Entering edit mode
Lizhe Xu
▴
210
@lizhe-xu-666
Last seen 10.3 years ago
I just tried to run gcrma and got 30 warnings. I wonder if there are
something I did wrong and if these warning affect my results. Thanks.
> gcrmaDataB<-gcrma(DataB)
Loading required package: hgu133bprobe
Loading required package: matchprobes
background correction: gcrma
normalization: quantiles
PM/MM correction : pmonly
expression values: medianpolish
background correcting...There were 30 warnings (use warnings() to see
them)
done.
normalizing...done.
22645 ids to be processed
.........
> warnings()
Warning messages:
1: multi-argument returns are deprecated in: return(y = yhat, wt)
2: multi-argument returns are deprecated in: return(y = yhat, wt)
3: multi-argument returns are deprecated in: return(y = yhat, wt)
4: multi-argument returns are deprecated in: return(y = yhat, wt)
5: multi-argument returns are deprecated in: return(y = yhat, wt)
6: multi-argument returns are deprecated in: return(y = yhat, wt)
7: multi-argument returns are deprecated in: return(y = yhat, wt)
8: multi-argument returns are deprecated in: return(y = yhat, wt)
9: multi-argument returns are deprecated in: return(y = yhat, wt)
10: multi-argument returns are deprecated in: return(y = yhat, wt)
11: multi-argument returns are deprecated in: return(y = yhat, wt)
12: multi-argument returns are deprecated in: return(y = yhat, wt)
13: multi-argument returns are deprecated in: return(y = yhat, wt)
14: multi-argument returns are deprecated in: return(y = yhat, wt)
15: multi-argument returns are deprecated in: return(y = yhat, wt)
16: multi-argument returns are deprecated in: return(y = yhat, wt)
17: multi-argument returns are deprecated in: return(y = yhat, wt)
18: multi-argument returns are deprecated in: return(y = yhat, wt)
19: multi-argument returns are deprecated in: return(y = yhat, wt)
20: multi-argument returns are deprecated in: return(y = yhat, wt)
21: multi-argument returns are deprecated in: return(y = yhat, wt)
22: multi-argument returns are deprecated in: return(y = yhat, wt)
23: multi-argument returns are deprecated in: return(y = yhat, wt)
24: multi-argument returns are deprecated in: return(y = yhat, wt)
25: multi-argument returns are deprecated in: return(y = yhat, wt)
26: multi-argument returns are deprecated in: return(y = yhat, wt)
27: multi-argument returns are deprecated in: return(y = yhat, wt)
28: multi-argument returns are deprecated in: return(y = yhat, wt)
29: multi-argument returns are deprecated in: return(y = yhat, wt)
30: multi-argument returns are deprecated in: return(y = yhat, wt)
Lizhe
-----Original Message-----
From: bioconductor-request@stat.math.ethz.ch [mailto:bioconductor-
request@stat.math.ethz.ch]
Sent: Monday, March 15, 2004 12:43 PM
To: bioconductor@stat.math.ethz.ch
Subject: Bioconductor Digest, Vol 13, Issue 30
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When replying, please edit your Subject line so it is more specific
than "Re: Contents of Bioconductor digest..."
Today's Topics:
1. Re: questions about Affy package from new user: one more
question (James MacDonald)
2. Re: MOE430 A and B (Ben Bolstad)
3. Re: utils required for EBarrays (A.J. Rossini)
4. Subsetting Affybatch objects by gene lists. (Horswell, Stuart)
5. Re: Minimum no. of chips for express/rma/gcrma etc
(James MacDonald)
6. Re: SAM : warning messages problem (James MacDonald)
7. Re: Quantile normalization vs. data distributions (Naomi Altman)
8. question on making affy environment (Straubhaar, Juerg)
9. RE: questions about Affy package from new user: onemore
question (Adaikalavan Ramasamy)
----------------------------------------------------------------------
Message: 1
Date: Mon, 15 Mar 2004 09:11:29 -0500
From: "James MacDonald" <jmacdon@med.umich.edu>
Subject: Re: [BioC] questions about Affy package from new user: one
more question
To: <lxu@chnola-research.org>, <bioconductor@stat.math.ethz.ch>
Message-ID: <s055735d.015@med-gwia-02a.med.umich.edu>
Content-Type: text/plain; charset=US-ASCII
AH. GS==GeneSpring.
If you want to join them before importing to GeneSpring, you should do
this after computing expression values. You can do something like:
out <- rbind(exprs(exprSetA), exprs(exprSetB))
write.table(out, "Combined expression data.txt", sep="\t", quote=F,
col.names=NA)
HTH,
Jim
James W. MacDonald
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623
>>> "Lizhe Xu" <lxu@chnola-research.org> 03/14/04 06:20PM >>>
Now, I tried to load the exported data from Bioconductor to GeneSpring
and found another question. Since I used U133 chip set, I wonder if I
can joint the U133A and B directly and import them to GS or I should
do
probeset level normalization first (if so, which package in
bioconductor
can do it) before joint them. Thanks.
