Dear list, I obtain different values for chip effects using fitPLM or rmaPLM: >Pset<-fitPLM(Data,model=PM~-1+probes+samples,output.param=list(weight s=TRUE)) >Pset.rma <-rmaPLM(Data,output.param=list(weights=TRUE)) I did not expect that as I thought that, by default, both procedure use the same bkg+normalization+summarization Any help will be welcome Regards, Linux AMD Opteron 64bit R Version 2.0.1 affyPLM 1.2.5 affy1.5.8 Ariel./ -- Ariel Chernomoretz, Ph.D. Centre de recherche du CHUL 2705 Blv Laurier, bloc T-367 Sainte-Foy, Qc G1V 4G2 (418)-525-4444 ext 46339

The bkg and normalization routines in your calls are identical, the difference lies in the summarization algorithm. rmaPLM() uses the median polish so that the chip effects returned are identical to the values you get out of the rma() function. However, rmaPLM() returns a PLMset object, which means it is possible to get the resulting residuals and probe-effect coefficients estimates. Note that the weights returned from rmaPLM() are synthetic (ie not part of the modeling procedure) but may satisfactorily be used for visualization. fitPLM() uses robust regression for the model fitting procedure. The weights returned are the weights used in the final stage of the iterative reweighted least squares fitting algorithm. Ben On Wed, 2005-05-04 at 16:02 -0400, Ariel Chernomoretz wrote: > Dear list, > > I obtain different values for chip effects > using fitPLM or rmaPLM: > > >Pset<-fitPLM(Data,model=PM~-1+probes+samples,output.param=list(weig hts=TRUE)) > >Pset.rma <-rmaPLM(Data,output.param=list(weights=TRUE)) > > I did not expect that as I thought that, by default, both procedure use the > same bkg+normalization+summarization > > Any help will be welcome > Regards, > > Linux AMD Opteron 64bit > R Version 2.0.1 > affyPLM 1.2.5 > affy1.5.8 > > > Ariel./ > >