Hi Tom Sing, Thank you, this sounds reasonable. I think the analysis is conceptually not different from that of 4 replicates of 3 one-color arrays. So, you could think of the measurement for each gene as a point in 3D space, and consider different projections (e.g. on the plane normal to the average vector (1,1,1)), perhaps like Fig. 7 in http://www-huber.embl.de/pub/pdf/hvhv.pdf Depending on how that plot looks, one could attempt to detect gene set enrichment in different areas (directions) of the plot, e.g. using Hotelling's t-statistic and the applyByCategory function in the Category package; or the polar angle. Hope this helps. Wolfgang PS - was a dye swap performed between the four biological replicates? If not, I'd expect to pay a substantial amount of attention to confounding of biological effects with dye-effects. Il Oct/17/10 7:02 PM, Thomas Sandmann ha scritto: > Dear Wolfgang, > > thanks a lot for your pointers to all the different packages. > To give you a bit more information about the experiment: > > In this study, two factors are investigated: genotype and food. > Three different treatments were performed: > > A) wt genotype + normal food > B) wt genotype + supplemented food > C) mutant genotype + supplemented food > > Treatment/Genotype wt (W) mutant (M) > Normal food (N) x NA > Supplemented food (S) x x > > > (x = data available, NA = not available) > > Each treatment was performed in four biological replicates, giving rise > to 3 x 4 = 12 RNA samples. > These 12 samples were analyzed on four 3-color microarrays, > competitively hybridizing one sample from each treatment (A,B,C) to one > array. > > Two contrasts are of interest to the researchers: > > 1.) For the wt genotype: genes with differential expression between the > two food supplements (N, S) > 2.) For "Supplemented food" (S) : genes with differential expression > between wt and mutant genotypes (W, M) > > As these two question each refer to a single factor (either genotype OR > food), I could perform two separate analyses on the data e.g. by > treating the arrays like standard two-color hybridizations and > extracting only the two channels of interest each time. > > Of course, I would be grateful for any advice, > thanks, > > Thomas > > Wolfgang Huber wrote: >> Hi Thomas >> >> the NchannelSet class in the Biobase package can store such data [1], >> some of the normalisation [2] and QC-assessment [3] methods that are >> available for one- and two-color arrays can be either used directly or >> with a little adaptation to such data, as can the linear model based >> analysis of limma (e.g. by treating n 3-color arrays like 3n 1-color >> arrays). >> >> To be more specific, I think you will need to reveal the biological >> question and the experimental design behind these data. >> >> Best wishes >> Wolfgang >> >> >> [1] Have a look at the vignette of the CCl4 package "From the Genepix >> data files to RGList to NChannelSet" for an example where such an >> object is constructed, which you will need to adapt to the particular >> file format your friend uses (you'll have to modify the read.images >> function or emulate it with calls to read.table). >> >> [2] vsn, quantiles, ... >> >> [3] boxplots, MA-plots, between-array distance heatmap, such as in the >> arrayQualityMetrics package >> >> >> Il Oct/15/10 11:08 AM, Thomas Sandmann ha scritto: >>> Dear all, >>> >>> I have received data obtained using a three-color microarray platform, >>> e.g. three samples were labeled with three different fluorophores and >>> hybridized competitively to a single array. Would anyone be able to >>> point out a useful package for the analysis of three-color >>> hybridizations ? >>> >>> Thanks a lot, >>> Thomas >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >