[BioC] rma vs. call.exprs

[Benilton Carvalho]

are you really sure that you're getting different results?

all that call.exprs(obj, 'rma') does is to call rma(obj)... nothing else.

make sure you don't have any pre-existing object on your R session.

i'd be really surprised if both strategies give you discordant results...

here's a reproducible example of what i mean:

library(affydata)
library(simpleaffy)
data(Dilution)
y1 = exprs(rma(Dilution))
y2 = exprs(call.exprs(Dilution, 'rma'))
all.equal(y1, y2) ## i get TRUE, as expected

b

On 16 June 2011 11:41, Assa Yeroslaviz <frymor at gmail.com> wrote:
> Hallo everybody,
>
> I am running a microarray analysis using the miRNA2.0 arrays from
> affymetrix.
> I ran it a few times with different parameters and was wondering why I am
> getting different results in some cases but not in other.
>
> I than accidentally fond out that there is a strong different in the results
> I get when I am normalizing my data with RMA  or with call.exprs( data,
> "rma")
>
>> eset.rma_miRNA= call.exprs(data, "rma")
>
> or
>
>> normData<-rma(data)
>
> Can anyone explain to me what is the difference between the two methods. In
> the help file from call.exprs they only say that it works with the rma
> algorithm, but no more.
>
> Should there be a difference in the results?
>
> Thanks a lot for the help
>
> Assa
>
>> sessionInfo()
> R version 2.13.0 (2011-04-13)
> Platform: i386-pc-mingw32/i386 (32-bit)
>
> locale:
> [1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United
> States.1252    LC_MONETARY=English_United States.1252
> [4] LC_NUMERIC=C                           LC_TIME=English_United
> States.1252
>
> attached base packages:
>  [1] tools     tcltk     splines   stats     graphics  grDevices datasets
> utils     methods   base
>
> other attached packages:
>  [1] mirna20cdf_2.8.0       mirna102xgaincdf_2.8.0 tkWidgets_1.30.0
> DynDoc_1.30.0          widgetTools_1.30.0
>  [6] limma_3.8.2            siggenes_1.26.0        multtest_2.8.0
> simpleaffy_2.28.0      gcrma_2.24.1
> [11] genefilter_1.34.0      affy_1.30.0            Biobase_2.12.1
> rcom_2.2-3.1           rscproxy_1.3-1
>
> loaded via a namespace (and not attached):
>  [1] affyio_1.20.0         annotate_1.30.0       AnnotationDbi_1.14.1
> Biostrings_2.20.1     DBI_0.2-5
>  [6] IRanges_1.10.4        MASS_7.3-12           preprocessCore_1.14.0
> RSQLite_0.9-4         survival_2.36-5
> [11] xtable_1.5-6
>
>        [[alternative HTML version deleted]]
>
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