Hi All. Thank you for a very helpful discussion. I have a followup question on a remark from Gordon. "I think your approach is actually a good one *but* you need to give double weight to cases where you have averaged over two technical replicates. Use the 'weights' component of your MAList object to do this." I have never used weights in LIMMA and the helpfile of gls.series doesnt tell in what range the weights should be. I tried to search the mail archives after info regarding weights but I found only information on spotweights when creating the RG object etc. So this is how I think it is done. I have six columns of slides in my Mvalue matrix. I create a weightmatrix of the same size and give the slides that I want to give "double" weight a 2 and slides that I want to give "normal" weight 1. Something like this: weightmatrixp <- matrix(nrow = 64896, ncol = 6) weightmatrixp[,1] <- 1 weightmatrixp[,2] <- 2 weightmatrixp[,3] <- 2 weightmatrixp[,4] <- 1 weightmatrixp[,5] <- 1 weightmatrixp[,6] <- 1 And then I use this in gls.series: fitpTB <- gls.series(MpannusTB, design=designpTB, ndups=2, spacing=32448, correlation=corp$cor,weights= weightmatrixp) Is this the correct way of using weights? Best regards / Johan Lindberg >X-Sender: smyth@imaphost.wehi.edu.au >X-Mailer: QUALCOMM Windows Eudora Version 6.0.1.1 >Date: Thu, 01 Apr 2004 17:43:13 +1000 >To: Johan Lindberg <johanl@kiev.biotech.kth.se> >From: Gordon Smyth <smyth@wehi.edu.au> >Subject: Re: [BioC] Different levels of replicates and how to create a > correct targets file out of that. >Cc: bioconductor@stat.math.ethz.ch > >Dear Johan, > >Now I've had a chance to read your email more thoroughly, I think you >actually have a clever approach. > >At 11:51 PM 30/03/2004, Johan Lindberg wrote: >>Sorry, I forgot to have a subject on the mail I sent before. >> >>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 > >You have an unbalanced design with three error strata: patient, biopsy, >microarray. In principle one would like to treat this using a model with >nested random effects but, as recent discussion has indicated, this is not >so straightforward. > >>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..... > >This is actually very clever. You've got rid of one error strata by >averaging, then use duplicateCorrelation to handle the other. I think your >approach is actually a good one *but* you need to give double weight to >cases where you have averaged over two technical replicates. Use the >'weights' component of your MAList object to do this. > >>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 have understood your approach, you don't need to do anything about >the targets file or the design matrix. Just use design <- rep(1,6). You >now have independent M-values estimating the same thing. > >Gordon > >>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

Hi, I am a rather new user of Bioconductor and I have one question. Which function should I use to "loess" normalize gene intensities from an experiment conducted with AFFYMETRIX? The experiment contains 3 replicates with one treatment and one control condition for the genes. I cannot understand how I should use function "read.marrayRaw" if this is the correct one. Thanks in advance. Makis ---------------------- E Motakis, Mathematics E.Motakis@bristol.ac.uk