>To: Pedro L?pez Romero <plopez at="" cnic.es=""> >From: Naomi Altman <naomi at="" stat.psu.edu=""> >Subject: Re: [BioC] implementing limma with several sistematic effects >Cc: >Bcc: >X-Eudora-Signature: <work> >Date: Fri, 10 Mar 2006 16:21:54 -0500 > >I would say that the problem is that the 6 cell >lines are nested in the 3 sib pairs. The >simplest way to handle this is just to leave out the sib effect. >If you want to estimate the sib effect, you need >to code the cell lines with only 2 levels (sib A >and sib B in each pair) and include the sib*cell >line interaction. The cell line "main effect" >and interaction do not really have a meaning in >this context, but the two effects together are >the cell line effect you tried to estimate in your set-up below. > >--Naomi > > >At 03:17 PM 3/10/2006, you wrote: >>Dear list, >> >>I am triying to use limma to compare 6 different treatments.- According to >>how the data have been generated, appart from the treatment effect, there >>are two other clear effects 1) due to the cell lines where the treatments >>have been applied and 2) due to the genetic relationship between the animals >>where the cells were extracted.- Well, I would like to make a comparison >>between treatments taking into account the variability that these tow >>additional sistematic effects introduce in the data.- The model equation >>would be: >> >>Y = treatment + cell + sib + error .- >> >>I am constructing the design matrices manually as follows: >> >>sibEFF=factor(c("mr1","mr1","mr1","mr2","mr2","mr2","mr3","mr3","mr3 ", >>"mr1","mr1","mr1","mr2","mr2","mr2","mr3","mr3","mr3"), >>levels=c("mr1","mr2","mr3")) >> >>claEFF=factor(c("c1","c1","c1","c2","c2","c2","c3","c3","c3","c4","c 4","c4", >>"c5","c5","c5","c6","c6","c6"), levels=c("c1","c2","c3","c4","c5","c6")) >> >> >>ttoEFF=factor(c("t1","t2","t3","t1","t2","t3","t1","t2","t3","t4", >>"t5","t6","t4","t5","t6","t4","t5","t6"), >>levels=c("t1","t2","t3","t4","t5","t6")) >> >>design=model.matrix(~ - 1 + ttoEFF + claEFF + sibEFF) >>CM= makeContrasts(t1-t2,levels=ttoEFF) >>fit=lmFit(eset,design) >> >>I do not know how the parameters of the model are estimated. I guess that >>with this model equation, the X?X matrix is not singular and some >>coefficients of the cell and sib effects can not be estimated.- >> >> >From here I get an error message when I apply fit2=contrasts.fit(fit,CM). I >>have to be doing something wrong but I do not know what it is. >> >>I was thinking to fit first the linear model >> >>Y = cell + sib + error >>and from here I think I could use limma with the estimated error terms to >>get the DE genes due to the treatment effect. Could be this a good strategy? >> >> >>I would appreciate very much if someone could give me any advice. >> >>Other think that I would like to know is if it s possible to check the >>quality of the limma models (some residual analysis, QQ-plots, BIC , ). >> >>Thanks for any suggestion.- >> >>Pedro.- >> >> >> >> >> >> >> >> [[alternative HTML version deleted]] >> >>_______________________________________________ >>Bioconductor mailing list >>Bioconductor at stat.math.ethz.ch >>https://stat.ethz.ch/mailman/listinfo/bioconductor > >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 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