Hello,

I am new to R and DeSEq. I am using DeSeq to identify DE genes in my experiment. The experiment consists of a 2x2 matrix with treatment and control administered in specific tissues. Thus creating 4 different combinations as below

1. treatment_tissue1

2. treatment_tissue2

3. control_tissue1

4. control_tissue2

I want to compare the DE genes found in the following combinations

1. treatment_tissue1 vs treatment_tissue2

2. control_tissue1 vs treatment_tissue1

3. control_tissue2 vs treatment_tissue2

I have designed my dds object as below

dds <- DESeqDataSetFromMatrix(countData = countdata, colData = coldata, design = ~ treatment )

dds$group <- factor(paste0(dds$treatment, dds$tissue))

dds$group <- relevel(dds$group, "control_tissue1")

I am not sure if I am doing it correct, please advise. Also, I do not understand how I can set up the contrast. Please help.

Someone suggested that I use a model matrix and then use contrast. Though I have set up a model matrix but I am stuck at the point where I have to set up contrasts. Please help

Dear Michael,

Thanks for your reply, I have 40 samples from 10 patients. I did follow the example mentioned in the link. I created a group like this

dds$group <- factor(paste0(dds$treatment, dds$tissue))

dds$group <- relevel(dds$group, "control_tissue1")

design(dds) <- ~ group

dds <- DESeq(dds)

So considering this design I got ~600 DE genes.

But now I also have to take into account one more factor

My design is like this

patient treatment tissue grouppatient1 treatment1 tissue1 control

patient1 treatment2 tissue1 control

patient1 treatment1 tissue2 control

patient1 treatment2 tissue2 control

patient2 treatment1 tissue1 treated

patient2 treatment2 tissue1 treated

patient2 treatment1 tissue2 treated

patient2 treatment2 tissue2 treated

patient3 treatment1 tissue1 control

patient3 treatment2 tissue1 control

patient3 treatment1 tissue2 control

patient3 treatment2 tissue2 control

patient4 treatment1 tissue1 treated

patient4 treatment2 tissue1 treated

patient4 treatment1 tissue2 treated

patient4 treatment2 tissue2 treated

patient5 treatment1 tissue1 control

patient5 treatment2 tissue1 control

patient5 treatment1 tissue2 control

patient5 treatment2 tissue2 control

patient6 treatment1 tissue1 treated

patient6 treatment2 tissue1 treated

patient6 treatment1 tissue2 treated

patient6 treatment2 tissue2 treated

patient7 treatment1 tissue1 control

patient7 treatment2 tissue1 control

patient7 treatment1 tissue2 control

patient7 treatment2 tissue2 control

patient8 treatment1 tissue1 treated

patient8 treatment2 tissue1 treated

patient8 treatment1 tissue2 treated

patient8 treatment2 tissue2 treated

patient9 treatment1 tissue1 treated

patient9 treatment2 tissue1 treated

patient9 treatment1 tissue2 treated

patient9 treatment2 tissue2 treated

patient10 treatment1 tissue1 treated

patient10 treatment2 tissue1 treated

patient10 treatment1 tissue2 treated

patient10 treatment2 tissue2 treated

and I also want to consider the variations in the patients data.

So, I generated a second design

design(dds) <- ~ group + patient

and here I got ~1000 DE genes. I do not understand why there is a difference in the DE gene numbers and also if I am dong it the right way. Please help.

Adding patient as a term should help remove the variation across patient, and improve sensitivity. This is potentially why you have more DE genes.

Note that you should follow the recommendations in the vignette and always put the variable of interest (group) at the END of the design formula: ~ patient + group.

Why should variables of interest go at the end of design formulae?

I've briefly read about sequential sums of squares. Are sequential sums of squares calculated in DESeq2, and (if so) are they calculated in the order that terms appear in the design formula?

Nearly all R packages will automatically test for the last coefficient first, if you don't provide any additional information to the software about which coefficient to test. Of course, you can also specify which coefficient to test, and then the order doesn't matter.