On Tue, Mar 20, 2012 at 8:37 AM, Ovokeraye Achinike-Oduaran <
ovokeraye@gmail.com> wrote:

> Hi,
>
> Sorry about the vagueness.
>
> This is how I have retrieved my data from GEO. I'm trying to see the
> DE of the genes across the two conditions (IR and IS). I just couldn't
> figure out how to apply this info to wilcox.test()
>
> gds157dat = getGEO('GDS157',destdir=".")
> gds157eset = GDS2eSet(gds157dat, do.log2=TRUE)
> groups= pData(gds157eset)$metabolism
> groups=as.character(groups)
> groups[groups=="insulin sensitive"]= "IS"
> groups[groups=="insulin resistant"]= "IR"
>
>
First, wilcox.test works on a gene/probe at a time, so you'll need some
type of looping structure (apply, for example).  Second, you'll need to
split your data into two vectors corresponding to the IR and IS subsets;
these two vectors will be the x and y variables in wilcox.test.

You might also look at the multtest package and consider using limma.
 Particularly since your data contain only 10 samples, rank-based methods
are going to be of limited use.

Sean





> sessionInfo()
> R version 2.14.1 (2011-12-22)
> Platform: i386-pc-mingw32/i386 (32-bit)
>
> locale:
> [1] LC_COLLATE=English_.1252  LC_CTYPE=English_.1252
> [3] LC_MONETARY=English_.1252 LC_NUMERIC=C
> [5] LC_TIME=English_.1252
>
> attached base packages:
> [1] stats4    splines   stats     graphics  grDevices utils     datasets
> [8] methods   base
>
> other attached packages:
>  [1] coin_1.0-21         modeltools_0.2-19   mvtnorm_0.9-9992
>  [4] survival_2.36-12    XML_3.9-4.1         RCurl_1.91-1.1
>  [7] bitops_1.0-4.1      puma_2.6.0          mclust_3.4.11
> [10] limma_3.10.2        ArrayExpress_1.14.0 affy_1.32.1
> [13] GEOquery_2.20.8     Biobase_2.14.0
>
> loaded via a namespace (and not attached):
> [1] affyio_1.22.0         BiocInstaller_1.2.1   preprocessCore_1.16.0
> [4] zlibbioc_1.0.0
> >
>
> Regards,
>
> Avoks
>
> On Tue, Mar 20, 2012 at 2:15 PM, Sean Davis <sdavis2@mail.nih.gov> wrote:
> >
> >
> > On Tue, Mar 20, 2012 at 7:56 AM, Vincent Carey <
> stvjc@channing.harvard.edu>
> > wrote:
> >>
> >> Please read the posting guide
> >> http://www.bioconductor.org/help/mailing-list/posting-guide/ before
> >> querying this list.
> >>
> >> You have not given any information on how you have used getGEO.  To help
> >> you, I issued
> >>
> >> > library(GEOquery)
> >> Setting options('download.file.method.GEOquery'='auto')
> >> > gg = getGEO("GDS157")
> >> File stored at:
> >>
> >>
> /var/folders/4D/4DI98FkjGzq0K2niUTEHSE+++TM/-Tmp-//RtmpGnz9Cf/GDS157.soft.gz
> >> > gg
> >> An object of class "GDS"
> >
> >
> > At this point, if you would like to work with an ExpressionSet instead
> of a
> > GDS object, try:
> >
> > expset = GDS2eSet(gg)
> >
> > Sean
> >
> >>
> >> channel_count
> >> [1] "1"
> >> dataset_id
> >> [1] "GDS157" "GDS157"
> >> description
> >> [1] "Analysis of gene expression in pooled vastus lateralis muscle
> samples
> >> from insulin-sensitive and insulin-resistant equally obese, non-diabetic
> >> Pima Indians. A search for susceptibility genes for type 2 diabetes.
>  "
> >> ...
> >>
> >> > getClass("GDS")
> >> Class "GDS" [package "GEOquery"]
> >>
> >> Slots:
> >>
> >> Name:           gpl    dataTable       header
> >> Class:          GPL GEODataTable         list
> >>
> >> Extends: "GEOData"
> >> > getClass("GEODataTable")
> >> Class "GEODataTable" [package "GEOquery"]
> >>
> >> Slots:
> >>
> >> Name:     columns      table
> >> Class: data.frame data.frame
> >>
> >> Here I am using R's self-describing capacities to learn about what the
> >> query returned.
> >>
> >> > gg@dataTable@columns
> >>    sample        metabolism
> >> 1  GSM2289 insulin resistant
> >> 2  GSM2294 insulin resistant
> >> 3  GSM2299 insulin resistant
> >> 4  GSM2304 insulin resistant
> >> 5  GSM2309 insulin resistant
> >> 6  GSM2313 insulin sensitive
> >> 7  GSM2318 insulin sensitive
> >> 8  GSM2323 insulin sensitive
> >> 9  GSM2328 insulin sensitive
> >> 10 GSM2333 insulin sensitive
> >>
> >> description
> >> 1  Value for GSM2289: insulin resistant sample pool 1 muscle on HuFL;
> src:
> >> muscle
> >> 2  Value for GSM2294: insulin resistant sample pool 2 muscle on HuFL;
> src:
> >> muscle
> >> 3  Value for GSM2299: insulin resistant sample pool 3 muscle on HuFL;
> src:
> >> muscle
> >> 4  Value for GSM2304: insulin resistant sample pool 4 muscle on HuFL;
> src:
> >> muscle
> >> 5  Value for GSM2309: insulin resistant sample pool 5 muscle on HuFL;
> src:
> >> muscle
> >> 6  Value for GSM2313: insulin sensitive sample pool 1 muscle on HuFL;
> src:
> >> muscle
> >> 7  Value for GSM2318: insulin sensitive sample pool 2 muscle on HuFL;
> src:
> >> muscle
> >> 8  Value for GSM2323: insulin sensitive sample pool 3 muscle on HuFL;
> src:
> >> muscle
> >> 9  Value for GSM2328: insulin sensitive sample pool 4 muscle on HuFL;
> src:
> >> muscle
> >> 10 Value for GSM2333: insulin sensitive sample pool 5 muscle on HuFL;
> src:
> >> muscle
> >>
> >> Now I start to see that the collection of samples may be viewed as
> falling
> >> into two classes.  If you want to use wilcox.test to address a
> two-sample
> >> problem arising from this experiment, you will have to use the
> information
> >> shown above to distinguish numerical values on gene expression into the
> >> classes.  There is more than enough information in the above to begin
> this
> >> process; for biological interpretation you need to know a little more:
> you
> >> will need to know the GPL80 is documented in the package hu6800.db.
> >>
> >> On Tue, Mar 20, 2012 at 7:24 AM, Ovokeraye Achinike-Oduaran <
> >> ovokeraye@gmail.com> wrote:
> >>
> >> > Hi all,
> >> >
> >> > I am not quite sure how to use the expression set I get from getGEO(),
> >> > say gds157, in wilcox.test().
> >> >
> >> > Please help.
> >> >
> >> > Thanks.
> >> >
> >> > Avoks
> >> >
> >> > _______________________________________________
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> >> > Bioconductor@r-project.org
> >> > https://stat.ethz.ch/mailman/listinfo/bioconductor
> >> > Search the archives:
> >> > http://news.gmane.org/gmane.science.biology.informatics.conductor
> >> >
> >>
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> >>
> >>
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> >
> >
>
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