Dear Naomi, Thanks for your initial response. My intention was to test out the single channel analysis (SCA) approach on a very simple and easy dataset. The reason behind choosing SCA for this dataset was based upon finding a larger amount of genes that reacted inconsistently (|FC| >1.4 and opposite direction on both the normal and the dye flip) during my two-channel analysis compared with the amount of genes that reacted consistently on both arrays. This could be explained as a possible dye-effect and I thought that a SCA approach might improve these type of 'weird' results, at least for that specific list of genes. I do understand that the SCA enables you to do indirect comparisons between different two-coloured arrays (using contrasts), and that that is not my main aim here. I'm trying to catch up on the subject, so do correct me if I'm wrong! (-; I do have other experimental Agilent data constructed in a more general loop-design (also dye-flipped) with the same issue at hand. However, if I use the SCA for that, then I'd still come across my 'pretty basic' questions mentioned below, i.e. take dye-effect into design-file and bypassing the 'error' in the intraspotCorrelation function (-: Best wishes, Stan -----Original Message----- From: Naomi Altman [mailto:naomi@stat.psu.edu] Sent: 03 October 2006 05:13 To: Gaj Stan (BIGCAT); bioconductor at stat.math.ethz.ch Subject: Re: [BioC] Single Channel Approach for Agilent Arrays While I always use the single channel approach for loop designs, I see no compelling reason to use it for a reference design, which is what you have here. Since you appear to have technical replicates for the dye swap, I would use the 2-channel approach, and save the "blocks" for the technical replications. --Naomi At 09:17 AM 10/2/2006, Gaj Stan (BIGCAT) wrote: >Dear BioConductor-user, > >I've recently tested out Chapter 9 in the Limma user documentation >concerning applying single channel approach on Agilent arrays. My >experimental setup consists of two different (pooled) food >interventions in dye flip using the same control sample (n=4 - For >each experiment, 1 array + dye-flip). I assume that my ultimate >single-channel design matrix would look like this > > fControl fFood1 fFood2 >(1) 1 0 0 >(2) 0 1 0 >(3) 0 1 0 >(4) 1 0 0 >(5) 1 0 0 >(6) 0 0 1 >(7) 0 0 1 >(8) 1 0 0 > >Where: >Food1_vs_Control.Cy3 (1) >Food1_vs_Control.Cy5 (2) >Control_vs_Food1.Cy3 (3) >Control_vs_Food1.Cy5 (4) >Food2_vs_Control.Cy3 (5) >Food2_vs_Control.Cy5 (6) >Control_vs_Food2.Cy3 (7) >Control_vs_Food2.Cy5 (8) > >Question 1: Am I missing any information here? Because if I do this >as suggested in the manual, I get a matrix with some extra (and >empty) attributes such as assign (1,1,1), contrasts (NULL) and >contrasts$f ("contr.treatment"). Are these attributes necessary for >further steps? > >Question 2: In my normal two-channel analysis approach I added an >extra column in my design file that includes the DyeEffect in the >statistical analysis (Ebayes) afterwards. Do I need to include them >here as well? > >The next part is calculating the intraspotCorrelation, based upon >the design file above. When executing, I get the following error: >"Missing or Infinite Values found in M or A". This error should >indeed occur, since individual spots of bad quality were flagged as >NA before normalization, resulting in no M or A value (missing >value). Is there a way to omit these spots from the >intraspotCorrelation function (By changing them to 0 it might have >an effect on the average correlation factor?) or does something else >need to be done to solve this? > >Any suggestions would be greatly appreciated! > >Best wishes, > > Stan > > >--------------------------------------- >Stan Gaj, MSc >PhD Student >Dept. of Human Biology / BiGCaT Bioinformatics / Nutrigenomics Consortium >PO BOX 616 >UNS 50 - Box 28 >University Maastricht >6200MD Maastricht >the Netherlands > >Tel: +31 (0)43 3882913 >Fax: +31 (0)43 3670976 > > >_______________________________________________ >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 Naomi S. Altman 814-865-3791 (voice) Associate Professor Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111