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)  "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!