[BioC] Options for spatial normalization? (Oliver Homann)
Tarca, Adi
atarca at med.wayne.edu
Thu Mar 8 17:06:35 CET 2007
Hi Olivier,
There is also nnNorm package that does spatial normalization within
print-tips. It uses pseudo-spatial coordinates to avoid
over-normalization, and the values of a given spot are not used when
computing the amount of bias for the corresponding spot (via a
cross-validation using neural nets).
If your arrays does not show spatial artifacts nnNorm normalization will
behave similarly with print-tip loess.
Adi Laurentiu TARCA, Ph.D.
Research Associate,
NIH-Perinatology Research Branch,
Wayne State University,
3990 John R., Detroit, Michigan 48201
Tel: 1-313-5775305
Cell: 1-313-4043116
http://vortex.cs.wayne.edu/tarca/
>[1] Are there any other methods for spatial normalization of two-color
> data implemented in R?
> [2] In my attempts to develop a normalization pipeline I have been
> stymied by the need to ascertain on a slide-by-slide basis which types
> of normalization are needed (e.g. pin/intensity/spatial). Do any
> of you
> have a "rule-of-thumb", or better yet a quantitative approach to
> making
> this decision?
-----Original Message-----
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Today's Topics:
1. Re: crlmm warning (Morten Mattingsdal)
2. Re: crlmm warning (James W. MacDonald)
3. Re: crlmm warning (Benilton Carvalho)
4. Re: crlmm warning (Morten Mattingsdal)
5. Re: Unable to compile the impute package on debian with gcc
4.1 (Seth Falcon)
6. Re: rma on new samples (Hassane, Duane)
7. Re: crlmm warning (Benilton Carvalho)
8. NEwbie: How to determine significant enrichment differences
of GO term vectors? (Johannes Graumann)
9. Re: rma on new samples (Kuhn, Max)
10. Options for spatial normalization? (Oliver Homann)
11. how to enlarge memory (Yihuan Xu)
12. Re: how to enlarge memory (James W. MacDonald)
13. Re: Options for spatial normalization? (Jay Konieczka)
14. RMA, RefRMA questions (James Anderson)
15. Re: RMA, RefRMA questions (Kuhn, Max)
16. Re: RMA, RefRMA questions (James W. MacDonald)
17. Re: RMA, RefRMA questions (Kuhn, Max)
18. two questions about limma (cont.2) (De-Jian,ZHAO)
----------------------------------------------------------------------
Message: 1
Date: Wed, 07 Mar 2007 13:10:59 +0100
From: Morten Mattingsdal <mortenm at inbox.com>
Subject: Re: [BioC] crlmm warning
To: Morten Mattingsdal <mortenm at inbox.com>
Cc: BioC <bioconductor at stat.math.ethz.ch>
Message-ID: <45EEABD3.70208 at inbox.com>
Content-Type: text/plain; charset=ISO-8859-1; format=flowed
Hello again,
Ill just answer that myself, since I posted a bit too prematurely.
Seems like I need to use the makePlatformDesign package, with the code:
> library(makePlatformDesign)
>
makePDpackage("Mapping50K_Xba240.CDF","Mapping50K_Xba240_probe_fasta","M
apping50K_Xba240_annot.csv",type="SNP")
R CMD INSTALL pdmapping50kxba240
I find it ... non-trivial.. to locate the fasta file for Nsp and Sty
arrays, but I think Ill harass Affyemtrix Inc for that.
I am aware that the oligo package is in development, but It would be
nice to have some vignettes to read
regards
morten
Morten Mattingsdal wrote:
> Hello everyone,
>
> Ive managed to get the oligo package and the crlmm function up and
> running. Ive also installed the meta-data libraries from this URL
> http://www.biostat.jhsph.edu/~bcarvalh/oligoAddOns.tar.gz
> provided by Benliton
>
> but when I run the commands:
>
> >files <- list.celfiles()
> >snpFSet <- read.celfiles(files)
> >
> >Welcome to the pd.Mapping250K_Nsp prototype pdInfo package
> >WARNING: DO NOT USE THIS PACKAGE FOR ANY ANALYSIS.
> >THIS PACKAGE IS FOR INTERFACE PROTOTYPE USE ONLY!
> >THE DATA HAS NOT BEEN VALIDATED AND LIKELY HAS ERRORS.
> >Have fun!
>
> Im am happy that this information is warning me, but to my point:
> - When will the "safe" mapping libraries come?
> - Can I build this by myself ?
>
> I want to compare brlmm from Affymetrix and crlmm from BioC genotype
> calls for my data
>
> regards
> morten
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives:
http://news.gmane.org/gmane.science.biology.informatics.conductor
>
>
> .
>
>
------------------------------
Message: 2
Date: Wed, 07 Mar 2007 08:49:07 -0500
From: "James W. MacDonald" <jmacdon at med.umich.edu>
Subject: Re: [BioC] crlmm warning
To: Morten Mattingsdal <mortenm at inbox.com>
Cc: BioC <bioconductor at stat.math.ethz.ch>
Message-ID: <45EEC2D3.6000504 at med.umich.edu>
Content-Type: text/plain; charset="utf-8"; format=flowed
Morten Mattingsdal wrote:
>
> I find it ... non-trivial.. to locate the fasta file for Nsp and Sty
> arrays, but I think Ill harass Affyemtrix Inc for that.
>
These files are available on the support page for the 500K snp arrays
(near the bottom).
http://www.affymetrix.com/support/technical/byproduct.affx?product=500k
--
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623
**********************************************************
Electronic Mail is not secure, may not be read every day, and should not
be used for urgent or sensitive issues.
------------------------------
Message: 3
Date: Wed, 7 Mar 2007 09:15:27 -0500
From: Benilton Carvalho <bcarvalh at jhsph.edu>
Subject: Re: [BioC] crlmm warning
To: Morten Mattingsdal <mortenm at inbox.com>
Cc: BioC <bioconductor at stat.math.ethz.ch>
Message-ID: <EFC72C58-D847-4952-B251-C92A73860FF5 at jhsph.edu>
Content-Type: text/plain; charset=US-ASCII; delsp=yes; format=flowed
Hi Morten,
the 'safe' versions will become available with BioC 2.0.
My question, regarding the email you send next (about using
makePlatformDesign for SNP arrays), that's not required anymore. All
you need is in that oligoAddOns.tar.gz. All you need to do is install
the packages and use the latest oligo.
Let me know how things go,
b
On Mar 7, 2007, at 5:45 AM, Morten Mattingsdal wrote:
> Hello everyone,
>
> Ive managed to get the oligo package and the crlmm function up and
> running. Ive also installed the meta-data libraries from this URL
> http://www.biostat.jhsph.edu/~bcarvalh/oligoAddOns.tar.gz
> provided by Benliton
>
> but when I run the commands:
>
>> files <- list.celfiles()
>> snpFSet <- read.celfiles(files)
>>
>> Welcome to the pd.Mapping250K_Nsp prototype pdInfo package
>> WARNING: DO NOT USE THIS PACKAGE FOR ANY ANALYSIS.
>> THIS PACKAGE IS FOR INTERFACE PROTOTYPE USE ONLY!
>> THE DATA HAS NOT BEEN VALIDATED AND LIKELY HAS ERRORS.
>> Have fun!
>
> Im am happy that this information is warning me, but to my point:
> - When will the "safe" mapping libraries come?
> - Can I build this by myself ?
>
> I want to compare brlmm from Affymetrix and crlmm from BioC genotype
> calls for my data
>
> regards
> morten
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives: http://news.gmane.org/
> gmane.science.biology.informatics.conductor
------------------------------
Message: 4
Date: Wed, 07 Mar 2007 15:55:24 +0100
From: Morten Mattingsdal <mortenm at inbox.com>
Subject: Re: [BioC] crlmm warning
To: Benilton Carvalho <bcarvalh at jhsph.edu>
Cc: BioC <bioconductor at stat.math.ethz.ch>
Message-ID: <45EED25C.8020007 at inbox.com>
Content-Type: text/plain; charset=ISO-8859-1; format=flowed
Hi Benilton,
Readning the data, loading annotation libraries and rma normalizting
goes perfect!
But I encounter an error in crlmm, which will probably expose my
ignorance here, but crlmm complains about a "correctionFile is not
found".
>crlmm_NSP=crlmm(rma_NSP)
>Error in crlmm(rma_NSP) : Provide correctionFile.
>If the correctionFile is not found, it will be created and it will
contain the EM results.
The error claims, if not found it will be created. I don't think it is
created, although I have an R object called "reference"
The crlmm doc says: The 'correction' argument is a list with the
following elements:
'f0' (scalar), 'fs' (numeric vector), 'pis' (numeric matrix) and 'snr'.
I cant seem to figure out the nature of these correction elements nor
the data format of this file.
Could you be so kind and explain a bit what this means ?
regards
morten
NB Ill just paste all commands and output for your leisure
>library(oligo)
>files <- list.celfiles()
>files()
[1] "1580_Nsp1_090207.CEL" "1620_Nsp1_020307.CEL"
"1736_Nsp1_020307.CEL"
[4] "1812_Nsp1_020307.CEL" "355_Nsp1_090207.CEL"
"4379_Nsp1_020307.CEL"
[7] "4436_Nsp1_020307.CEL" "5968_Nsp1_020307.CEL" "635_Nsp1_090207.CEL"
[10] "654_Nsp1_090207.CEL" "659_Nsp1_090207.CEL" "680_Nsp1_090207.CEL"
>NSP <- read.celfiles(files)
Incompatible phenoData object. Created a new one.
Welcome to the pd.Mapping250K_Nsp prototype pdInfo package
WARNING: DO NOT USE THIS PACKAGE FOR ANY ANALYSIS.
THIS PACKAGE IS FOR INTERFACE PROTOTYPE USE ONLY!
THE DATA HAS NOT BEEN VALIDATED AND LIKELY HAS ERRORS.
Have fun!
Platform design info loaded.
>rma_NSP <- snprma(NSP)
Position -4
Position -2
Position -1
Position 0
Position 1
Position 3
Position 4
Loading required package: pd.mapping250k.nsp.crlmm.regions
Calculating Expression
>crlmm_NSP=crlmm(rma_NSP)
Error in crlmm(rma_NSP) : Provide correctionFile.
If the correctionFile is not found, it will be created and it will
contain the EM results.
