Malick,
A few months back, Chris Harbron posted a reply to a related question
in which the RefPlus package was suggested for this purpose.
Best,
Duane
_________________________________________________
Duane Hassane, Ph.D.
Center for Pediatric Biomedical Research
University of Rochester Medical Center
Rochester, New York 14642
-----Original Message-----
From: bioconductor-bounces@stat.math.ethz.ch
[mailto:bioconductor-bounces at stat.math.ethz.ch]On Behalf Of
Malick.PAYE at eu.biomerieux.com
Sent: Monday, March 05, 2007 1:47 PM
To: bioconductor at stat.math.ethz.ch
Subject: [BioC] rma on new samples
hello,
I work for an invitro diagnostic company www.biomerieux.com) and we
are
interested in classification of patients based on expression profile
(we
are working with affymetrix chips).
I built a classification model based on a training set and i have new
samples and i want to make my new samples comparable with the training
set
in order to apply my built model.
We use RMA to compute expression measures.
If someone have a code to do this, it would be very greatful for me.
Ideally i want to extract rma parameters and apply them to my new
samples,
or if someone have a better idea.
Thanks in advance.
M.P
Malick Paye | bioM?rieux | Biomathematician
Phone: (+33)4 78 87 70 97 | Fax: (+33)4 78 87 53 40
[Centre Cristophe M?rieux, 5 Rue des Berges, 38004 Cedex 01 Grenoble,
France]
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There are a few ways to do this. The basic issue is that the RMA
normalization and summarization steps use data across multiple chips.
There is a package called refRMA that normalizes to a pre-defined
database of data generated by GeneLogic. See
http://www.biomedcentral.com/content/pdf/1471-2105-7-464.pdf
for more details.
I have approached the problem by keeping the background correction the
same and then
- normalize the PM values of new samples to a reference distribution
defined by the training set PM values
- computing a trimmed mean to get the summary measure
I'm sure that others have done something similar too.
I have code to do this (the normalization is based on some code from
limma:::normalizeQuantiles). I will send you (or anyone else) the code
in another email if you are interested.
I've looked at comparisons in performance in my approach, regular RMA
and MAS5 and found that the two RMA methods are pretty similar and
MAS5 did very poorly. This could have been due to my particular
problem and data (I was using the Staph chip), so take that for what
it's worth.
Max
-----Original Message-----
From: bioconductor-bounces@stat.math.ethz.ch [mailto:bioconductor-
bounces@stat.math.ethz.ch] On Behalf Of Malick.PAYE@eu.biomerieux.com
Sent: Monday, March 05, 2007 1:47 PM
To: bioconductor at stat.math.ethz.ch
Subject: [BioC] rma on new samples
hello,
I work for an invitro diagnostic company www.biomerieux.com) and we
are
interested in classification of patients based on expression profile
(we
are working with affymetrix chips).
I built a classification model based on a training set and i have new
samples and i want to make my new samples comparable with the training
set
in order to apply my built model.
We use RMA to compute expression measures.
If someone have a code to do this, it would be very greatful for me.
Ideally i want to extract rma parameters and apply them to my new
samples,
or if someone have a better idea.
Thanks in advance.
M.P
Malick Paye | bioM?rieux | Biomathematician
Phone: (+33)4 78 87 70 97 | Fax: (+33)4 78 87 53 40
[Centre Cristophe M?rieux, 5 Rue des Berges, 38004 Cedex 01 Grenoble,
France]
----------------------------------------------------------------------
-------
AVIS : Ce courrier et ses pieces jointes sont destines a leur seul
destinataire et peuvent contenir des informations confidentielles
appartenant a bioMerieux. Si vous n'etes pas destinataire, vous etes
informe que toute lecture, divulgation, ou reproduction de ce message
et des pieces jointes est strictement interdite. Si vous avez recu ce
message par erreur merci d'en prevenir l'expediteur et de le detruire,
ainsi que ses pieces jointes.
NOTICE: This message and attachments are intended only for the use of
their addressee and may contain confidential information belonging to
bioMerieux. If you are not the intended recipient, you are hereby
notified that any reading, dissemination, distribution, or copying of
this message, or any attachment, is strictly prohibited. If you have
received this message in error, please notify the original sender
immediately and delete this message, along with any attachments.
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