[BioC] Advice on experimental setup

Alex Gutteridge alexg at ruggedtextile.com
Thu Sep 6 12:12:25 CEST 2012

On 05.09.2012 13:53, David Westergaard wrote:
> Hi Alex,
> There is no control group as such. One of the diets is somewhat of a
> control group, but not quite because it is still a diet that has some
> 'special' properties. I am used to working with experiments which has
> atleast one control group, so this setup is a bit out of my domain,
> which is the reason I'm asking this list for advice.
> I guess what I meant by 'differentially expressed genes for each
> diet', was a list of genes that can be attributed to this exact diet.
> Now that I think about it, it may be more appropriate to collect mRNA
> at the start, mid and end of the experiment, and measure the change 
> in
> each group, instead of comparing these. The experiment is set to run
> for 4months. I have not before dealt with experiments which have ran
> for so long.

Collecting a baseline measure sounds sensible. If these are human 
subjects you should expect a lot of variation (more than in an inbred 
animal model), the baseline measure can help correct for that.

Your question is still quite hard though. It's often useful for me to 
think through some scenarios for patterns of expresison that might 
appear and plot them out before deciding which ones will be interesting 
and then how to design the experiment to find them. E.g: Say Gene X goes 
up two fold after 4 months of Diet A and eight fold after 4 months on 
Diet B do you consider that a Diet B 'specific' gene or not? It goes up 
in both A and B, but much more in B, so either interpretation is 
possible. If you do consider that gene Diet B specific then you could do 
a contrast like (DietBEnd - DietBStart) - (DietAEnd - DietAStart), which 
shows you genes where the effect was greater in diet B than A without 
excluding genes that still showed an effect in A.

In experiments like these I am always quite wary of the temptation to 
get differentially expressed gene sets and then do set subtraction. I.e. 
Diet A 'specific' genes = Diet A DE genes - Diet B DE genes - Diet C DE 
genes. I always find that approach is very sensitive to the cutoff used 
to define DE, but it can be easier to interpret I suppose. Again if 
there is really no control diet then creating a mean 'meta-diet' might 
simplify the analysis (at the cost of the interpretation being more 
abstract). So something like: (DietAEnd - DietAStart) - ((DietAEnd - 
DietAStart)+(DietBEnd - DietBStart)+(DietCEnd - DietCStart))/3.

> Would the data collected be suited for microarray analysis? And if 
> so,
> when should the microarray analysis be performed? When each sample is
> collected, or all together at the end?

I would go for altogether at the end. RNA is very prone to degradation 
though, so you need to take all neccessary steps to preserve the samples 
(remove RNases and get to -80C) as soon as possible after collection.

> Best,
> David
> 2012/9/5 Alex Gutteridge <alexg at ruggedtextile.com>:
>> On 05.09.2012 09:55, David Westergaard wrote:
>>> Hello,
>>> I am assisting in the setup of an experiment, in which 3 groups, 
>>> each
>>> consisting of 8 subjects, will be fed 3 diets:
>>> Group 1 - Diet A
>>> Group 2 - Diet B
>>> Group 3 - Diet C
>>> We plan on using limma to identify the differentially expressed 
>>> genes.
>>> Reading the limma users guide, a factorial design matrix seems to 
>>> be
>>> appropriate. I am, however, wondering if we, by using this setup, 
>>> can
>>> elucidate the differentially expressed genes for each diet, and not
>>> just the ones between groups, e.g. when comparing Group 1 - Group 
>>> 2.
>> From your reply to Sean it's not clear what you mean by this last 
>> sentence.
>> What are the 'differentially expressed genes for each diet'? Any
>> differential expression analysis must compare groups of samples by
>> definition, no?
>> You could compare, say Diet A with the average of Diet B and Diet C 
>> (or even
>> the average of all three). Is that what you mean? Whether that makes 
>> any
>> sense depends on your experimental design. Most obviously, is one of 
>> the the
>> three diets a 'control' diet? If not then would it be appropriate to
>> consider an average of the three diets a kind of meta-control 
>> (probably not
>> a word, but hopefully you know what I mean!)?
>> --
>> Alex Gutteridge
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Alex Gutteridge

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