> sessionInfo()
R version 2.5.0 Under development (unstable) (2007-03-04 r40813)
x86_64-unknown-linux-gnu
locale:
LC_CTYPE=no_NO;LC_NUMERIC=C;LC_TIME=C;LC_COLLATE=C;LC_MONETARY=C;LC_MESS
AGES=C;LC_PAPER=C;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=C
;LC_IDENTIFICATION=C
attached base packages:
[1] "splines" "tools" "stats" "graphics" "grDevices" "utils"
[7] "datasets" "methods" "base"
other attached packages:
pd.mapping250k.nsp.crlmm.regions pd.mapping250k.nsp
"0.1.0" "0.1.5"
geneplotter lattice
"1.13.7" "0.14-16"
annotate oligo
"1.13.6" "0.99.82"
BufferedMatrixMethods BufferedMatrix
"0.1.1" "0.1.27"
RSQLite DBI
"0.4-20" "0.1-12"
affyio Biobase
"1.3.3" "1.13.39"
Benilton Carvalho wrote:
> Hi Morten,
>
> the 'safe' versions will become available with BioC 2.0.
>
> My question, regarding the email you send next (about using
> makePlatformDesign for SNP arrays), that's not required anymore. All
> you need is in that oligoAddOns.tar.gz. All you need to do is install
> the packages and use the latest oligo.
>
> Let me know how things go,
>
> b
>
> On Mar 7, 2007, at 5:45 AM, Morten Mattingsdal wrote:
>
>> Hello everyone,
>>
>> Ive managed to get the oligo package and the crlmm function up and
>> running. Ive also installed the meta-data libraries from this URL
>> http://www.biostat.jhsph.edu/~bcarvalh/oligoAddOns.tar.gz
>> provided by Benliton
>>
>> but when I run the commands:
>>
>>> files <- list.celfiles()
>>> snpFSet <- read.celfiles(files)
>>>
>>> Welcome to the pd.Mapping250K_Nsp prototype pdInfo package
>>> WARNING: DO NOT USE THIS PACKAGE FOR ANY ANALYSIS.
>>> THIS PACKAGE IS FOR INTERFACE PROTOTYPE USE ONLY!
>>> THE DATA HAS NOT BEEN VALIDATED AND LIKELY HAS ERRORS.
>>> Have fun!
>>
>> Im am happy that this information is warning me, but to my point:
>> - When will the "safe" mapping libraries come?
>> - Can I build this by myself ?
>>
>> I want to compare brlmm from Affymetrix and crlmm from BioC genotype
>> calls for my data
>>
>> regards
>> morten
>>
>> _______________________________________________
>> Bioconductor mailing list
>> Bioconductor at stat.math.ethz.ch
>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>> Search the archives:
>> http://news.gmane.org/gmane.science.biology.informatics.conductor
>
>
> .
>
------------------------------
Message: 5
Date: Wed, 07 Mar 2007 07:14:42 -0800
From: Seth Falcon <sfalcon at fhcrc.org>
Subject: Re: [BioC] Unable to compile the impute package on debian
with gcc 4.1
To: Sebastian Bauer <Sebastian.Bauer at charite.de>
Cc: bioconductor at stat.math.ethz.ch
Message-ID: <m2wt1t2ihp.fsf at ziti.local>
Content-Type: text/plain; charset=us-ascii
Sebastian Bauer <Sebastian.Bauer at charite.de> writes:
> Hi Seth,
>
> Seth Falcon wrote:
>> We are seeing the same issues on our build systems. This is almost
>> certainly due to gfortran being more strict than g77.
>
> Yes, you're right. When compiling the file manually with g77 it runs
> through. Is there any possibility to alter R to take g77 instead the
> gfortran compiler?
Well, yes. But I suspect you would want all of R to be using same
fortran and that the way to do it is to rebuild R from source setting
a config option. The R Admin manual might have some useful details.
Before you recompile, I suppose it wouldn't hurt to edit R's Makeconf
file located in R_HOME/etc/Makeconf.
>> I have sent the impute package maintainer an email asking that he
take
>> a look.
And I've heard back that he has a patch that should be applied this
week.
+ seth
--
Seth Falcon | Computational Biology | Fred Hutchinson Cancer Research
Center
http://bioconductor.org
------------------------------
Message: 6
Date: Wed, 7 Mar 2007 10:26:18 -0500
From: "Hassane, Duane" <Duane_Hassane at URMC.Rochester.edu>
Subject: Re: [BioC] rma on new samples
To: <Malick.PAYE at eu.biomerieux.com>, <bioconductor at stat.math.ethz.ch>
Message-ID:
<F66FEDF8D4A99B499577086F6C2E4FFC0187C56A at e2k3ms3.urmc-sh.rochester.edu>
Content-Type: text/plain; charset="iso-8859-1"
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 at 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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------------------------------
Message: 7
Date: Wed, 7 Mar 2007 11:16:31 -0500
From: Benilton Carvalho <bcarvalh at jhsph.edu>
Subject: Re: [BioC] crlmm warning
To: Morten Mattingsdal <mortenm at inbox.com>
Cc: BioC <bioconductor at stat.math.ethz.ch>
Message-ID: <25D76B7F-2D68-4057-8762-15B337546A78 at jhsph.edu>
Content-Type: text/plain; charset=US-ASCII; delsp=yes; format=flowed
Hi Morten,
I'm writing a vignette to help users with oligo. My bad! Sorry for that.
All you need to do is give a file name... if the file does not exist,
it'll be created... if it exists, it'll be loaded.
For example, try the following:
crlmm_NSP=crlmm(rma_NSP, correctionFile="nspCorrection.rda")
The reason this is required (at least for now) is because the EM
algorithm may take a long time depending on the sample size. So, once
it is done, it saves the results in this correctionFile... which you
can just load later in case you want to run CRLMM again.
