Age+BMI+Age:BMI)
DESeq(dds)
resultsNames(dds)
[1] "Intercept" "Age" "BMI" "Age.BMI"
```

As you can see, I want to study the interaction between 2 continuous variables, age and BMI. I read the interaction examples on the help page and I also read Michael's answer on a thread regarding the interaction between a discrete and a continuous variable, from what I learned is that, there's no reference point for the continuous variable, it's all embedded in the intercept? So for my design, results(dds, name="Age.BMI") will give the effect of BMI across all ages? Is that correct? And also if I want to use LRT test, should the reduced model be reduced=~Age+BMI?

Thank you in advance, any help is greatly appreciated!

Thank you very much Mike! I have another similar question, so I have a design

`design= Sex+Sex:Age`

, and the resultsNames are`"Intercept" "Sex_M_vs_F" "SexF.Gestational.Age..wks." "SexM.Gestational.Age..wks."`

If my understanding is right,`results(dds, name="Sex_M_vs_F)`

is the main effect (the gender effect across all ages), and if I do`results(dds, contrast=list("SexF.Gestational.Age..wks." "SexM.Gestational.Age..wks."))`

, it will test if the age effect is different between males and females? Isn't that the same as the main effect?See the interactions section of the vignette, and then for further questions I'd recommend consulting with a local statistician. In particular, the main effect is not averaging across all ages.