Dear all,
I am looking for free-to-download two color cDNA data that contain
gene
replicates in the same slide (not repeated experiments). Could anyone
please tell me where to find this type? I have checked Stanford
Microarray
database but I don't think that the data I have found are what I am
looking
for.
Thanks,
Makis
----------------------
E Motakis, Mathematics
E.Motakis at bristol.ac.uk
Hi Makis,
Have you looked at the Gene Expression Omnibus web site (GEO).
Here is some microarrays with reporters spotted in quadruplicate:
http://www.ncbi.nlm.nih.gov/projects/geo/query/acc.cgi?acc=GPL1309
Hope it helps,
Nolwenn
**************************************
Nolwenn Le Meur, PhD
Fred Hutchinson Cancer Research Center
Computational Biology
1100 Fairview Ave. N., M2-B876
P.O. Box 19024
Seattle, WA 98109-1024
On Tue, 14 Feb 2006, E Motakis, Mathematics wrote:
> Dear all,
>
> I am looking for free-to-download two color cDNA data that contain
gene
> replicates in the same slide (not repeated experiments). Could
anyone
> please tell me where to find this type? I have checked Stanford
Microarray
> database but I don't think that the data I have found are what I am
looking
> for.
>
> Thanks,
> Makis
>
> ----------------------
> E Motakis, Mathematics
> E.Motakis at bristol.ac.uk
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/bioconductor
>
On a similar subject, does anyone know of any studies done on
Affymetrix
platform using technical replicates ?
Specifically I am looking for datasets used for real biological
application rather those done for methodological purposes (i.e.
calibration, reproducibility, spike in studies).
Thank you.
Regards, Adai
On Tue, 2006-02-14 at 10:25 -0800, Nolwenn LeMeur wrote:
> Hi Makis,
> Have you looked at the Gene Expression Omnibus web site (GEO).
> Here is some microarrays with reporters spotted in quadruplicate:
> http://www.ncbi.nlm.nih.gov/projects/geo/query/acc.cgi?acc=GPL1309
>
> Hope it helps,
> Nolwenn
>
> **************************************
> Nolwenn Le Meur, PhD
> Fred Hutchinson Cancer Research Center
> Computational Biology
> 1100 Fairview Ave. N., M2-B876
> P.O. Box 19024
> Seattle, WA 98109-1024
>
> On Tue, 14 Feb 2006, E Motakis, Mathematics wrote:
>
> > Dear all,
> >
> > I am looking for free-to-download two color cDNA data that contain
gene
> > replicates in the same slide (not repeated experiments). Could
anyone
> > please tell me where to find this type? I have checked Stanford
Microarray
> > database but I don't think that the data I have found are what I
am looking
> > for.
> >
> > Thanks,
> > Makis
> >
> > ----------------------
> > E Motakis, Mathematics
> > E.Motakis at bristol.ac.uk
> >
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor at stat.math.ethz.ch
> > https://stat.ethz.ch/mailman/listinfo/bioconductor
> >
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/bioconductor
>
Dear all,
I think that D.R. Godstein has tried to answer Sylvia's question in
http://ludwig-sun2.unil.ch/~darlene/ms/prRMA.pdf
Ron
>>> <larry.lapointe at="" csiro.au=""> 02/15/06 11:55 AM >>>
Dear Martin,
We have run up to 550 chips achieving a reasonable processing time --
not more than an hour or so. The practical limits seem to be more
related to machine RAM and R memory management. RMA normalization of
550 chips occupies about 12 GB or so on our quad processor Opteron-
based
system.
Larry
Lawrence LaPointe
CSIRO Bioinformatics for Human Health
Sydney, Australia
-----Original Message-----
From: bioconductor-bounces at stat.math.ethz.ch on behalf of
martin.schumacher at novartis.com
Sent: Wed 2/15/2006 7:43 PM
To: bioconductor at stat.math.ethz.ch
Cc:
Subject: Re: [BioC] RMA normalization when using subsets of
samples
Dear Colleagues,
Greetings from Switzerland !
I agree with the statements of Wolfgang and Adai. Using all chips will
certainly put you on the safe side.
I wonder what you feel would be the minimal number of chips for a
"stable"
(meaning that a larger set would give essentially the same results)
RMA
processing? People from GeneLogic told me that about 20 chips are
sufficient.
Is it possible to run RMA using Bioconductor with 200 chips and get
the
results back within a reasonable time?
Best regards,
Martin
Adaikalavan Ramasamy <ramasamy at="" cancer.org.uk="">
Sent by: bioconductor-bounces at stat.math.ethz.ch
15.02.2006 01:01
Please respond to ramasamy
To: Wolfgang Huber <huber at="" ebi.ac.uk="">
cc: Sylvia.Merk at ukmuenster.de,
bioconductor at stat.math.ethz.ch, (bcc: Martin
Schumacher/PH/Novartis)
Subject: Re: [BioC] RMA normalization when using
subsets
of samples
Category:
This would be a problem if one or more of the resulting subsets is
small
and contains outliers.
My preference is to preprocess all arrays together. My reasoning is
that
doing this will give RMA median polish (and to a lesser extent with
the
quantile normalisation) steps much more information to work with.
Regards, Adai
On Wed, 2006-02-15 at 17:16 +0000, Wolfgang Huber wrote:
> Dear Sylvia,
>
> this might not be the answer that you want to hear, but for the end
> result it should not matter (substantially) which of the two
> possibilities you take, and I would be worried if it did.
>
> Best wishes
> Wolfgang
>
> -------------------------------------
> Wolfgang Huber
> European Bioinformatics Institute
> European Molecular Biology Laboratory
> Cambridge CB10 1SD
> England
> Phone: +44 1223 494642
> Fax: +44 1223 494486
> Http: www.ebi.ac.uk/huber
> -------------------------------------
>
> Sylvia.Merk at ukmuenster.de wrote:
> > Dear bioconductor list,
> >
> > I have a question concerning RMA-normalization:
> >
> > There are for example 200 CEL-Files and the clinicians have
several
> > research questions - each concernig only a subset of the 200
samples
> > including the possibility that some samples are included in more
than
> > one question.
> >
> > There are two possibilities to normalize the CEL-Files:
> >
> > 1.: Read all 200 chips in an affybatch-object and normalize all
200
> > chips together and further analyze the required subset.
> >
> > 2.: Read only the required chips in an affybatch-object, normalize
these
> > chips and then perform further analysis
> > I think that this approach is the better one but it has the
disadvantage
> > that some samples are included in several normalizations ending in
> > different gene expression levels for a single sample.
> >
> > What is (from a statisticians view) the appropriate approach to
> > normalize CEL-Files in this case?
> >
> > Thank you in advance
> > Sylvia
> >
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/bioconductor
>
_______________________________________________
Bioconductor mailing list
Bioconductor at stat.math.ethz.ch
https://stat.ethz.ch/mailman/listinfo/bioconductor
[[alternative HTML version deleted]]
_______________________________________________
Bioconductor mailing list
Bioconductor at stat.math.ethz.ch
https://stat.ethz.ch/mailman/listinfo/bioconductor
_______________________________________________
Bioconductor mailing list
Bioconductor at stat.math.ethz.ch
https://stat.ethz.ch/mailman/listinfo/bioconductor
Dear Sylvia,
this might not be the answer that you want to hear, but for the end
result it should not matter (substantially) which of the two
possibilities you take, and I would be worried if it did.
Best wishes
Wolfgang
-------------------------------------
Wolfgang Huber
European Bioinformatics Institute
European Molecular Biology Laboratory
Cambridge CB10 1SD
England
Phone: +44 1223 494642
Fax: +44 1223 494486
Http: www.ebi.ac.uk/huber
-------------------------------------
Sylvia.Merk at ukmuenster.de wrote:
> Dear bioconductor list,
>
> I have a question concerning RMA-normalization:
>
> There are for example 200 CEL-Files and the clinicians have several
> research questions - each concernig only a subset of the 200 samples
> including the possibility that some samples are included in more
than
> one question.
>
> There are two possibilities to normalize the CEL-Files:
>
> 1.: Read all 200 chips in an affybatch-object and normalize all 200
> chips together and further analyze the required subset.
>
> 2.: Read only the required chips in an affybatch-object, normalize
these
> chips and then perform further analysis
> I think that this approach is the better one but it has the
disadvantage
> that some samples are included in several normalizations ending in
> different gene expression levels for a single sample.