If for some reason you need to run CRLMM on the exact same data
(rma_NSP), by using
crlmm_NSP=crlmm(rma_NSP, correctionFile="nspCorrection.rda")
the EM step will be skipped and loaded from nspCorrection.rda instead.
b
On Mar 7, 2007, at 9:55 AM, Morten Mattingsdal wrote:
> Hi Benilton,
>
> Readning the data, loading annotation libraries and rma
> normalizting goes perfect!
> But I encounter an error in crlmm, which will probably expose my
> ignorance here, but crlmm complains about a "correctionFile is not
> found".
>
> >crlmm_NSP=crlmm(rma_NSP)
> >Error in crlmm(rma_NSP) : Provide correctionFile.
> >If the correctionFile is not found, it will be created and it will
> contain the EM results.
>
> The error claims, if not found it will be created. I don't think it
> is created, although I have an R object called "reference"
>
> The crlmm doc says: The 'correction' argument is a list with the
> following elements:
> 'f0' (scalar), 'fs' (numeric vector), 'pis' (numeric matrix) and
> 'snr'.
>
> I cant seem to figure out the nature of these correction elements
> nor the data format of this file.
> Could you be so kind and explain a bit what this means ?
>
> regards
> morten
>
>
> NB Ill just paste all commands and output for your leisure
>
> >library(oligo)
>
> >files <- list.celfiles()
>
> >files()
> [1] "1580_Nsp1_090207.CEL" "1620_Nsp1_020307.CEL"
> "1736_Nsp1_020307.CEL"
> [4] "1812_Nsp1_020307.CEL" "355_Nsp1_090207.CEL"
> "4379_Nsp1_020307.CEL"
> [7] "4436_Nsp1_020307.CEL" "5968_Nsp1_020307.CEL"
> "635_Nsp1_090207.CEL"
> [10] "654_Nsp1_090207.CEL" "659_Nsp1_090207.CEL"
> "680_Nsp1_090207.CEL"
>
> >NSP <- read.celfiles(files)
> Incompatible phenoData object. Created a new one.
>
>
> Welcome to the pd.Mapping250K_Nsp prototype pdInfo package
> WARNING: DO NOT USE THIS PACKAGE FOR ANY ANALYSIS.
> THIS PACKAGE IS FOR INTERFACE PROTOTYPE USE ONLY!
> THE DATA HAS NOT BEEN VALIDATED AND LIKELY HAS ERRORS.
> Have fun!
>
> Platform design info loaded.
>
> >rma_NSP <- snprma(NSP)
> Position -4
> Position -2
> Position -1
> Position 0
> Position 1
> Position 3
> Position 4
> Loading required package: pd.mapping250k.nsp.crlmm.regions
> Calculating Expression
>
> >crlmm_NSP=crlmm(rma_NSP)
> Error in crlmm(rma_NSP) : Provide correctionFile.
> If the correctionFile is not found, it will be created and it will
> contain the EM results.
>
>
> > sessionInfo()
> R version 2.5.0 Under development (unstable) (2007-03-04 r40813)
> x86_64-unknown-linux-gnu
>
> locale:
> LC_CTYPE=no_NO;LC_NUMERIC=C;LC_TIME=C;LC_COLLATE=C;LC_MONETARY=C;LC_ME
> SSAGES=C;LC_PAPER=C;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREME
> NT=C;LC_IDENTIFICATION=C
>
> attached base packages:
> [1] "splines" "tools" "stats" "graphics" "grDevices"
> "utils" [7] "datasets" "methods" "base"
> other attached packages:
> pd.mapping250k.nsp.crlmm.regions pd.mapping250k.nsp
> "0.1.0" "0.1.5"
> geneplotter lattice
> "1.13.7" "0.14-16"
> annotate oligo
> "1.13.6" "0.99.82"
> BufferedMatrixMethods BufferedMatrix
> "0.1.1" "0.1.27"
> RSQLite DBI
> "0.4-20" "0.1-12"
> affyio Biobase
> "1.3.3" "1.13.39"
>
> Benilton Carvalho wrote:
>> Hi Morten,
>>
>> the 'safe' versions will become available with BioC 2.0.
>>
>> My question, regarding the email you send next (about using
>> makePlatformDesign for SNP arrays), that's not required anymore.
>> All you need is in that oligoAddOns.tar.gz. All you need to do is
>> install the packages and use the latest oligo.
>>
>> Let me know how things go,
>>
>> b
>>
>> On Mar 7, 2007, at 5:45 AM, Morten Mattingsdal wrote:
>>
>>> Hello everyone,
>>>
>>> Ive managed to get the oligo package and the crlmm function up and
>>> running. Ive also installed the meta-data libraries from this URL
>>> http://www.biostat.jhsph.edu/~bcarvalh/oligoAddOns.tar.gz
>>> provided by Benliton
>>>
>>> but when I run the commands:
>>>
>>>> files <- list.celfiles()
>>>> snpFSet <- read.celfiles(files)
>>>>
>>>> Welcome to the pd.Mapping250K_Nsp prototype pdInfo package
>>>> WARNING: DO NOT USE THIS PACKAGE FOR ANY ANALYSIS.
>>>> THIS PACKAGE IS FOR INTERFACE PROTOTYPE USE ONLY!
>>>> THE DATA HAS NOT BEEN VALIDATED AND LIKELY HAS ERRORS.
>>>> Have fun!
>>>
>>> Im am happy that this information is warning me, but to my point:
>>> - When will the "safe" mapping libraries come?
>>> - Can I build this by myself ?
