Hello everybody, I have started to use the limma package for a couple of two-colour hybridization slides RNA/RNA... can anybody tell me if I can still use it When I have features in the array at one spacing (controls) and other features at another spacing? Or do I have to modify my gpr file? (generated by genepix) And a second issue I want to compare the above RNA(mutant)/RNA(wild type) direct method with the option: RNA(mutant)/gDNA plus RNA(wildtype)/gDNA . Could anyone advice me in type of package to carry out this type of normalization? (RNA/gDNA gives a very skewed kind of data) Thank you very much in advance Mar?a

Hello! I have a question concerning data interpretation in LIMMA. First of all I'll give you the commands: contrast.matrix<-makeContrasts(APC-ISCH,IPC-ISCH,levels=design) fitPRECOND2<-contrasts.fit(fit,contrast.matrix) fitPRECOND2<-eBayes(fitPRECOND2) clas3<-classifyTestsP(fitPRECOND2,p.value=0.01,method="fdr") vennDiagram(clas3,include="up") vennDiagram(clas3,include="down") So far, no problem! I've got nice Venn diagrams. After that my input was: topTable(fitPRECOND2,number=20,genelist=NULL,coef=1,adjust="fdr") which yielded: M t P.Value B 2380 -1.0241489 -6.058845 0.01147946 5.0373154 379 -0.8440311 -5.464009 0.02976549 3.6259203 2382 -0.9576517 -5.261334 0.02976549 3.1387221 8436 -0.6746846 -5.216491 0.02976549 3.0306287 2383 -0.7343412 -5.064616 0.03638846 2.6639293 7974 -1.0281853 -4.889561 0.04943332 2.2404780 7925 -0.8519961 -4.833610 0.04952967 2.1050515 2384 -0.7728411 -4.698536 0.05865728 1.7781412 432 0.5732701 4.656867 0.05865728 1.6773365 7680 0.5717780 4.640318 0.05865728 1.6373117 7927 -0.7303740 -4.598890 0.05865728 1.5371476 2569 0.5033168 4.579322 0.05865728 1.4898512 1688 0.6904956 4.540636 0.06028835 1.3963860 6603 0.5590289 4.506030 0.06162635 1.3128274 5487 -0.7299432 -4.425216 0.07196002 1.1179075 3247 0.6442329 4.377510 0.07698329 1.0030059 5501 -0.8589314 -4.348990 0.07839810 0.9343814 7434 0.7573137 4.293568 0.07860314 0.8011835 7230 0.6309675 4.277457 0.07860314 0.7625059 8527 -0.8768646 -4.252795 0.07860314 0.7033388 It occurred to me that none of the p-palues in the ranking was below p=0.01. Why were there significantly differentially regulated genes in my Venn diagrams (constructed using p=0.01)? Second question: Do the numbers in the first column correspont to the row number in the eset (expression set, I used RMA to do the analysis)? THANKS IN ADVANCE! ELIANA =============================== Eliana Lucchinetti Zaugg, PhD Institute of Pharmacology and Toxicology Section of Cardiovascular Research-Room 17 J 28 University of Zurich Winterthurerstr. 190 CH-8057 Zürich (Switzerland) Phone: +41-1-635 59 18 Fax: +41-1-635 68 71 e-mail: eliana.zaugg@pharma.unizh.ch [[alternative HTML version deleted]]

Hi all, It has been a while since I have used the affy package. I have done everything in the help manual and installed the HGU133 plus2 cdf environment. When I start the program I put in the correct directory and load the affy library successfully. When I try to import the cel files using the following command data<-ReadAffy() I get the error message: Error in read.affybatch(filenames = filenames, phenoData = phenoData, : The file C:/Documents and Settings/smoore/Desktop/nat evbr cel files/BR1.CEL does not look like a CEL file These files were taken from GCOS, does anyone know what may be happening here, am I maybe doing something wrong that I haven't noticed? any help would be greatly appreciated. Many Thanks Steve. Dr. Stephen Moore Dept. of Oncology Queens University of Belfast U-Floor Belfast City Hospital Belfast N.Ireland BT9 7AB

