[BioC] warnings from li wong summary method in expresso
lgautier at altern.org
lgautier at altern.org
Wed Jan 31 02:54:50 CET 2007
Marco,
If I remember correctly, dChip authors were talking more of having
at least 25 chips. Obviously the number either 10, 16, 25 is not
a hard threshold, as the convergence depends on the very numerical
values.
Knowing what are the probes failing is possible. A quick-but-dirty
way is to only edit the function
"generateExprVal.method.liwong".
Try:
generateExprVal.method.liwong2 <- edit(generateExprVal.method.liwong)
and edit the code as:
probes <- t(probes)
if (ncol(probes) == 1) {
warning("method liwong unsuitable when only one probe pair")
list(exprs=as.vector(probes),se.exprs=rep(NA,length(probes)))
}
else {
tmp <- fit.li.wong(probes, ...)
if ( !tmp$convergence1 & !tmp$convergence2) {
id <- get("id", envir=parent.frame(3))
print(id)
}
list(exprs=tmp$theta,se.exprs=tmp$sigma.theta)
}
(the only change is near the end).
Now you can use the summary method "liwong2" in place of "liwong".
You can hack this to your specific need (and want to store the 'id'
into a variable in you global workspace for example).
Hoping this helps,
Laurent
> James,
>
> I am actually using 16 hgu133plus2 arrays, so I find it a little strange.
>
> Is there any way to know which probes failed (and how many totally) ?
>
> Does anybody know if dChip is still freely available? Seems like there is
> no
> link anymore
> on the home page... I would like to cross check if the warnings are coming
> up there as well
>
> Cherrs
>
> Marco
>
>
> On 1/25/07, James W. MacDonald <jmacdon at med.umich.edu> wrote:
>>
>> Hi Marco,
>>
>> marco zucchelli wrote:
>> > Hi,
>> >
>> > I am using in R the dChip method to normalize and summarize my micro
>> > arrays.
>> > I tried several times and I always get warnings. What this does mean?
>> Are
>> > the expression levels
>> > returned reliable anyway ?
>>
>> If you don't have enough samples, the LiWong method won't converge for
>> some of your probesets. Lack of convergence is usually not a good thing.
>> I think the recommendation for using LiWong is to have at least 10 or 15
>> samples.
>>
>> You might consider using a different method to summarize your data.
>>
>> Best,
>>
>> Jim
>>
>>
>> >
>> > I use R2.4.1 on linux redhat
>> >
>> > Marco
>> >
>> >
>> > eset <- expresso(hum.brain.embryo, bg.correct =
>> > FALSE,normalize.method="invariantset",
>> > pmcorrect.method = "pmonly",summary.method="liwong")
>> > normalization: invariantset
>> > PM/MM correction : pmonly
>> > expression values: liwong
>> > normalizing...done.
>> > 54675 ids to be processed
>> > | |
>> > |####################|
>> > There were 50 or more warnings (use warnings() to see the first 50)
>> >
>> > Warning messages:
>> > 1: No convergence achieved in outlier loop
>> > in: fit.li.wong(probes, ...)
>> > 2: No convergence achieved in outlier loop
>> > in: fit.li.wong(probes, ...)
>> > 3: No convergence achieved in outlier loop
>> > in: fit.li.wong(probes, ...)
>> > 4: No convergence achieved in outlier loop
>> > in: fit.li.wong(probes, ...)
>> >
>> > [[alternative HTML version deleted]]
>> >
>> > _______________________________________________
>> > Bioconductor mailing list
>> > Bioconductor at stat.math.ethz.ch
>> > https://stat.ethz.ch/mailman/listinfo/bioconductor
>> > Search the archives:
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>>
>>
>> --
>> James W. MacDonald, M.S.
>> Biostatistician
>> Affymetrix and cDNA Microarray Core
>> University of Michigan Cancer Center
>> 1500 E. Medical Center Drive
>> 7410 CCGC
>> Ann Arbor MI 48109
>> 734-647-5623
>>
>>
>> **********************************************************
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>>
>
> [[alternative HTML version deleted]]
>
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