> The dye effect is likely to be significant for only some genes (after > normalization) because the dye is a chemical reagent that binds to the > cDNA > or RNA. > The binding properties of the 2 dyes differ, and the chemical compositions > of all cDNA fragments are different. Sorry I cannot give more exact > chemistry > info but that is the reason stripped of the technicalities. That makes sense. > The reason I put "after normalization" is that scanner settings and how > the > labeling was performed can affect the mean detection over the entire > array, > but the mean effect (over all genes) is remove during normalization. Will normalizaion completely remove the mean effect of dye? I didn't tudy spot cDNA array much, but for affy arrays, normalization can't remove effect like labs completely. I normalized data generated at two labs (similar experimental setting) together, but limma model test that the mean effect which is lab here is still significant for >50 genes, with lab*treatment interaction included. dye effect is reasonably be "dye-by-gene interaction", but lab effect seems should be mean effect for all genes. Why normalizaion still can't remove it completely? Thanks Fangxin > So, > what we are calling the "dye-effect" is called the "dye by gene > interaction" in some papers such as Churchill and Kerr, 2001. > > --Naomi > > > At 04:31 PM 1/6/2005 -0800, Fangxin Hong wrote: >>Is it possible that dye-effect is still tested to be significant for some >>genes, let's say 40% of genes? Do we remove or keep this effect for all >>genes? >>I met this problem, the factor origin (like differnent laboratories)was >>significant for > 50% genes, what I did was keeping it in the model for >>all genes. >>However, I can't figure out a nice explanation of this, like why dye >>effect is only significant for 40% of genes, what does this tell us about >>this effect. >> >>Thanks. >>Fangxin >> > I agree. In my reply to Fangxin I should have added that I would >> remove a >> > non-essential effect >> > like a dye-effect if it appeared non-significant, but I'd remove it >> for >> > all the genes. >> > >> > Gordon >> > >> > On Tue, January 4, 2005 1:18 am, Naomi Altman said: >> >> Reducing the model based on removing nonsignificant effects is called >> >> "pre-test estimation". It is known to increase the false- positive >> rate, >> >> even in the classical setting. In the microarray setting, there is >> no >> >> compelling reason to use pre-test estimators that differ from gene to >> >> gene. >> >> >> >> --Naomi Altman >> >> >> >> At 10:57 PM 1/3/2005 +1100, Gordon K Smyth wrote: >> >>> > Date: Sun, 2 Jan 2005 14:05:15 -0800 (PST) >> >>> > From: "Fangxin Hong" <fhong@salk.edu> >> >>> > Subject: [BioC] A question about Limma >> >>> > To: bioconductor@stat.math.ethz.ch >> >>> > Message-ID: <1867.66.75.240.64.1104703515.squirrel@66.75.240.64> >> >>> > Content-Type: text/plain;charset=iso-8859-1 >> >>> > >> >>> > Hi Bioconductor users; >> >>> > I have a general question about limma model. >> >>> > In limma package, usually one linear model applies to all genes, >> and >> >>> error >> >>> > variances from all genes are modified simultaneously. What if some >> >>> > factors, for example, one main effect, is only significant for >> some >> >>> genes. >> >>> > Then if we want identify genes based on the significance of >> another >> >>> main >> >>> > effect (of interest). What is the best way to do it? Currently I >> juse >> >>> > leave this factor in the model which is applied to all genes, >> >>> >> >>>That's what I do, leave all terms in the models for all the genes. I >> >>>don't see a strong case for >> >>>doing a separate model selection process for every gene. >> >>> >> >>> > but this >> >>> > might under-estimate the total number of genes on which the effect >> of >> >>> > interest is significant. >> >>> >> >>>Why do you think so? The only disadvantage of keeping a >> non-significant >> >>>term in the model is a >> >>>reduction in residual degrees of freedom, with some consequential >> loss >> >>> of >> >>>power, but this >> >>>disadvantage is mitigated by the empirical Bayes moderation process. >> >>> >> >>>Perhaps someday someone will work out a model selection theory for >> >>>massively parallel regression >> >>>situations like microarray experiments, but there isn't such a theory >> >>>now. It seems safer to me >> >>>to have the same model for every gene, keeping all the 'a priori' >> >>>important predictors in the >> >>>model. >> >>> >> >>>Gordon >> >>> >> >>> > I am sorry if this question has been asked/answered here before, I >> >>> > wouldn't find it through searching the archive. Any comment, >> >>> suggestion or >> >>> > experience is appreciated. >> >>> > >> >>> > Fangxin >> >>> > -- >> >>> > Fangxin Hong, Ph.D. >> >>> > Plant Biology Laboratory >> >>> > The Salk Institute >> >>> > 10010 N. Torrey Pines Rd. >> >>> > La Jolla, CA 92037 >> >>> > E-mail: fhong@salk.edu >> >>> >> >>>_______________________________________________ >> >>>Bioconductor mailing list >> >>>Bioconductor@stat.math.ethz.ch >> >>>https://stat.ethz.ch/mailman/listinfo/bioconductor >> >> >> >> Naomi S. Altman 814-865-3791 (voice) >> >> Associate Professor >> >> Bioinformatics Consulting Center >> >> Dept. of Statistics 814-863-7114 (fax) >> >> Penn State University 814-865-1348 >> (Statistics) >> >> University Park, PA 16802-2111 >> >> >> > >> > >> > >> >> >>-- >>Fangxin Hong, Ph.D. >>Plant Biology Laboratory >>The Salk Institute >>10010 N. Torrey Pines Rd. >>La Jolla, CA 92037 >>E-mail: fhong@salk.edu > > Naomi S. Altman 814-865-3791 (voice) > Associate Professor > Bioinformatics Consulting Center > Dept. of Statistics 814-863-7114 (fax) > Penn State University 814-865-1348 (Statistics) > University Park, PA 16802-2111 > > > > -- Fangxin Hong, Ph.D. Plant Biology Laboratory The Salk Institute 10010 N. Torrey Pines Rd. La Jolla, CA 92037 E-mail: fhong@salk.edu