>>>
>>> I want to compare brlmm from Affymetrix and crlmm from BioC genotype
>>> calls for my data
>>>
>>> regards
>>> morten
>>>
>>> _______________________________________________
>>> Bioconductor mailing list
>>> Bioconductor at stat.math.ethz.ch
>>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>>> Search the archives: http://news.gmane.org/
>>> gmane.science.biology.informatics.conductor
>>
>>
>> .
>>
------------------------------
Message: 8
Date: Wed, 07 Mar 2007 17:19:28 +0100
From: Johannes Graumann <johannes_graumann at web.de>
Subject: [BioC] NEwbie: How to determine significant enrichment
differences of GO term vectors?
To: bioconductor at stat.math.ethz.ch
Message-ID: <esmomh$lfo$1 at sea.gmane.org>
Content-Type: text/plain; charset=us-ascii
Hello,
Please excuse this naive question, but I would appreciate if someone
could
point me at the right function(s) to use:
I have two vectors containing all GO terms associated with proteins
retrieved in two proteomic experiments and would like to figure out for
which categories they differ significantly from each other. I am
obviously
somewhat limited by the fact of not being able to use the 'standard'
annotation packages, but I have build my own protein -> GenBank -> GO
package using AnnBuilder.
Please let me know how you would tackle this.
Thanks for your patience,
Joh
------------------------------
Message: 9
Date: Wed, 7 Mar 2007 13:05:19 -0500
From: "Kuhn, Max" <Max.Kuhn at pfizer.com>
Subject: Re: [BioC] rma on new samples
To: <Malick.PAYE at eu.biomerieux.com>, <bioconductor at stat.math.ethz.ch>
Message-ID:
<71257D09F114DA4A8E134DEAC70F25D307B70F0C at groamrexm03.amer.pfizer.com>
Content-Type: text/plain; charset="iso-8859-1"
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 at 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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Message: 10
Date: Wed, 07 Mar 2007 10:47:03 -0800
From: "Oliver Homann" <Oliver.Homann at ucsf.edu>
Subject: [BioC] Options for spatial normalization?
To: bioconductor at stat.math.ethz.ch
Message-ID: <45EF08A7.7040604 at ucsf.edu>
Content-Type: text/plain; charset=iso-8859-1; format=flowed
Hello,
I was wondering if anyone could offer me some advice on the best
approach for normalizing my two-color expression arrays. I will be
processing a large number of arrays, and ideally I would like to develop
a semi-automated normalization pipeline. Some of my arrays have issues
with spatial effects, and currently the only method that I'm aware of
for dealing with such effects is in the Maanova package (the "rlowess"
method of transform.madata). However, this method is far from ideal for
my purposes because it utilizes grid layout rather than 'X' and 'Y'
positions to calculate proximity (which causes some problems with gaps
between blocks) and because it is coupled to a intensity-based
normalization (which limits the flexibility somewhat).
I have a few specific questions:
[1] Are there any other methods for spatial normalization of two-color
data implemented in R?
[2] In my attempts to develop a normalization pipeline I have been
stymied by the need to ascertain on a slide-by-slide basis which types
of normalization are needed (e.g. pin/intensity/spatial). Do any of you
have a "rule-of-thumb", or better yet a quantitative approach to making
this decision?
Thanks!
Oliver Homann
------------------------------
Message: 11
Date: Wed, 7 Mar 2007 13:55:39 -0500
From: "Yihuan Xu" <yihuan.xu at jefferson.edu>
Subject: [BioC] how to enlarge memory
To: <bioconductor at stat.math.ethz.ch>
Message-ID: <004501c760ea$33029710$1504a60a at XU>
Content-Type: text/plain; charset="iso-8859-1"
Hi, There,
How can I enlarge memory when I use Affy package? Thanks a lot.
Yihuan
------------------------------
Message: 12
Date: Wed, 07 Mar 2007 14:43:09 -0500
From: "James W. MacDonald" <jmacdon at med.umich.edu>
Subject: Re: [BioC] how to enlarge memory
To: Yihuan Xu <yihuan.xu at jefferson.edu>
Cc: bioconductor at stat.math.ethz.ch
Message-ID: <45EF15CD.10001 at med.umich.edu>
Content-Type: text/plain; charset="utf-8"; format=flowed
Yihuan Xu wrote:
> Hi, There,
>
> How can I enlarge memory when I use Affy package? Thanks a lot.
This question has been asked and answered many many many times on this
list, so searching first would get you the answer right away rather than
ansking and waiting.
See here:
> Search the archives:
http://news.gmane.org/gmane.science.biology.informatics.conductor
In addition, without giving us any information about your OS, version of
R, etc., this question is almost unanswerable.
See here:
http://www.bioconductor.org/docs/postingGuide.html
>
> Yihuan
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives:
http://news.gmane.org/gmane.science.biology.informatics.conductor
--
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623
**********************************************************
Electronic Mail is not secure, may not be read every day, and should not
be used for urgent or sensitive issues.
------------------------------
Message: 13
Date: Wed, 7 Mar 2007 12:45:41 -0700
From: Jay Konieczka <jayk at u.arizona.edu>
Subject: Re: [BioC] Options for spatial normalization?
