[BioC] GEO and Limma

[Sean Davis]

On Tue, Oct 4, 2011 at 2:04 PM, Ovokeraye Achinike-Oduaran
<ovokeraye at gmail.com> wrote:
> Thanks a bunch, Johannes and Sean. I'll try the GSE data again as soon
> as I  get in front of a comp. About the first question on creating a
> model matrix  for a 3x2 factorial dataset like gds3715, any ideas?

You'll probably need a contrast matrix in there somewhere to pull out
contrasts of interest.  I'm not sure which you are interested in.

Sean


> Thanks again.
>
> Avoks
>
> On 10/4/11, Sean Davis <sdavis2 at mail.nih.gov> wrote:
>> On Tue, Oct 4, 2011 at 10:28 AM, Ovokeraye Achinike-Oduaran
>> <ovokeraye at gmail.com> wrote:
>>> Hi all,
>>>
>>> I've been struggling a bit with GEO and limma. I've come up with 2
>>> questions that I'ld appreciate some input on.
>>>
>>> Question 1:
>>> I have a design matrix with 6columns for an expression set with 3
>>> columns(3 levels, 2 agents). How do I create a model matrix to fit my
>>> design matrix for this dataset (eg. gds3715 below)? For a straight
>>> forward single factor analysis eg. gds161, the code below seems to
>>> work fine.
>>>
>>> gds161dat = getGEO('GDS161',destdir=".")
>>> gds161eset = GDS2eSet(gds161dat, do.log2=TRUE)
>>> m = pData(gds161eset)$metabolism
>>> design_gds161 = createDesignMatrix(gds161eset)
>>> design_gds161 = model.matrix(~m)
>>> fit = lmFit(gds161eset, design_gds161)
>>> fit2 = eBayes(fit)
>>> results = topTable(fit2, adjust ="BH", number = nrow(gds161eset))
>>> excel = write.table(results, file = file.choose(), sep = ",")
>>>
>>> gds3715dat = getGEO('GDS3715',destdir=".")
>>> gds3715eset = GDS2eSet(gds3715dat, do.log2=TRUE)
>>> DIR = paste(pData(gds3715eset)$disease.state,
>>> pData(gds3715eset)$agent, sep =".")
>>> m = data.frame("fac1" = pData(gds3715eset)$disease.state, "fac2" =
>>> pData(gds3715eset)$agent)
>>> design_gds3715 = createDesignMatrix(gds3715eset)
>>> design_gds3715 = model.matrix(~m)
>>> fit = lmFit(gds3715eset, design_gds3715)
>>> fit2 = eBayes(fit)
>>> results = topTable(fit2, adjust ="BH", number = nrow(gds3715eset))
>>> excel = write.table(results, file = file.choose(), sep = ",")
>>>
>>> Question 2:
>>> How can I run a similar (limma) analysis with GSE files?
>>> I can hardly figure out how to get from the expression set to the
>>> analysis...I've read the Using the GEOquery Package documentation
>>> several times over. I'm just have a hard time getting it to work.
>>>
>>> gse25724dat = getGEO('GSE25724', GSEMatrix = TRUE)
>>
>> Hi, Avoks.  The return value from this call is a LIST of
>> ExpressionSets, not a single ExpressionSet.  You probably want the
>> first ExpressionSet from the list and can get that:
>>
>> gse25724eSet = gse25724dat[[1]]
>>
>> GEOquery works this way because many GSEs (so-called superseries) have
>> multiple series included within them, so returning a list is necessary
>> to be general.
>>
>> Sean
>>
>>
>>> This is supposed to give me an expressionset, I can't seem to figure
>>> out how to analyze it with limma.
>>>
>>> Please help.
>>>
>>> Thanks.
>>>
>>> Avoks
>>>
>>> sessionInfo()
>>> R version 2.13.1 (2011-07-08)
>>> Platform: i386-pc-mingw32/i386 (32-bit)
>>>
>>> locale:
>>> [1] LC_COLLATE=English_xxx. LC_CTYPE=English_xxx
>>> [3] LC_MONETARY=English_xxx LC_NUMERIC=C
>>> [5] LC_TIME=English_xxx
>>>
>>> attached base packages:
>>> [1] stats     graphics  grDevices utils     datasets  methods   base
>>>
>>> other attached packages:
>>> [1] puma_2.4.0      mclust_3.4.10   affy_1.30.0     limma_3.8.3
>>> [5] GEOquery_2.19.4 Biobase_2.12.2
>>>
>>> loaded via a namespace (and not attached):
>>> [1] affyio_1.20.0         preprocessCore_1.14.0 RCurl_1.6-10.1
>>> [4] tools_2.13.1          XML_3.4-2.2
>>>
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>>>
>>
>
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