Hi,

I've done a model design and I hope someone can help out with my understanding of it!

I have an experimental setup that looks something like this:

**3 Time points (0hrs, 6hrs, 12hrs)**

**3 Different Conditions (Treatments A, B and C)**

So that makes **9 different combinations** of time points and treatments, each is in triplicate. There are therefore **27 Samples**.

My design formula is : `~ Treatment + TimePoint + Treatment:TimePoint`

My current understanding is this will give me small p-values of Treatment-specific effects over time?

I wanted to further refine this and look at a specific treatment and how it differs between two time points. So I used the following line:

```
foo <- list(c("TreatmentA.TimePoint12hrs"), c("TreatmentA.TimePoint0hrs"))
resMFType <- results(dds, contrast=foo)
```

Is this correct?

Thanks
<p>&quot;My current understanding is this will give me small pValues of Treatment-specific effects over time?&quot;</p>

<p>I wouldn&#39;t say that, rather you&#39;re fitting with a model that can measure treatment-specific effects over time (i.e., time:treatment interactions as well as time-specific and treatment-specific effects). Whether the resulting p-values are small or not depends on whether there are any significant effects in the dataset at hand (yes, I&#39;m being rather nit-picky here :) ).</p>

<p>BTW, you can shorten your design to <code>~Treatment*TimePoint</code>.</p>

<p>The contrast you mentioned looks correct for looking at changes between 12 and 0hrs within treatmentA.</p>

<p>Anyway, you seem to have the correct design and know how to form the contrasts, so you should be good to go!</p>
