Hi Alex, That was a very informative reply on a difficult question, I think you very much for your advice. I might revive this mail at some point, when we get closer to the actual statistical analysis. Best, David 2012/9/6 Alex Gutteridge <alexg at="" ruggedtextile.com="">: > 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 >>> >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor at r-project.org >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor > > > -- > Alex Gutteridge