To OP: How about something like this?

library(GEOquery)
renal <- getGEO('GSE11024')[[1]]

## this is usually where you find the sample names
sampleNames(renal) <- renal$title

## 599 unannotated probesets, plus 1 dupe each for SKIP and PRG2
featureNames(renal) <- make.unique(fData(renal)$Gene, sep='.extra')

## more conveniently labeled now?
set.seed(1234)
exprs(renal)[ sample(1:nrow(renal), 5), sample(1:ncol(renal), 5) ]
##          P2_KIDNEY_12 CC_KIDNEY_1 NO_KIDNEY_2 WM_KIDNEY_79 P1_KIDNEY_4
## NHEDC2           5.84        6.11        6.07         6.21        6.04
## KIAA1370         8.51        8.98        8.29         8.97        9.04
## PRKCA            7.60        6.89        7.53         7.04        7.54
## PROP1            7.52        6.69        7.26         6.60        7.29
## HIST1H4L         5.25        5.15        5.17         5.14        5.13


There are times when you might want to fiddle around with the returned data
from GEOquery,
but IMHO this isn't one of them.  Re-read Sean's emails until you agree ;-)

--t


ps.  for completeness,

sessionInfo()
R version 3.1.0 (2014-04-10)
Platform: x86_64-pc-linux-gnu (64-bit)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
 [9] LC_ADDRESS=C               LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] splines   grDevices datasets  parallel  stats     graphics  utils
[8] methods   base

other attached packages:
 [1] mnormt_1.4-7          MASS_7.3-33           dma_1.2-0
 [4] survival_2.37-7       GEOquery_2.31.0       Biobase_2.25.0
 [7] BiocInstaller_1.15.3  rtracklayer_1.25.5    GenomicRanges_1.17.12
[10] GenomeInfoDb_1.1.3    IRanges_1.99.13       S4Vectors_0.0.6
[13] BiocGenerics_0.11.2   bigrquery_0.1         gtools_3.4.0
[16] dplyr_0.1.3

loaded via a namespace (and not attached):
 [1] assertthat_0.1          BatchJobs_1.2           BBmisc_1.6
 [4] BiocParallel_0.7.0      Biostrings_2.33.6       bitops_1.0-6
 [7] brew_1.0-6              BSgenome_1.33.2         codetools_0.2-8
[10] DBI_0.2-7               devtools_1.5            digest_0.6.4
[13] evaluate_0.5.5          fail_1.2                foreach_1.4.2
[16] formatR_0.10            GenomicAlignments_1.1.9 httr_0.3
[19] iterators_1.0.7         jsonlite_0.9.7          knitr_1.5
[22] memoise_0.2.1           plyr_1.8.1              Rcpp_0.11.1
[25] RCurl_1.95-4.1          Rsamtools_1.17.10       RSQLite_0.11.4
[28] sendmailR_1.1-2         stats4_3.1.0            stringr_0.6.2
[31] tcltk_3.1.0             tools_3.1.0             whisker_0.3-2
[34] XML_3.98-1.1            XVector_0.5.6           zlibbioc_1.11.1



Statistics is the grammar of science.
Karl Pearson <http://en.wikipedia.org/wiki/The_Grammar_of_Science>


On Thu, May 8, 2014 at 8:30 AM, Sean Davis <sdavis2@mail.nih.gov> wrote:

> On Thu, May 8, 2014 at 11:22 AM, Marcelo Pereira <marcelops@gmail.com>
> wrote:
> > One last question:
> >
> >       GSM278765 GSM278766 GSM278767 ...
> > A1BG   5.459950  5.548725  5.477436 ...
> > NAT2   6.728919  6.329578  6.570104 ...
> > ADA    6.861095  7.005730  7.235361 ...
> > CDH2   9.660035  9.189507  9.740223 ...
> > ...    5.644313  5.898675  5.475838 ...
> > ...    7.838040  7.564335  8.397569 ...
> >
> > Each CEL file has a description, telling which kind of tissue that
> sample is
> > related to.
> >
> > Is there a direct way of translating the column names  from (GSM278765,
> > GSM278766, ...) to the description of the tissue (CC_KIDNEY_1,
> CC_KIDNEY_2,
> > CC_KIDNEY_3, ...) ?
> >
> >       CC_KIDNEY_1  CC_KIDNEY_2  CC_KIDNEY_3 ...
> > A1BG     5.459950     5.548725     5.477436 ...
> > NAT2     6.728919     6.329578     6.570104 ...
> > ADA      6.861095     7.005730     7.235361 ...
> > CDH2     9.660035     9.189507     9.740223 ...
> > ...      5.644313     5.898675     5.475838 ...
> > ...      7.838040     7.564335     8.397569 ...
> >
> > Thanks,
> > Marcelo
>
> You'll need to do a little work using sub(), but this information is
> typically in one of the columns of:
>
> pData(gset[[1]])
>
> This blog post by Rafa Irizarry might be helpful to understand how an
> ExpressionSet works:
>
>
> http://simplystatistics.org/2014/02/03/the-three-tables-for-genomics-collaborations/
>
> Sean
>
>
> >
> > On Thu, May 8, 2014 at 10:21 AM, Marcelo Pereira <marcelops@gmail.com>
> > wrote:
> >>
> >> Thanks Sean,
> >>
> >> That is exactly what I was looking for!
> >>
> >> Cheers,
> >> Marcelo
> >>
> >>
> >> On Thu, May 8, 2014 at 10:15 AM, Sean Davis <sdavis2@mail.nih.gov>
> wrote:
> >>>
> >>> On Thu, May 8, 2014 at 8:21 AM, Marcelo Pereira <marcelops@gmail.com>
> >>> wrote:
> >>> > That is all because I am interested in the expression values for some
> >>> > pairs
> >>> > of genes.
> >>> >
> >>> > If I had something like this:
> >>> >
> >>> >       GSM278765 GSM278766 GSM278767 ...
> >>> > A1BG   5.459950  5.548725  5.477436 ...
> >>> > NAT2   6.728919  6.329578  6.570104 ...
> >>> > ADA    6.861095  7.005730  7.235361 ...
> >>> > CDH2   9.660035  9.189507  9.740223 ...
> >>> > ...    5.644313  5.898675  5.475838 ...
> >>> > ...    7.838040  7.564335  8.397569 ...
> >>> >
> >>> > Then I could extract lines for the genes of interest (for example,
> >>> > 'A1BG'
> >>> > and 'ADA'), and then plot scatterplots, compute correlation
> >>> > coefficients,
> >>> > etc...
> >>>
> >>> Something like this might work:
> >>>
> >>> plot(exprs(gset[[1]])[fData(gset[[1]])$Gene=='A1BG',])
> >>>
> >>> Sean
> >>>
> >>>
> >>> > The name of the genes for each line is the only detail that is not
> >>> > present
> >>> > in my dataset.
> >>> >
> >>> > What am I missing here?
> >>> >
> >>> > Thanks,
> >>> > Marcelo
> >>> >
> >>> >
> >>> >
> >>> > On Thu, May 8, 2014 at 7:42 AM, Marcelo Pereira <marcelops@gmail.com
> >
> >>> > wrote:
> >>> >>
> >>> >> Hello Sean,
> >>> >>
> >>> >> Thanks for your replies.
> >>> >>
> >>> >> I used to download all the CEL files, and then load, normalize and
> >>> >> generate the ExpressionSet output.  All manually, and it was working
> >>> >> fine!
> >>> >>
> >>> >> Then I found out about doing it automatically using the GEOquery
> >>> >> library.
> >>> >> And this is what have been taking my hours lately.
> >>> >>
> >>> >> The output of exprs(gset[[1]]) is the initial point where I got
> stuck
> >>> >> after a few minutes using the GEOquery library, because I have the
> >>> >> expression, but not the gene's names.
> >>> >>
> >>> >>       GSM278765 GSM278766 GSM278767 ...
> >>> >> 1      5.459950  5.548725  5.477436 ...
> >>> >> 10     6.728919  6.329578  6.570104 ...
> >>> >> 100    6.861095  7.005730  7.235361 ...
> >>> >> 1000   9.660035  9.189507  9.740223 ...
> >>> >> 10000  5.644313  5.898675  5.475838 ...
> >>> >> 10001  7.838040  7.564335  8.397569 ...
> >>> >>
> >>> >> After that, I tried to manipulate the output in order to translate
> 1,
> >>> >> 10,
> >>> >> 100, 1000, to the actual names of the genes.  And my last resource
> was
> >>> >> to
> >>> >> ask here at the forum.
> >>> >>
> >>> >> It is looking good already.  I only need to have an extra column,
> with
> >>> >> the
> >>> >> names of the genes.
> >>> >>
> >>> >> Thanks,