To: "Oliver Homann" <Oliver.Homann at ucsf.edu>
Cc: bioconductor at stat.math.ethz.ch
Message-ID: <97D697B0-521E-460A-9FB5-FF0E46029EFE at u.arizona.edu>
Content-Type: text/plain; charset=US-ASCII; delsp=yes; format=flowed
Hi Olivier,
Take a look at the OLIN package. It takes xy coordinates and uses a
machine learning approach to approximate the smoothing parameters for
spatial and intensity normalization. I have the same issue and I've
had a great deal of success with it. I wouldn't recommend bypassing
the slide-by-slide oversight, but you may find a set of parameters
supplied to OLIN that is sufficient for the overwhelming majority of
your chips.
Cheers,
jay
On Mar 7, 2007, at 11:47 AM, Oliver Homann wrote:
> Hello,
>
> I was wondering if anyone could offer me some advice on the best
> approach for normalizing my two-color expression arrays. I will be
> processing a large number of arrays, and ideally I would like to
> develop
> a semi-automated normalization pipeline. Some of my arrays have
> issues
> with spatial effects, and currently the only method that I'm aware of
> for dealing with such effects is in the Maanova package (the "rlowess"
> method of transform.madata). However, this method is far from
> ideal for
> my purposes because it utilizes grid layout rather than 'X' and 'Y'
> positions to calculate proximity (which causes some problems with gaps
> between blocks) and because it is coupled to a intensity-based
> normalization (which limits the flexibility somewhat).
>
> I have a few specific questions:
> [1] Are there any other methods for spatial normalization of two-color
> data implemented in R?
> [2] In my attempts to develop a normalization pipeline I have been
> stymied by the need to ascertain on a slide-by-slide basis which types
> of normalization are needed (e.g. pin/intensity/spatial). Do any
> of you
> have a "rule-of-thumb", or better yet a quantitative approach to
> making
> this decision?
>
> Thanks!
> Oliver Homann
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives: http://news.gmane.org/
> gmane.science.biology.informatics.conductor
------------------------------
Message: 14
Date: Wed, 7 Mar 2007 12:09:54 -0800 (PST)
From: James Anderson <janderson_net at yahoo.com>
Subject: [BioC] RMA, RefRMA questions
To: bioconductor <bioconductor at stat.math.ethz.ch>
Message-ID: <614088.55974.qm at web43139.mail.sp1.yahoo.com>
Content-Type: text/plain
Hi,
I roughly understand the issue of RMA and RefRMA. When using RefRMA, one
RMA model is generated first based on the samples from one lab. If some
additional arrays measured in a different lab needs to be normalized,
does RefRMA automatically take care of the systematic difference between
the two labs or except RefRMA, there are still some extra work needs to
be done to correct the systematic difference between the two labs?
Thanks,
James
---------------------------------
The fish are biting.
[[alternative HTML version deleted]]
------------------------------
Message: 15
Date: Wed, 7 Mar 2007 15:26:00 -0500
From: "Kuhn, Max" <Max.Kuhn at pfizer.com>
Subject: Re: [BioC] RMA, RefRMA questions
<bioconductor at stat.math.ethz.ch>
Message-ID:
<71257D09F114DA4A8E134DEAC70F25D307B711C8 at groamrexm03.amer.pfizer.com>
Content-Type: text/plain; charset="us-ascii"
James,
>From the presentation that I saw on refRMA (and the paper), the
quantile
normalization process would coerce the distribution of the low-level
probe data to have the same shape as the "reference" data. In
GeneLogic's case, they used sets of biological samples from their large
data base to define this reference distribution. Assuming that the
reference distribution used is acceptable for your samples/problem, this
should remove many of the systematic effects that you may have in your
data.
After googling, it seems that the package is only available form the
authors at this time, so I can't be exact.
As I mentioned earlier today, I have similar code that I'd be willing to
share. It works very similar to the affy RMA functions.
Max
-----Original Message-----
From: bioconductor-bounces at stat.math.ethz.ch
[mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of James
Anderson
Sent: Wednesday, March 07, 2007 3:10 PM
To: bioconductor
Subject: [BioC] RMA, RefRMA questions
Hi,
I roughly understand the issue of RMA and RefRMA. When using RefRMA, one
RMA model is generated first based on the samples from one lab. If some
additional arrays measured in a different lab needs to be normalized,
does RefRMA automatically take care of the systematic difference between
the two labs or except RefRMA, there are still some extra work needs to
be done to correct the systematic difference between the two labs?
Thanks,
James
---------------------------------
The fish are biting.
[[alternative HTML version deleted]]
_______________________________________________
Bioconductor mailing list
Bioconductor at stat.math.ethz.ch
https://stat.ethz.ch/mailman/listinfo/bioconductor
Search the archives:
http://news.gmane.org/gmane.science.biology.informatics.conductor
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------------------------------
Message: 16
Date: Wed, 07 Mar 2007 16:09:20 -0500
From: "James W. MacDonald" <jmacdon at med.umich.edu>
Subject: Re: [BioC] RMA, RefRMA questions
To: "Kuhn, Max" <Max.Kuhn at pfizer.com>
Cc: bioconductor <bioconductor at stat.math.ethz.ch>
Message-ID: <45EF2A00.6030900 at med.umich.edu>
Content-Type: text/plain; charset="utf-8"; format=flowed
Kuhn, Max wrote:
> After googling, it seems that the package is only available form the
> authors at this time, so I can't be exact.
This package is part of BioC devel:
http://bioconductor.org/packages/2.0/bioc/html/RefPlus.html
It can be installed directly using biocLite() if you are running
R-2.5.0devel.
Best,
Jim
--
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623
**********************************************************
Electronic Mail is not secure, may not be read every day, and should not
be used for urgent or sensitive issues.