>
> What is (from a statisticians view) the appropriate approach to
> normalize CEL-Files in this case?
>
> Thank you in advance
> Sylvia
>
This would be a problem if one or more of the resulting subsets is
small
and contains outliers.
My preference is to preprocess all arrays together. My reasoning is
that
doing this will give RMA median polish (and to a lesser extent with
the
quantile normalisation) steps much more information to work with.
Regards, Adai
On Wed, 2006-02-15 at 17:16 +0000, Wolfgang Huber wrote:
> Dear Sylvia,
>
> this might not be the answer that you want to hear, but for the end
> result it should not matter (substantially) which of the two
> possibilities you take, and I would be worried if it did.
>
> Best wishes
> Wolfgang
>
> -------------------------------------
> Wolfgang Huber
> European Bioinformatics Institute
> European Molecular Biology Laboratory
> Cambridge CB10 1SD
> England
> Phone: +44 1223 494642
> Fax: +44 1223 494486
> Http: www.ebi.ac.uk/huber
> -------------------------------------
>
> Sylvia.Merk at ukmuenster.de wrote:
> > Dear bioconductor list,
> >
> > I have a question concerning RMA-normalization:
> >
> > There are for example 200 CEL-Files and the clinicians have
several
> > research questions - each concernig only a subset of the 200
samples
> > including the possibility that some samples are included in more
than
> > one question.
> >
> > There are two possibilities to normalize the CEL-Files:
> >
> > 1.: Read all 200 chips in an affybatch-object and normalize all
200
> > chips together and further analyze the required subset.
> >
> > 2.: Read only the required chips in an affybatch-object, normalize
these
> > chips and then perform further analysis
> > I think that this approach is the better one but it has the
disadvantage
> > that some samples are included in several normalizations ending in
> > different gene expression levels for a single sample.
> >
> > What is (from a statisticians view) the appropriate approach to
> > normalize CEL-Files in this case?
> >
> > Thank you in advance
> > Sylvia
> >
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/bioconductor
>
I only wish that Wolfgang's answer matched my experience. It does
seem to matter.
I don't think there is a statistical answer to your question, but as
a statistician, I do feel more comfortable preprocessing all together.
--Naomi
At 07:01 PM 2/14/2006, Adaikalavan Ramasamy wrote:
>This would be a problem if one or more of the resulting subsets is
small
>and contains outliers.
>
>My preference is to preprocess all arrays together. My reasoning is
that
>doing this will give RMA median polish (and to a lesser extent with
the
>quantile normalisation) steps much more information to work with.
>
>Regards, Adai
>
>
>
>
>On Wed, 2006-02-15 at 17:16 +0000, Wolfgang Huber wrote:
> > Dear Sylvia,
> >
> > this might not be the answer that you want to hear, but for the
end
> > result it should not matter (substantially) which of the two
> > possibilities you take, and I would be worried if it did.
> >
> > Best wishes
> > Wolfgang
> >
> > -------------------------------------
> > Wolfgang Huber
> > European Bioinformatics Institute
> > European Molecular Biology Laboratory
> > Cambridge CB10 1SD
> > England
> > Phone: +44 1223 494642
> > Fax: +44 1223 494486
> > Http: www.ebi.ac.uk/huber
> > -------------------------------------
> >
> > Sylvia.Merk at ukmuenster.de wrote:
> > > Dear bioconductor list,
> > >
> > > I have a question concerning RMA-normalization:
> > >
> > > There are for example 200 CEL-Files and the clinicians have
several
> > > research questions - each concernig only a subset of the 200
samples
> > > including the possibility that some samples are included in more
than
> > > one question.
> > >
> > > There are two possibilities to normalize the CEL-Files:
> > >
> > > 1.: Read all 200 chips in an affybatch-object and normalize all
200
> > > chips together and further analyze the required subset.
> > >
> > > 2.: Read only the required chips in an affybatch-object,
normalize these
> > > chips and then perform further analysis
> > > I think that this approach is the better one but it has the
disadvantage
> > > that some samples are included in several normalizations ending
in
> > > different gene expression levels for a single sample.
> > >
> > > What is (from a statisticians view) the appropriate approach to
> > > normalize CEL-Files in this case?