Hello everyone. I would really appreciate some comments/hints/help with a pretty long question. I have an experiment consisting of 18 hybridizations. On the 30K cDNA arrays knee joint bioipsies (from different patients) before and after a certain treatment is hybridized. What I want to find out is the effect of the treatment, not the difference between the patients. The problem is how to deal with different levels of replicates and how to create a correct target file since I have no common reference? This is how the experimental set-up looks like. Patient Hybridization Cy3 Cy5 1 1A Biopsy 1 before treatment Biopsy 1 after treatment 1B Biopsy 1 after treatment Biopsy 1 before treatment 3 2A Biopsy 1 before treatment Biopsy 1 after treatment 2B Biopsy 1 after treatment Biopsy 1 before treatment 3A Biopsy 2 before treatment Biopsy 2 after treatment 3B Biopsy 2 after treatment Biopsy 2 before treatment 4 4A Biopsy 1 before treatment Biopsy 1 after treatment 4B Biopsy 1 after treatment Biopsy 1 before treatment 5A Biopsy 2 before treatment Biopsy 2 after treatment 5B Biopsy 2 after treatment Biopsy 2 before treatment 5 6A Biopsy 1 before treatment Biopsy 1 after treatment 6B Biopsy 1 after treatment Biopsy 1 before treatment 6 7A Biopsy 1 before treatment Biopsy 1 after treatment 7B Biopsy 1 after treatment Biopsy 1 before treatment 7 8A Biopsy 1 before treatment Biopsy 1 after treatment 8B Biopsy 1 after treatment Biopsy 1 before treatment 10 9A Biopsy 1 before treatment Biopsy 1 after treatment 9B Biopsy 1 after treatment Biopsy 1 before treatment As you can see different patients have one or two biopsies taken from them. Since I realize it would be a mistake to include all those into the target file because if I have more measurements of a certain patient that would bias the ranking of the B-stat towards the patient having the most biopsies in the end, right? Or? Since the differentially expressed genes in the patient with more biopsies will get smaller variance? My solution to the problem was just to create an artificial Mmatrix twice as long as the original MA object. For the patients with two biopsies I averaged over the technical replicates (dye-swaps) and put the values from biopsy one and then the values from biopsy two in the matrix. From patients with just a technical replicate I put the values from hybridization 1A and then hybridization 1B into the matrix. The M-values of that matrix object would look something like: patient 1 patient3 .... Rows 1-30000 Hybridization 1A Average of hybridization 2A and 2B .... Rows 30001-60000 Hybridization 1B Average of hybridization 3A and 3B .... After this I plan to use dupcor on the new matrix of M-values, as if I would have a slide with replicate spots on it. So far so good or? Is this a good way of treating replicates on different levels or has anyone else some better idea of how to do this. Comments please..... And now, how to create a correct targets file since I have no common reference. I guess it would look something like this: SlideNumber Name FileName Cy3 Cy5 1 pat1_p test1.gpr Before_p1 After_p1 2 pat3_p test2.gpr Before_p2 After_p2 3 pat4_p test3.gpr Before_p3 After_p3 4 pat6_p test4.gpr Before_p4 After_p4 5 pat7_p test5.gpr Before_p5 After_p5 6 pat10_p test6.gpr Before_p6 After_p6 But when I want to make my contrast matrix I am lost since I do not have anything to write as ref. design <- modelMatrix(targets, ref="????????") If I redo the matrix to SlideNumber Name FileName Cy3 Cy5 1 pat1_p test1.gpr Before_p After_p 2 pat3_p test2.gpr Before_p After_p 3 pat4_p test3.gpr Before_p After_p 4 pat6_p test4.gpr Before_p After_p 5 pat7_p test5.gpr Before_p After_p 6 pat10_p test6.gpr Before_p After_p wouldnt that be the same as treating this as a common reference design when it is not? And wouldnt that effect the variance of the experiment? How do I do this in a correct way. I looked at the Zebra fish example in the LIMMA user guide but isnt that wrong as well. Because technical and biological replicates are treated the same way in the targets file of the zebra fish. I realize that many of these questions should have been considered before conducting the lab part but unfortunately they were not. So I will not be surprised if someone sends me the same quote as I got yesterday from a friend: "To consult a statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of." - R.A. Fisher, Presidential Address to the First Indian Statistical Congress, 1938 Best regards /Johan Lindberg