Dear Gentleman,

i'm not confuded about my datasets and I have read all the documentation and
all the "vignette" about factDesign, but i have found nothing at all.

In the factDesign vignette there is only one example and the explanation
about the dataset called "estrogen" contains gene expression levels for 500
genes from Aymetrix HGU95av2 chips for eight samples from a breast cancer
cell line.

In my experiment i have 8 samples in more or less the same 2 conditions: ES
(ST and PUFA) and TYPE (WK and SHR) so i think (but you can correct me)
factDesign is the right package to perform an analysis from a factorial
designed microarray experiment.

WK = normal rats
SHR means for rats appeared frankly hypertensive at the beginning of the
study
PUFA = n-3 polyunsaturated fatty acids (PUFAs)
ST= starndard rat with no dietary treatment

So in total 8 samples... i know the arrays are a very small number but i'm
not the experiment designer! I have only to analyze this dataset if it is
possible from a statistical point of view.

These are my dataset after normalizing with RMA all the samples.
>> pData(data.rma)
>
>>   ES              TYPE
>> SHR-PUFA5.CEL     PUFA          SHR
>> SHR-PUFA6.CEL     PUFA          SHR
>> SHR-st7.CEL           ST               SHR
>> SHR-st8.CEL           ST               SHR
>> WK-PUFA3.CEL      PUFA           WK
>> WK-PUFA4.CEL      PUFA           WK
>> WK-st1.CEL            ST                WK
>> WK-st2.CEL            ST                WK


So my question is if i have to filter these samples toghether (you can see
data.rma above and then perform IQR ) or for example rma for all the samples
together and then filter by IQR WK-st VS WK-PUFA and  SHR-PUFA VS SHR-st
separately. In the second step i can add what i obtain from the first group
with the second in order to obtain only one list of genes.

So i have read the results of the analysis of the full data set (12,625
probes, 32 samples) like are discussed in Scholtens, et al. Analyzing
Factorial Designed Microarray Experiments. Journal of Multivari-ate Analysis
where the expression estimates were calculated using the rma method after
quantile normalization from the affy package, but the paper doesn't explain
how the technician has obtained the 500 genes.
The microarray expert has obtained the "estrogen" dataset (500 genes, 8
samples) from 12,625 probes, 32 samples filtering all the samples togheter
or adding many different dataset (by the function "combine" or something
else) from different sub-groups?

If i know the right procedure perhaps i can analyze my dataset in the right
way.

That's  all.

I hope to be clear now, and sorry for the inconvenience.


2009/6/10 Robert Gentleman <rgentlem@fhcrc.org>

> Hi Alberto,
>
>
> Alberto Goldoni wrote:
> > Hello to everybody
> >
> > i'm writing this email because i need some explanation about the
> "estrogen"
> > dataset analyzed in the "factDesign" package.
> > I have to perform the same analysis on 8 sample (affychip):
> >
> >> pData(data.rma)
> >
> >>   ES              TYPE
> >> SHR-PUFA5.CEL     PUFA          SHR
> >> SHR-PUFA6.CEL     PUFA          SHR
> >> SHR-st7.CEL           ST               SHR
> >> SHR-st8.CEL           ST               SHR
> >> WK-PUFA3.CEL      PUFA           WK
> >> WK-PUFA4.CEL      PUFA           WK
> >> WK-st1.CEL            ST                WK
> >> WK-st2.CEL            ST                WK
> >>
> >
> >
> >> data.rma
> > ExpressionSet (storageMode: lockedEnvironment)
> > assayData: 31099 features, 8 samples
> >   element names: exprs
> > phenoData
> >   sampleNames: SHR-PUFA5.CEL, SHR-PUFA6.CEL, ..., WK-st2.CEL  (8 total)
> >   varLabels and varMetadata description:
> >     sample: arbitrary numbering
> > featureData
> >   featureNames: 1367452_at, 1367453_at, ..., AFFX-TrpnX-M_at  (31099
> total)
> >   fvarLabels and fvarMetadata description: none
> > experimentData: use 'experimentData(object)'
> > Annotation: rat2302
> >
> >
> > What i need to know is if i have to analyze all toghether: nomalization
> with
> > rma, filtering with IQR and then i can perform factDesign technique or i
> > have to threat the two group (1:4) and (5:8) separately and then to
> rebuild
> > and exprset at the end.
>
>  You *must* jointly normalize, and that is what we did.
> There is no such thing as an exprset anymore (they were deprecated a long
> time ago).
>
> >
> > So my curiosity is to understand how the "estrogen" dataset has been
> > analyzed in order to obtain the 500 genes listed in pData(estrogen).
>
>  You seem very confused. pData accesses the phenotypic data. I have no idea
> where you are getting 500 genes from? Perhaps you have a script or
> something?
> Perhaps you are reading the vignette? If the vignette then you have access
> to
> all the code and can easily answer these questions.
>  I think you will need to be more explicit about where you are getting 500
> genes from (but I don't see how it has anything to do with
> pData(estrogen).)
>
>  best wishes
>   Robert
>
> >
> > that all
> > best regards
> >
> >
>
> --
> Robert Gentleman, PhD
> Program in Computational Biology
> Division of Public Health Sciences
> Fred Hutchinson Cancer Research Center
> 1100 Fairview Ave. N, M1-B514
> PO Box 19024
> Seattle, Washington 98109-1024
> 206-667-7700
> rgentlem@fhcrc.org
>



-- 
-----------------------------------------------------
Dr. Alberto Goldoni
Bologna, Italy
-----------------------------------------------------

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