On Fri, Aug 17, 2012 at 6:46 AM, Salvador <salvador@bio.bsu.by> wrote:

> Dear listers,
>
> Apologies for asking a general statistics question here, but maybe someone
> will be willing to help me.
>
> I'm analysing an Illumina dataset that comes from 4 biological groups. For
> simplicity let's call them:
> Control
> Drug A
> Drug B - Concentration 1
> Drug B - concentration 2
>
> Each group has 4 biological replicates and they were hybridised across 2
> chips so that each chip had 2 samples from each group. In terms of
> biological questions asked, Drug A is being compared to Control. And two
> concentrations of Drug B are compared to control as well as to each other.
> So Drug A is never compared to Drug B.
>
> As far as I understand, for comparing Drug A to Control I have two options:
>
> 1) Extract data for Drug A and Control from the dataset and run a linear
> model on those;
> 2) Run a linear model on samples from all groups and set up contrasts to
> compare Drug A to Control.
>
> Naturally, the second option has a higher number of experimental units,
> which brings variation down and results in more differentially expressed
> genes being detected between Drug A and Control.
>
> Now my question is, is there anything wrong (ethically, statistically,
> etc) with the second option?
>
>
Hi, Aliksei.

Option 2 is the preferred option.

Sean



> Many thanks for your help!
>
> Aliaksei.
>
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