> > >
> > > Thank you in advance
> > > Sylvia
> > >
> >
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor at stat.math.ethz.ch
> > https://stat.ethz.ch/mailman/listinfo/bioconductor
> >
>
>_______________________________________________
>Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch
>https://stat.ethz.ch/mailman/listinfo/bioconductor
Naomi S. Altman 814-865-3791 (voice)
Associate Professor
Dept. of Statistics 814-863-7114 (fax)
Penn State University 814-865-1348
(Statistics)
University Park, PA 16802-2111
Dear Martin,
We have run up to 550 chips achieving a reasonable processing time --
not more than an hour or so. The practical limits seem to be more
related to machine RAM and R memory management. RMA normalization of
550 chips occupies about 12 GB or so on our quad processor Opteron-
based system.
Larry
Lawrence LaPointe
CSIRO Bioinformatics for Human Health
Sydney, Australia
-----Original Message-----
From: bioconductor-bounces at stat.math.ethz.ch on behalf of
martin.schumacher at novartis.com
Sent: Wed 2/15/2006 7:43 PM
To: bioconductor at stat.math.ethz.ch
Cc:
Subject: Re: [BioC] RMA normalization when using subsets of
samples
Dear Colleagues,
Greetings from Switzerland !
I agree with the statements of Wolfgang and Adai. Using all chips will
certainly put you on the safe side.
I wonder what you feel would be the minimal number of chips for a
"stable"
(meaning that a larger set would give essentially the same results)
RMA
processing? People from GeneLogic told me that about 20 chips are
sufficient.
Is it possible to run RMA using Bioconductor with 200 chips and get
the
results back within a reasonable time?
Best regards,
Martin
Adaikalavan Ramasamy <ramasamy at="" cancer.org.uk="">
Sent by: bioconductor-bounces at stat.math.ethz.ch
15.02.2006 01:01
Please respond to ramasamy
To: Wolfgang Huber <huber at="" ebi.ac.uk="">
cc: Sylvia.Merk at ukmuenster.de, bioconductor at
stat.math.ethz.ch, (bcc: Martin
Schumacher/PH/Novartis)
Subject: Re: [BioC] RMA normalization when using
subsets of samples
Category:
This would be a problem if one or more of the resulting subsets is
small
and contains outliers.
My preference is to preprocess all arrays together. My reasoning is
that
doing this will give RMA median polish (and to a lesser extent with
the
quantile normalisation) steps much more information to work with.
Regards, Adai
On Wed, 2006-02-15 at 17:16 +0000, Wolfgang Huber wrote:
> Dear Sylvia,
>
> this might not be the answer that you want to hear, but for the end
> result it should not matter (substantially) which of the two
> possibilities you take, and I would be worried if it did.
>
> Best wishes
> Wolfgang
>
> -------------------------------------
> Wolfgang Huber
> European Bioinformatics Institute
> European Molecular Biology Laboratory
> Cambridge CB10 1SD
> England
> Phone: +44 1223 494642
> Fax: +44 1223 494486
> Http: www.ebi.ac.uk/huber
> -------------------------------------
>
> Sylvia.Merk at ukmuenster.de wrote:
> > Dear bioconductor list,
> >
> > I have a question concerning RMA-normalization:
> >
> > There are for example 200 CEL-Files and the clinicians have
several
> > research questions - each concernig only a subset of the 200
samples
> > including the possibility that some samples are included in more
than
> > one question.
> >
> > There are two possibilities to normalize the CEL-Files:
> >
> > 1.: Read all 200 chips in an affybatch-object and normalize all
200
> > chips together and further analyze the required subset.
> >
> > 2.: Read only the required chips in an affybatch-object, normalize
these
> > chips and then perform further analysis
> > I think that this approach is the better one but it has the
disadvantage
> > that some samples are included in several normalizations ending in
> > different gene expression levels for a single sample.
> >
> > What is (from a statisticians view) the appropriate approach to
> > normalize CEL-Files in this case?
> >
> > Thank you in advance
> > Sylvia
> >
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/bioconductor
>
_______________________________________________
Bioconductor mailing list
Bioconductor at stat.math.ethz.ch
https://stat.ethz.ch/mailman/listinfo/bioconductor
[[alternative HTML version deleted]]
_______________________________________________
Bioconductor mailing list
Bioconductor at stat.math.ethz.ch
https://stat.ethz.ch/mailman/listinfo/bioconductor
