**2.2k**wrote:

Hi all,

I have 4 paired samples in treatment vs control at 3 different time points.

How can I set up a `model.matrix`

so I can treat them as paired samples and compare the following comparisons in `glmLRT`

of edgeR:

- Treatment vs control (paired samples), regardless of time
- Treatment vs control (paired samples) at T1
- Treatment vs control (paired samples) at T2
- Treatment vs control (paired samples) at T3

Tentative code:

```
subject <- factor(c(rep("1", 3),rep("2", 2),rep("3", 3),rep("4", 3),
rep("1", 3),rep("2", 3),rep("3", 3),rep("4", 3)))
condition <- factor(c(rep("treatment",11), rep("control",12)), levels=c("control","treatment")) #missing one treatment data
timepoints <- factor(c("t1","t2","t3","t1","t2","t1","t2","t3","t1","t2","t3",
"t1","t2","t3","t1","t2","t3","t1","t2","t3",
"t1","t2","t3"), levels=c("t1","t2","t3"))
sampleTable <- data.frame(subject = as.factor(subject),
condition = as.factor(condition),
timepoints = as.factor(timepoints))
design <- model.matrix(~subject+condition+timepoints+condition:timepoints)
fit <- glmFit(y, design)
```

**Can my model.matrix fulfill what I need?**

Sample table:

subject condition timepoints

1 treatment t1

1 treatment t2

1 treatment t3

2 treatment t1

2 treatment t2

3 treatment t1

3 treatment t2

3 treatment t3

4 treatment t1

4 treatment t2

4 treatment t3

1 control t1

1 control t2

1 control t3

2 control t1

2 control t2

2 control t3

3 control t1

3 control t2

3 control t3

4 control t1

4 control t2

4 control t3

Not an actual answer but just a question I got by looking at your design matrix, shouldn't you add a +0 such that the first column doesn't represent the intercept? (

`design <- model.matrix(~0+subject+condition+timepoints+condition:timepoints)`

). Also as I was writing this I checked out the manual and this seems very similar to the section 3.3 of the edgeR User Guide, perhaps it is worth a look?470The intercept doesn't matter, some people replace it since it makes their contrasts easier to specify or explain.

5.5k