> >>> >> Marcelo
> >>> >>
> >>> >>
> >>> >> On Thu, May 8, 2014 at 7:14 AM, Sean Davis <sdavis2@mail.nih.gov>
> >>> >> wrote:
> >>> >>>
> >>> >>> On Thu, May 8, 2014 at 6:58 AM, Marcelo Pereira <
> marcelops@gmail.com>
> >>> >>> wrote:
> >>> >>> > Hi Sean,
> >>> >>> >
> >>> >>> > Thanks for your answer!
> >>> >>> >
> >>> >>> > That is great already.
> >>> >>> >
> >>> >>> > I can see the gene's names now:
> >>> >>> >
> >>> >>> >> library(GEOquery)
> >>> >>> >> gset <- getGEO("GSE11024", GSEMatrix=TRUE, AnnotGPL=TRUE)
> >>> >>> >> head(fData(gset[[1]]))$Gene
> >>> >>> > [1] A1BG NAT2 ADA  CDH2 AKT3 MED6
> >>> >>> > 17098 Levels:  A1BG ABCB6 ABCC5 ABCC9 ABCF2 ABI1 ACOT8 ACTR2
> ACTR3
> >>> >>> > ADA
> >>> >>> > ADAM8 AKT3 ... ZNF254
> >>> >>> >
> >>> >>> > But the data frame only contains these columns.
> >>> >>> >
> >>> >>> >> names(fData(gset[[1]]))
> >>> >>> >  [1] "ID"           "Gene"         "UniGene"      "Description"
> >>> >>> > "Ensembl*
> >>> >>> > Chr" "Start (bp)"
> >>> >>> >  [7] "End (bp)"     "Strand"       "ORF"          "SPOT_ID"
> >>> >>> >
> >>> >>> > Where is the expression information for each gene?
> >>> >>>
> >>> >>> exprs(gset[[1]])
> >>> >>>
> >>> >>> gset is an ExpressionSet, so you should read a bit about
> >>> >>> ExpressionSets in the Biobase vignette as well as the help page.
> >>> >>>
> >>> >>> Sean
> >>> >>>
> >>> >>>
> >>> >>> >
> >>> >>> > Thanks,
> >>> >>> > Marcelo
> >>> >>> >
> >>> >>> >
> >>> >>> >
> >>> >>> > On Thu, May 8, 2014 at 6:24 AM, Sean Davis <sdavis2@mail.nih.gov
> >
> >>> >>> > wrote:
> >>> >>> >
> >>> >>> >> Hi, Marcelo.
> >>> >>> >>
> >>> >>> >>
> >>> >>> >> On Wed, May 7, 2014 at 8:01 PM, Marcelo Pereira
> >>> >>> >> <marcelops@gmail.com>
> >>> >>> >> wrote:
> >>> >>> >> > Quick question:
> >>> >>> >> >
> >>> >>> >> > I am trying to import some GEO datasets, and having some
> issues
> >>> >>> >> > with
> >>> >>> >> > the
> >>> >>> >> > annotations:
> >>> >>> >> >
> >>> >>> >> > I can download the GSE dataset using:
> >>> >>> >> >
> >>> >>> >> > gset <- getGEO("GSE11024", GSEMatrix=TRUE, AnnotGPL=TRUE)
> >>> >>> >> >
> >>> >>> >> >
> >>> >>> >> > However, it will return me a ExpressionSet with the following
> >>> >>> >> > format:
> >>> >>> >> >
> >>> >>> >> >               X1    X10    X100   X1000 ...
> >>> >>> >> > GSM278765
> >>> >>> >> > GSM278766
> >>> >>> >> > GSM278767
> >>> >>> >> > GSM278768
> >>> >>> >> > GSM278769
> >>> >>> >> > ...
> >>> >>> >>
> >>> >>> >> This is not what is returned by GEOquery, so you have done some
> >>> >>> >> manipulation (looks like you did a transpose on the expression
> >>> >>> >> matrix), it seems.
> >>> >>> >>
> >>> >>> >> > This is pretty much what I need, but I still need to translate
> >>> >>> >> > (X1,
> >>> >>> >> > X10,
> >>> >>> >> > X100, X1000, etc...) to the actual names of the genes.
> >>> >>> >>
> >>> >>> >> library(GEOquery)
> >>> >>> >> gset <- getGEO("GSE11024", GSEMatrix=TRUE, AnnotGPL=TRUE)[[1]]
> >>> >>> >> head(fData(gset))
> >>> >>> >>
> >>> >>> >> The gene symbols are in the "Gene" column:
> >>> >>> >>
> >>> >>> >> genesymbols = fData(gset)$Gene
> >>> >>> >>
> >>> >>> >> Sean
> >>> >>> >>
> >>> >>> >>
> >>> >>> >> >
> >>> >>> >> > Any suggestions?
> >>> >>> >> >
> >>> >>> >> > Thanks,
> >>> >>> >> > Marcelo
> >>> >>> >> >
> >>> >>> >> >         [[alternative HTML version deleted]]
> >>> >>> >> >
> >>> >>> >> > _______________________________________________
> >>> >>> >> > Bioconductor mailing list
> >>> >>> >> > Bioconductor@r-project.org
> >>> >>> >> > https://stat.ethz.ch/mailman/listinfo/bioconductor
> >>> >>> >> > Search the archives:
> >>> >>> >>
> http://news.gmane.org/gmane.science.biology.informatics.conductor
> >>> >>> >>
> >>> >>> >
> >>> >>> >         [[alternative HTML version deleted]]
> >>> >>> >
> >>> >>> > _______________________________________________
> >>> >>> > Bioconductor mailing list
> >>> >>> > Bioconductor@r-project.org
> >>> >>> > https://stat.ethz.ch/mailman/listinfo/bioconductor
> >>> >>> > Search the archives:
> >>> >>> >
> http://news.gmane.org/gmane.science.biology.informatics.conductor
> >>> >>
> >>> >>
> >>> >
> >>
> >>
> >
>
> _______________________________________________
> Bioconductor mailing list
> 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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