------------------------------
Message: 17
Date: Wed, 7 Mar 2007 16:26:38 -0500
From: "Kuhn, Max" <Max.Kuhn at pfizer.com>
Subject: Re: [BioC] RMA, RefRMA questions
To: "James W. MacDonald" <jmacdon at med.umich.edu>
Cc: bioconductor <bioconductor at stat.math.ethz.ch>
Message-ID:
<71257D09F114DA4A8E134DEAC70F25D307B712F4 at groamrexm03.amer.pfizer.com>
Content-Type: text/plain; charset="us-ascii"
So there is no refRMA method. They are called RMA+ and RMA++. I can't
wait for RMA#.
Thanks,
Max
-----Original Message-----
From: James W. MacDonald [mailto:jmacdon at med.umich.edu]
Sent: Wednesday, March 07, 2007 4:09 PM
To: Kuhn, Max
Cc: James Anderson; bioconductor
Subject: Re: [BioC] RMA, RefRMA questions
Kuhn, Max wrote:
> After googling, it seems that the package is only available form the
> authors at this time, so I can't be exact.
This package is part of BioC devel:
http://bioconductor.org/packages/2.0/bioc/html/RefPlus.html
It can be installed directly using biocLite() if you are running
R-2.5.0devel.
Best,
Jim
--
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623
**********************************************************
Electronic Mail is not secure, may not be read every day, and should not
be used for urgent or sensitive issues.
----------------------------------------------------------------------
LEGAL NOTICE\ Unless expressly stated otherwise, this
messag...{{dropped}}
------------------------------
Message: 18
Date: Thu, 8 Mar 2007 12:19:51 +0800 (CST)
From: "De-Jian,ZHAO" <zhaodj at ioz.ac.cn>
Subject: [BioC] two questions about limma (cont.2)
To: bioconductor at stat.math.ethz.ch
Message-ID: <2070.159.226.67.50.1173327591.squirrel at mail.ioz.ac.cn>
Content-Type: text/plain;charset=gb2312
Dear members,
Thanks to you for your attention to my questions.
Special thanks to Dr. Smyth, the author and maintainer of limma package,
for his detailed answer.
However,questions remain. The two questions first appeared in
Bioconductor
Digest, Vol 49, Issue 7 on Mar 7, 2007 .
-------Question 1: About NaNs after backgroundCorrect------------
I checked the data before and after correction. The components
("R","G","Rb" and "Gb") of RGList are all positive. The dispersed spots
at
low intensites before backgroundCorrect (plotMA(RG)) shrink to a line or
a
cluster after backgroundCorrect and normalizeWithinArrays. NaNs occur in
log(x) right during the backgroundCorrect step using method "normexp".
Therefore I investigated the function backgroundCorrect() and the method
normexp. They are as follows:
>RG.b<-backgroundCorrect(RG,method="normexp",offset=0)
> backgroundCorrect
function (RG, method = "subtract", offset = 0, printer = RG$printer,
verbose = TRUE)
{
if (is.null(RG$Rb) != is.null(RG$Gb))
stop("Background values exist for one channel but not the
other")
method <- match.arg(method, c("none", "subtract", "half",
"minimum", "movingmin", "edwards", "normexp", "rma"))
if (is.null(RG$Rb) && is.null(RG$Gb))
method <- "none"
switch(method, subtract = {
RG$R <- RG$R - RG$Rb
RG$G <- RG$G - RG$Gb
}, half = {
RG$R <- pmax(RG$R - RG$Rb, 0.5)
RG$G <- pmax(RG$G - RG$Gb, 0.5)
}, minimum = {
RG$R <- as.matrix(RG$R - RG$Rb)
RG$G <- as.matrix(RG$G - RG$Gb)
for (slide in 1:ncol(RG$R)) {
i <- RG$R[, slide] < 1e-18
if (any(i, na.rm = TRUE)) {
m <- min(RG$R[!i, slide], na.rm = TRUE)
RG$R[i, slide] <- m/2
}
i <- RG$G[, slide] < 1e-18
if (any(i, na.rm = TRUE)) {
m <- min(RG$G[!i, slide], na.rm = TRUE)
RG$G[i, slide] <- m/2
}
}
}, movingmin = {
RG$R <- RG$R - ma3x3.spottedarray(RG$Rb, printer = printer,
FUN = min, na.rm = TRUE)
RG$G <- RG$G - ma3x3.spottedarray(RG$Gb, printer = printer,
FUN = min, na.rm = TRUE)
}, edwards = {
one <- matrix(1, NROW(RG$R), 1)
delta.vec <- function(d, f = 0.1) {
quantile(d, mean(d < 1e-16, na.rm = TRUE) * (1 +
f), na.rm = TRUE)
}
sub <- as.matrix(RG$R - RG$Rb)
delta <- one %*% apply(sub, 2, delta.vec)
RG$R <- ifelse(sub < delta, delta * exp(1 - (RG$Rb +
delta)/RG$R), sub)
sub <- as.matrix(RG$G - RG$Gb)
delta <- one %*% apply(sub, 2, delta.vec)
RG$G <- ifelse(sub < delta, delta * exp(1 - (RG$Gb +
delta)/RG$G), sub)
}, normexp = {
for (j in 1:ncol(RG$R)) {
x <- RG$G[, j] - RG$Gb[, j]
out <- normexp.fit(x)
RG$G[, j] <- normexp.signal(out$par, x)
x <- RG$R[, j] - RG$Rb[, j]
out <- normexp.fit(x)
RG$R[, j] <- normexp.signal(out$par, x)
if (verbose)
cat("Corrected array", j, "\n")
}
}, rma = {
require("affy")
RG$R <- apply(RG$R - RG$Rb, 2, bg.adjust)
RG$G <- apply(RG$G - RG$Gb, 2, bg.adjust)
})
RG$Rb <- NULL
RG$Gb <- NULL
if (offset) {
RG$R <- RG$R + offset
RG$G <- RG$G + offset
}
new("RGList", unclass(RG))
}
<environment: namespace:limma>
The method normexp is within the function backgroundCorrect. It is
excerpted from the function and pasted here.
