Hi James Thank you Your first point solves a big problem I had: where is the zero signal in an array?. You gave me the answer: nobody knows so we ought to compare And the second, why do I think it is useful to know that? Imagine that we have the system ready to do this kind of analysis: will say in liver, in healthy conditions, no caspase 3 is expressed, or yes, that in normal situation, in spite of what has been described, caspase 3 is present. This tells you apoptosis is ready to be triggered even when the liver is in a good shape. May be in brain carbohydrate metabolism RNAs are highly represented whereas lipid ones are low represented, thus giving you information about what a tissue/cell etc needs and uses for its welfare With these naive expression data, you can tell a gene is expressed de novo vs naive, not just upregulated but newly expressed. It would be like having the tissue/cell/organ gene expression standards. Cheers! marta Marta Agudo PhD Departamento de Oftalmolog?a Facultad de Medicina Campus Espinardo 30100 Murcia- Spain Phone:+34 968363996 -----Mensaje original----- De: James W. MacDonald [mailto:jmacdon at med.umich.edu] Enviado el: martes, 12 de julio de 2005 15:53 Para: Marta Agudo CC: Bioconductor at stat.math.ethz.ch Asunto: Re: [BioC] (no subject) Hi Marta, I don't think this idea makes much sense scientifically for at least two reasons, and probably more. 1.) How exactly will you distinguish genes that are expressed from those that are not expressed? Note that in canonical microarray analyses nobody is claiming that a certain gene is expressed or not, only that it is expressed at a different level in one sample vs another. 2.) If you could somehow accurately determine which genes are being expressed, of what use is that information? When you are comparing two samples, you know phenotypically what the differences are, so you can attribute (rightly or wrongly) the differences in expression to that phenotypic difference. If you are just looking at e.g., normal liver and you find 5000 genes that are expressed, how do you attribute those genes to any phenotype or process (other than to note the trivial result that the liver appears to express these 5000 genes)? Best, Jim Marta Agudo wrote: > Hi there > > I?ve been thinking about gene expression in just one condition without > comparing to anything else. > > I explain better: I have data from an affy array experiment using naive > tissue RNA, and I want to know which genes, out of the 30000 present in the > chip, are being expressed in this tissue. > > I would like to know is this analysis is possible, i mean not just > statistically but also if scientifically has any sense, > > And if it is I would need some help > > a) is it possible to use bioconductor and GCRMA analysis ? then, anyone > knows a script or could guide me? > b) how many replicas do we need? > c) which is the cut off point? > > Basically which are the pros and the cons of this kind of analysis? > > thank you very much! > marta > > Marta Agudo PhD > Departamento de Oftalmolog?a > Facultad de Medicina > Campus Espinardo > 30100 Murcia- Spain > Phone:+34 968363996 > > > [[alternative HTML version deleted]] > > > > -------------------------------------------------------------------- ---- > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor -- James W. MacDonald Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109 734-647-5623

Hi Chen, I can not reproduce the same error in my Linux machine. Could you give us more details about the version of R and BioConductor you are using? You can use "sessionInfo()" to get these information. Ting-Yuan PS. Here is how I test this problem: > library(hgu95aprobe) Loading required package: matchprobes Loading required package: Biobase Loading required package: tools Welcome to Bioconductor Vignettes contain introductory material. To view, simply type: openVignette() For details on reading vignettes, see the openVignette help page. Loading required package: affy Loading required package: reposTools > library(hgu133atagprobe) > sessionInfo() R version 2.1.1, 2005-06-28, x86_64-unknown-linux-gnu attached base packages: [1] "tools" "methods" "stats" "graphics" "grDevices" "utils" [7] "datasets" "base" other attached packages: hgu133atagprobe hgu95aprobe matchprobes affy reposTools "0.0.1" "1.0" "1.0.22" "1.6.7" "1.5.19" Biobase "1.5.12" > On Mon, 18 Jul 2005, Chen Li wrote: > Dear bioconductor users, > > I try to use gcrma to analyze hgu133atag and hgu95a > Affymetrix genechip data, I downloaded hgu133atagprobe and hgu95aprobe > packages from the website > (http://bioconductor.org/data/probes/Packages/html/hgu133atagprobev0 .0.1 > .html, source package download) and then installed them in Linux system. > But when I load both packages, hgu95aporbe is fine, but hgu133atagprobe > get error message: > > > library(hgu95aprobe) > > library(hgu133atagprobe) > Error in .find.package(package, lib.loc, verbose = verbose) : > none of the packages were found > Error in library(hgu133atagprobe) : .First.lib failed > > Anyone got the similar problem and how to solve this problem? > > Regards > > Chen > > > ****************************************** > Dr. Chen Li > Post Doctoral Research Fellow > Systems Biology > Bioinformatics Institute > Tel: 64788319 > Fax: 64789048 > ****************************************** > > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor >