[BioC] two questions regarding Human Gene 1.0 ST arrays

[Javier Pérez Florido]

Dear Christian,
Thanks for your reply, but, is it right to assert that, for Gene arrays, 
probesets target a particular exon of a particular gene and transcript 
cluster enables gene-level estimates as Exon arrays, but using less 
probes per exon?

Thanks,
Javier


On 25/04/2011 21:13, cstrato wrote:
> While Exon ST arrays have usually 4 probes per probeset, Gene ST 
> arrays have only 1-2 probes per probeset. Thus my personal opinion is 
> not to use Gene ST arrays to detect alternative splicing events.
>
> However, there exists e.g. FIRMAGene for this purpose, see:
> http://bioinf.wehi.edu.au/folders/firmagene/
>
> Best regards
> Christian
>
>
> On 4/25/11 8:54 PM, Javier Pérez Florido wrote:
>> Sorry, I always forget sessionInfo(), see below
>>
>> You are right, for Human Gene ST arrays and at transcript level, only
>> "core" mode exists. However, when:
>> fit<-fitPLM(OligoRaw)
>> where OligoRaw is the set of Raw data, the size of "fit" object is
>> 257,430 and when the following command is executed
>>
>> OligoEset<-rma(OligoRaw,target="probeset")
>>
>> OligoEset has 257,430 features. So, the RMA procedure "inside" fitPLM
>> function performs a normalization at the probeset level.
>>
>> On the other hand, summarization using RMA can be performed at the
>> transcript level in the following way:
>> OligoEset<-rma(OligoRaw,target="core")
>>
>> which yields around 33000 transcripts.
>>
>> I'm still confused about the concepts of "probeset" and "transcript" on
>> Human Gene Arrays.
>>
>> For Exon arrays, probesets consists of four individual probes and
>> usually target a particular exon of a particular gene. Thus exon-level
>> intensity estimates correspond to the probeset-level estimates.
>> Probesets are further grouped into transcript clusters enabling
>> gene-level estimate to be computed by summarizing data from all probes
>> within the transcript cluster.
>>
>> However, I don't know if I can assert that, for Gene arrays, probesets
>> target a particular exon of a particular gene and transcript cluster
>> enables gene-level estimates as Exon arrays. The only difference is
>> that, for Exon arrays, we have two more "annotation levels" with less
>> confidence score (extended and full). Otherwise, what is the utility of
>> summarizing at the probeset level on Hu Gene arrays?
>>
>> This is related to my second question: can HuGene could detect
>> alternative splice events reliably? Can HuGene be used as an economical
>> exon array for just the well-annotated content (core)?
>>
>> Thanks again,
>> Javier
>>
>>
>> Thanks,
>> Javier
>>
>>
>> R version 2.13.0 (2011-04-13)
>> Platform: x86_64-pc-mingw32/x64 (64-bit)
>>
>> locale:
>> [1] LC_COLLATE=Spanish_Spain.1252 LC_CTYPE=Spanish_Spain.1252
>> LC_MONETARY=Spanish_Spain.1252 LC_NUMERIC=C
>> [5] LC_TIME=Spanish_Spain.1252
>>
>> attached base packages:
>> [1] stats graphics grDevices utils datasets methods base
>>
>> other attached packages:
>> [1] pd.hugene.1.0.st.v1_3.0.2 hugene10sttranscriptcluster.db_7.0.1
>> org.Hs.eg.db_2.5.0 RSQLite_0.9-4
>> [5] DBI_0.2-5 AnnotationDbi_1.14.1 oligo_1.16.0 oligoClasses_1.14.0
>> [9] affyPLM_1.28.5 preprocessCore_1.14.0 gcrma_2.24.1 affy_1.30.0
>> [13] Biobase_2.12.1
>>
>> loaded via a namespace (and not attached):
>> [1] affxparser_1.24.0 affyio_1.20.0 Biostrings_2.20.0 bit_1.1-6 ff_2.2-1
>> IRanges_1.10.0 splines_2.13.0 tools_2.13.0
>>
>>
>>
>>
>> On 25/04/2011 19:36, cstrato wrote:
>>> Dear Javier,
>>>
>>> Since you do not supply your sessionInfo() it is not possible to
>>> answer your question.
>>>
>>> However, please note that levels core, extended, full do only exist
>>> for Exon ST arrays but not for Gene ST arrays.
>>>
>>> Best regards
>>> Christian
>>> _._._._._._._._._._._._._._._._._._
>>> C.h.r.i.s.t.i.a.n S.t.r.a.t.o.w.a
>>> V.i.e.n.n.a A.u.s.t.r.i.a
>>> e.m.a.i.l: cstrato at aon.at
>>> _._._._._._._._._._._._._._._._._._
>>>
>>>
>>> On 4/25/11 7:24 PM, Javier Pérez Florido wrote:
>>>> Dear list,
>>>> I have two questions regarding Human Gene 1.0 ST arrays:
>>>>
>>>> * Both NUSE and RLE plots need a fitted object using fitPLM
>>>> function. Now, this function accepts raw data from a set of Hu
>>>> Gene 1.0 arrays, but, internally, this function performs a RMA
>>>> normalization. What level is used for this normalization? I cannot
>>>> choose the level (i.e. core, full, extended) for the "internal"
>>>> normalization.
>>>> * Are a splicing analysis using Hu Gene 1.0 arrays (core analysis)
>>>> and a splicing analysis using Hu Exon 1.0 arrays (core analysis)
>>>> equivalent in terms of results?
>>>>
>>>>
>>>> Thanks,
>>>> Javier
>>>>
>>>>
>>>> [[alternative HTML version deleted]]
>>>>
>>>> _______________________________________________
>>>> Bioconductor mailing list
>>>> Bioconductor at r-project.org
>>>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>>>> Search the archives:
>>>> http://news.gmane.org/gmane.science.biology.informatics.conductor
>>>>
>>>
>>
>>
>