[BioC] ERRORS IN LIWONG EXPRESSION VALUES.

[Stephen Nyangoma]

Thanks Jim, for clarifying. I will ask for dChip if necessary. Otherwise
I will use rma.

Steve.


On Thu, 2003-08-21 at 14:57, James MacDonald wrote:
> I think your function call is correct. Unfortunately, the implementation
> of Li and Wong's invariantset method in affy sometimes doesn't converge
> due to outliers (as mentioned in the warnings). I don't think there is
> anything you can do about it except:
> 
> a.) Give up on liwong and use rma, which is a better method anyway
> 
> b.) Use dChip, which doesn't have the convergence problems.
> 
> Jim
> 
> 
> 
> 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
> 
> >>> Stephen Nyangoma <S.Nyangoma at cs.rug.nl> 08/21/03 07:51AM >>>
> Dear All, 
> I want to obtain expression values based on my data using Li-Wong
> method. I gave the following command 
> 
> liwongexpre<-expresso(Data2,normalize.method="invariantset",bg.correct=FALSE,
> pmcorrect.method="pmonly",summary.method="liwong")
> 
> It looks like I do not attain convergence. Is may call function ok?
> 
> 
> > Data2
> AffyBatch object
> size of arrays=640x640 features (12803 kb)
> cdf=MG_U74Av2 (12488 affyids)
> number of samples=4
> number of genes=12488
> annotation=mgu74av2
> >
> 
> 
> Data2<-ReadAffy(filenames=c("../M7SS02080619.CEL","../M7SS02080607.CEL")
> 
> > colnames(exprs(Data2))<- c("BXD5a","BXD9a")
> >
> liwongexpre<-expresso(Data2,normalize.method="invariantset",bg.correct=FALSE,pmcorrect.method="pmonly",summary.method="liwong")
> normalization: invariantset
> PM/MM correction : pmonly
> expression values: liwong
> normalizing...done.
> 12488 ids to be processed
> .........
> There were 50 or more warnings (use warnings() to see the first 50)
> 
> 
> > warnings()
> Warning messages:
> 1: No convergence achieved, too many outliers in: fit.li.wong(probes,
> ...)
> 2: No convergence achieved, too many outliers in: fit.li.wong(probes,
> ...)
> 3: No convergence achieved, too many outliers in: fit.li.wong(probes,
> ...)
> 4: No convergence achieved, too many outliers in: fit.li.wong(probes,
> ...)
> 5: No convergence achieved, too many outliers in: fit.li.wong(probes,
> ...)
> 6: No convergence achieved, too many outliers in: fit.li.wong(probes,
> ...)
> 7: No convergence achieved, too many outliers in: fit.li.wong(probes,
> ...)
> 8: No convergence achieved, too many outliers in: fit.li.wong(probes,
> ...)
> 9: No convergence achieved, too many outliers in: fit.li.wong(probes,
> ...)
> 10: No convergence achieved, too many outliers in: fit.li.wong(probes,
> ...)
> 11: No convergence achieved, too many outliers in: fit.li.wong(probes,
> ...)
> 12: No convergence achieved, too many outliers in: fit.li.wong(probes,
> ...)
> 13: No convergence achieved, too many outliers in: fit.li.wong(probes,
> ...)
> 14: No convergence achieved, too many outliers in: fit.li.wong(probes,
> ...)
> 15: No convergence achieved, too many outliers in: fit.li.wong(probes,
> ...)
> 16: No convergence achieved, too many outliers in: fit.li.wong(probes,
> ...)
> 
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