Hi, thanks for the quick reply!

The control group are 14 samples, 7 batch A, 7 batch B. But those 7 are the
same individuals, same RNA. So sample c1 in batch A, is coming from same
individual than sample c1 in batch B.
Hope that is cleat.

And, yes, I also treated as separate level factor, but I want to check also
this scenario, the increase or decrease of gene expression from control ->
pre-case -> case.

thanks again!


Lo


On Fri, Jul 26, 2013 at 2:29 PM, Michael Love
<michaelisaiahlove@gmail.com>wrote:

> hi Lorena
>
> Can you clarify on a few points below:
>
> On Fri, Jul 26, 2013 at 1:29 PM, Lorena Pantano
> <lorena.pantano@gmail.com> wrote:
> > Hi,
> >
> > I have some doubts about my data.
> > I would like to do DE analysis of RNAseq data.
> >
> > I have two set of experiments done in two different time points, so there
> > is probably batch effect: let's say batch A and B.
> >
> > For A, I have 7 controls and 7 cases (clinical identified)
> > For B, I have the same 7 controls and other 7 pre-cases (preclinical
> > identified)
> >
> > I wanted to know if I can analyze all together, but I have questions
> like:
> >
> >
> > 1-7 cases from A are only in batch A, and the others 7 are only in batch
> B,
> > is it correct to setup a blocked variable indicating the batch effect
> > although I only have one entire group for one batch, and another entire
> > group for another batch?
>
> You can still use blocking. Because the control group spans batch A
> and batch B, you can still estimate the batch effect (the model matrix
> should still be full rank).
>
> > 2-and 7 control for A and B are same people, so it is not quite right to
> > merge all together because it would be more like technical replicates.
> >
>
> I don't understand this last sentence. Do you mean that some, but not
> all, of individuals in the control group from batch A are also in
> batch B?
>
> I wouldn't say they are technical replicates if the samples are drawn
> from the same individual. RNA-Seq experiments of the same individual
> will contain biological variability.
>
> > My goal is:
> >
> > 1-get DE genes that follow additive effect. Something like increase a
> > little in pre-cases, and increase more in cases.
> >
>
> It sounds to me like you might want to treat pre-cases and cases as
> separate levels of a factor variable, rather than assume that the
> trend will necessarily be: control < pre-case < case. But to receive
> more specific advice, you should probably explain more about the share
> of individuals across the 28 samples.
>
> Mike
>

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