that is a very cool resource -- if it could be filtered the way GEO
metadata can be, it would be just about perfect

thank you for the pointer (speaking for myself here), I am bookmarking it
for my own later use :-)

best,

--t


On Wed, Jul 24, 2013 at 7:03 PM, Levi Waldron
<lwaldron.research@gmail.com>wrote:

> Lina,
>
> you might try using insilicodb.org to facilitate the curation you need to
> do, then import ExpressionSets with your curated metadata from there.
>
> -Levi
>
>
> On Wed, Jul 24, 2013 at 4:06 PM, Lina Thomas <linadth6@gmail.com> wrote:
>
>> Well thanks a lot! In some way that is going to be really usefull! I will
>> try to learn some SQL code.. But actually I think I am really screwed...
>>
>> Because working with gds is not enough... I will nedd to work with gse
>> also, specially the ones that are no associated with a gds..
>> I am working with mus musculus and I need around 1000 different expression
>> tables. When I search for mus musculus in
>> https://www.ncbi.nlm.nih.gov/sites/GDSbrowser/
>> it returns around 1300 DataSet records and I could only find around 250
>> that have more than 20 samples... And then I have to filter the ones that
>> I
>> can identify possible transformations: log2, log10, etc
>>
>> I know the description is broad but that is what was asked for me to do...
>>
>> So I am starting to belive I will have to work with data one by one in R
>> to
>> choose states :(
>>
>>
>> On Wed, Jul 24, 2013 at 4:43 PM, Tim Triche, Jr. <tim.triche@gmail.com
>> >wrote:
>>
>> > Incidentally (picking up where the previous example left off), one can
>> > catalog the number of subsetted GDS records and the states by which they
>> > were subsetted as follows (assuming I have correctly understood your
>> > meaning for "states"):
>> >
>> > gds.with.subsets <- dbGetQuery(con, 'SELECT COUNT(DISTINCT gds) AS
>> > subsetted_datasets, type AS state FROM gds_subset GROUP BY type')
>> >
>> > gds.with.subsets
>> > ## data frame with 24 rows and 2 columns
>> > ##     subsetted_datasets             state
>> > ##              <integer>       <character>
>> > ## 1                  184               age
>> > ## 2                  866             agent
>> > ## 3                  145         cell line
>> > ## 4                  233         cell type
>> > ## 5                  136 development stage
>> > ## ...                ...               ...
>> > ## 20                 231            strain
>> > ## 21                 112            stress
>> > ## 22                  10       temperature
>> > ## 23                 776              time
>> > ## 24                 330            tissue
>> >
>> > Again, it may take a bit of SQL to assemble the results such that you
>> can
>> > troll through them with GEOquery, but I suspect that the process will be
>> > less painful than if you did this manually through GEO2R.  But that's
>> > something for you to decide.
>> >
>> > Best,
>> >
>> > --t
>> >
>> >
>> >
>> > On Wed, Jul 24, 2013 at 12:33 PM, Tim Triche, Jr. <tim.triche@gmail.com
>> >wrote:
>> >
>> >> You might want to look in gds_subset instead, for instances where
>> defined
>> >> differences exist.  If you're searching free-form for arbitrary
>> differences
>> >> then yes, you're pretty much screwed.  But for instances where GEO2R
>> can
>> >> see the differences, GEOmetadb can see them as well.  You may need to
>> poke
>> >> around a bit, though, since your description is so broad (nothing
>> personal,
>> >> it's just an incredibly broad description and you may be the only
>> person
>> >> who can narrow it).
>> >>
>> >> I'm cc:'ing Sean on this, since he's more familiar with the package
>> (his
>> >> package!) than I am, but here's a start...
>> >>
>> >>
>> >> library(GEOmetadb)
>> >> geometadbfile <- getSQLiteFile() ## if needed
>> >> con <- dbConnect(SQLite(), geometadbfile)
>> >>
>> >> colDescs <- columnDescriptions()
>> >> colDescs[ grep('gds_subset', colDescs[,1]), ]  ## ooh, this looks
>> >> promising...
>> >>
>> >> gds.res <- dbGetQuery(con, 'SELECT DISTINCT gds, type, description,
>> >> count(sample_id) AS samples FROM gds_subset GROUP BY type, gds')
>> >>
>> >> gds.res
>> >> ## data frame with 5498 rows and 4 columns
>> >> ##             gds        type                    description   samples
>> >> ##     <character> <character>                    <character> <integer>
>> >> ## 1        GDS1054         age                          36 wk
>> 5
>> >> ## 2        GDS1055         age                          36 wk
>> 5
>> >> ## 3        GDS1079         age                          23 mo
>> 2
>> >> ## 4        GDS1264         age                           20 m
>> 3
>> >> ## 5        GDS1265         age                           22 d
>> 5
>> >> ## ...          ...         ...                            ...
>> ...
>> >> ## 5494      GDS910      tissue                    bone marrow
>> 5
>> >> ## 5495      GDS917      tissue                    whole blood
>> 2
>> >> ## 5496      GDS953      tissue           non-branching region
>> 2
>> >> ## 5497      GDS956      tissue substantia nigra pars compacta
>> 2
>> >> ## 5498      GDS960      tissue                     right lung
>> 2
>> >>
>> >>
>> >> It may take some further twiddling on your part but I'd be surprised if
>> >> this can't be pressed into service for your needs... It could take a
>> while
>> >> to extract 5498 subsets of data from GEO2R ;-)
>> >>
>> >> Best,
>> >>
>> >>
>> >> --t
>> >>
>> >>
>> >>
>> >> On Wed, Jul 24, 2013 at 11:58 AM, Lina Thomas <linadth6@gmail.com>
>> wrote:
>> >>
>> >>> Hello, thanks for your answer!
>> >>>
>> >>> I am studying the alterations of gene expression when a biological
>> >>> system suffers a change. So any change, any difference can be a
>> state. You
>> >>> can also call it a condition.
>> >>>
>> >>> With GDS I know where to find these information (disease.state or
>> >>> infection or stress or tissue, etc), but GSE is giving me a little
>> >>> headache. I am starting to think the information is hidden in the
>> fields
>> >>> title or source name (the two columns in GEO2R). But I can`t think of
>> an
>> >>> automatic way to separate the data...
>> >>>
>> >>> I took a quick look at GEOmetadb but it does not seem that is quite
>> what
>> >>> I need. It might be helpful in the future though. Thanks anyway!
>> >>>
>> >>> See ya,
>> >>>
>> >>> Lina
>> >>>
>> >>>
>> >>> On Wed, Jul 24, 2013 at 3:02 PM, Tim Triche, Jr. <
>> tim.triche@gmail.com>wrote:
>> >>>
>> >>>> how do you define "states"?
>> >>>>
>> >>>> You might find the GEOmetadb package very useful.  Read the vignette
>> >>>> for it.  It may be what you need.
>> >>>>
>> >>>> In terms of processing GSEs and GSMs, once you know what you are
>> >>>> searching for, it's much easier to find it.
>> >>>>
>> >>>> Hope this helps.
>> >>>>
>> >>>> --t
>> >>>>
>> >>>>
>> >>>>
>> >>>> On Wed, Jul 24, 2013 at 10:55 AM, Lina Thomas [guest] <
>> >>>> guest@bioconductor.org> wrote:
>> >>>>
>> >>>>>
>> >>>>> I am trying to work with GSE expression data from GEO in R using
>> >>>>> GEOquery.
>> >>>>>
>> >>>>> I am using the following commands
>> >>>>> gset <- getGEO("GSE52",GSEMatrix =TRUE,)
>> >>>>> ex <- exprs(gset[[1]])
>> >>>>>
>> >>>>> But I have no idea how to separate the data in two different
>> states. I
>> >>>>> am trying to locate states data using
>> >>>>>
>> >>>>> varLabels(gset[[1]]@phenoData), when GSEMatrix =TRUE
>> >>>>>
>> >>>>> or
>> >>>>>
>> >>>>> Meta (gse), when GSEMatrix =FALSE
>> >>>>>
>> >>>>> But I still have no clue. I need to do this for around 1000
>> different
>> >>>>> database, so I cannot do it using GEO2R manually... Is there a
>> particular
>> >>>>> field I can consider state?
>> >>>>>
>> >>>>> I also would like to know I can find out if data is already log2
>> >>>>> transformed or log10 or inits raw form using just R.
>> >>>>>
>> >>>>>  -- output of sessionInfo():
>> >>>>>
>> >>>>> none
>> >>>>>
>> >>>>> --
>> >>>>> Sent via the guest posting facility at bioconductor.org.
>> >>>>>
>> >>>>> _______________________________________________
>> >>>>> 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
>> >>>>>
>> >>>>
>> >>>>
>> >>>>
>> >>>> --
>> >>>> *A model is a lie that helps you see the truth.*
>> >>>> *
>> >>>> *
>> >>>> Howard Skipper<
>> http://cancerres.aacrjournals.org/content/31/9/1173.full.pdf>
>> >>>>
>> >>>
>> >>>
>> >>
>> >>
>> >> --
>> >> *A model is a lie that helps you see the truth.*
>> >> *
>> >> *
>> >> Howard Skipper<
>> http://cancerres.aacrjournals.org/content/31/9/1173.full.pdf>
>> >>
>> >
>> >
>> >
>> > --
>> > *A model is a lie that helps you see the truth.*
>> > *
>> > *
>> > Howard Skipper<
>> http://cancerres.aacrjournals.org/content/31/9/1173.full.pdf>
>> >
>>
>>         [[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
>>
>
>


-- 
*A model is a lie that helps you see the truth.*
*
*
Howard Skipper<http://cancerres.aacrjournals.org/content/31/9/1173.full.pdf>

	[[alternative HTML version deleted]]