normexp = {
for (j in 1:ncol(RG$R)) {
x <- RG$G[, j] - RG$Gb[, j]
out <- normexp.fit(x)
RG$G[, j] <- normexp.signal(out$par, x)
x <- RG$R[, j] - RG$Rb[, j]
out <- normexp.fit(x)
RG$R[, j] <- normexp.signal(out$par, x)
if (verbose)
cat("Corrected array", j, "\n")
}
},
Then I modified the excerpted code and ran the code block as follows:
j=1 # j is from 1 to ncol(RG$R). Manually run the loop.
x <- RG$G[, j] - RG$Gb[, j]
out <- normexp.fit(x)
RG$G[, j] <- normexp.signal(out$par, x)
x <- RG$R[, j] - RG$Rb[, j]
out <- normexp.fit(x)
RG$R[, j] <- normexp.signal(out$par, x)
I found that the warnings of NaNs occur following the code "out <-
normexp.fit(x)".The output of this code block follows below.
> j=1
> x <- RG$G[, j] - RG$Gb[, j]
> out <- normexp.fit(x)
> RG$G[, j] <- normexp.signal(out$par, x)
> x <- RG$R[, j] - RG$Rb[, j]
> out <- normexp.fit(x)
Warning message: <<<<<<<<<<<<<<<<<<<<<<-----Warning!
Produced NaNs in: log(x)
> RG$R[, j] <- normexp.signal(out$par, x)
> out
$par
[1] 105.927694 4.874661 8.145515
$m2loglik
[1] 352762.2
$convergence
[1] 0
Then I tried to trace the origin of the warning by running the function
normexp.fit step by step. I packed the if-else clause into a customized
function myfunq1234().The modified normexp.fit for step-by-step
execution
is after the one embedded in the limma package. The code halts in the
middle and the error message points to something beyond my knowledge.
# normexp.fit Embedded in limma
> normexp.fit
function (x, trace = FALSE)
{
isna <- is.na(x)
if (any(isna))
x <- x[!isna]
if (length(x) < 4)
stop("Not enough data: need at least 4 non-missing corrected
intensities")
q <- quantile(x, c(0, 0.05, 0.1, 1), na.rm = TRUE, names = FALSE)
if (q[1] == q[4])
return(list(beta = q[1], sigma = 1, alpha = 1, m2loglik = NA,
convergence = 0))
if (q[2] > q[1]) {
beta <- q[2]
}
else {
if (q[3] > q[1]) {
beta <- q[3]
}
else {
beta <- q[1] + 0.05 * (q[4] - q[1])
}
}
sigma <- sqrt(mean((x[x < beta] - beta)^2, na.rm = TRUE))
alpha <- mean(x, na.rm = TRUE) - beta
if (alpha <= 0)
alpha <- 1e-06
Results <- optim(par = c(beta, log(sigma), log(alpha)), fn =
normexp.m2loglik,
control = list(trace = as.integer(trace)), x = x)
list(par = Results$par, m2loglik = Results$value, convergence =
Results$convergence)
}
<environment: namespace:limma>
# Modified normexp.fit
isna <- is.na(x)
if (any(isna))
x <- x[!isna]
if (length(x) < 4)
stop("Not enough data: need at least 4 non-missing corrected
intensities")
q <- quantile(x, c(0, 0.05, 0.1, 1), na.rm = TRUE, names = FALSE)
if (q[1] == q[4])
return(list(beta = q[1], sigma = 1, alpha = 1, m2loglik = NA,
convergence = 0))
myfunq1234<-function(q1,q2,q3,q4){
if (q2 > q1) {
beta <- q2
}
else {
if (q3 > q1) {
beta <- q3
}
else {
beta <- q1 + 0.05 * (q4 - q1)
}
}
}
myfunq1234(q[1],q[2],q[3],q[4])
sigma <- sqrt(mean((x[x < beta] - beta)^2, na.rm = TRUE)) <<<<--Error
ocurs hereafter!
alpha <- mean(x, na.rm = TRUE) - beta
if (alpha <= 0)
alpha <- 1e-06
Results <- optim(par = c(beta, log(sigma), log(alpha)), fn =
normexp.m2loglik,
control = list(trace = as.integer(trace)), x = x)
list(par = Results$par, m2loglik = Results$value, convergence =
Results$convergence)
Based on the fact that all RG$R, RG$Rb, RG$G and RG$Gb are positive, I
think the doubt shed upon the microarray data may be removed.
I wonder whether anyone else has reported this warning.
-------Question 2: Output of Results--------
The topTable() can output the average logFC easily, but it cannot select
differentially expressed genes based upon the combination of p value and
logFC.
The decideTests() can easily output the differentially expressed genes
based upon the combination of p value and logFC, but it cannot output
average logFC.
Is there a function that combines the two advantages?
Thanks!
------------------------------
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