Lxu
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------------------------------
Message: 2
Date: Mon, 15 Mar 2004 06:17:23 -0800
From: Ben Bolstad <bolstad@stat.berkeley.edu>
Subject: Re: [BioC] MOE430 A and B
To: peter robinson <peter.robinson@t-online.de>
Cc: bioconductor@stat.math.ethz.ch
Message-ID: <1079360243.1562.2.camel@bmbbox.dyndns.org>
Content-Type: text/plain
you need to read in the type A and type B files into separate
affybatch
objects.
eg
my.Data.A <-
ReadAffy(filenames=c("blahA1.cel","blahA2.cel","blahA3.cel"))
my.Data.B <-
ReadAffy(filenames=c("blahB1.cel","blahB2.cel","blahB3.cel"))
Ben
On Mon, 2004-03-15 at 04:20, peter robinson wrote:
> Dear List members,
>
> I would like to use the affy package to analyze data from MOE430A
and -B
> chips. I tried to read in data from both types of chips at once
using
> data <- ReadAffy(widget=T)
> and then reading in 3 MOE430A and 3 MOE430B CEL files.
> I got the error message:
> "Cel file does not seem to beo of 430MOEA type" when the script
tried to input
> data from a 430MOEB Cel file. I had imported the CDF and annotation
packages
> for both types of chip.
> I am using R 1.81, Bioconductor 1.3 on a SuSe 8.1 linux system.
>
> Thanks for any advice/tips!
>
> Peter
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor@stat.math.ethz.ch
> https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
--
Ben Bolstad <bolstad@stat.berkeley.edu>
http://www.stat.berkeley.edu/~bolstad
------------------------------
Message: 3
Date: Mon, 15 Mar 2004 06:22:12 -0800
From: rossini@blindglobe.net (A.J. Rossini)
Subject: Re: [BioC] utils required for EBarrays
To: <arne.muller@aventis.com>
Cc: bioconductor@stat.math.ethz.ch
Message-ID: <85wu5mb36z.fsf@servant.blindglobe.net>
Content-Type: text/plain; charset=us-ascii
As I tried to write clearly in my last email about the upcoming
release, you MUST use the Devel branch of R with packages in the devel
branch of Bioconductor.
best,
-tony
<arne.muller@aventis.com> writes:
> Hello,
>
> I'm running BioC 1.3 and R 1.8.1. I've tried to install the
EBarrays
> pacckage from the devel branch, but get the following error:
>
>> install.packages2('EBarrays', force=T)
> Note: You did not specify a download type. Using a default value
of: Source
> This will be fine for almost all users
>
> [1] "Attempting to download EBarrays from
> http://www.bioconductor.org/repository/devel/package/Source"
> [1] "Download complete."
> [1] "Installing EBarrays"
> * Installing *source* package 'EBarrays' ...
> ** libs
> gcc -I/tgx/soft/lib/R/include -I/usr/local/include
-D__NO_MATH_INLINES
> -mieee-fp -fPIC -g -O2 -c ebarrays.c -o ebarrays.o
> gcc -shared -L/usr/local/lib -o EBarrays.so ebarrays.o
> -L/tgx/soft/lib/R/bin -lR
> ** R
> ** data
> ** demo
> ** inst
> ** save image
> Error: Requires utils to run properly
> In addition: Warning message:
> There is no package called 'utils' in: library(package,
character.only =
> TRUE, logical = TRUE, warn.conflicts = warn.conflicts,
> Execution halted
> /tgx/soft/lib/R/bin/INSTALL: line -116: 2881 Broken pipe
cat
> "/tgx/soft/lib/R/library/EBarrays/R/EBarrays"
> ERROR: execution of package source for 'EBarrays' failed
> ** Removing '/tgx/soft/lib/R/library/EBarrays'
> Warning message:
> Installation of package EBarrays had non-zero exit status in:
> installPkg(fileName, pkg, pkgVer, type, lib, repEntry, versForce)
>>From URL:
http://www.bioconductor.org/repository/devel/package/Source
> EBarrays version 1.0-17
>
> I cannot find the utils package, where is it locatd in the BioC
repositories?
> Can I install EBarrays without installing the complete develompent
branch?
>
> kind regards + thank for your help,
>
> Arne
>
> --
> Arne Muller, Ph.D.
> Toxicogenomics, Aventis Pharma
> arne dot muller domain=aventis com
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor@stat.math.ethz.ch
> https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
>
--
rossini@u.washington.edu
http://www.analytics.washington.edu/
Biomedical and Health Informatics University of Washington
Biostatistics, SCHARP/HVTN Fred Hutchinson Cancer Research
Center
UW (Tu/Th/F): 206-616-7630 FAX=206-543-3461 | Voicemail is unreliable
FHCRC (M/W): 206-667-7025 FAX=206-667-4812 | use Email
CONFIDENTIALITY NOTICE: This e-mail message and any
attachme...{{dropped}}
------------------------------
Message: 4
Date: Mon, 15 Mar 2004 14:22:03 -0000
From: "Horswell, Stuart" <stuart.horswell@csc.mrc.ac.uk>
Subject: [BioC] Subsetting Affybatch objects by gene lists.
To: <bioconductor@stat.math.ethz.ch>
Message-ID:
<a09f613372b70c4cbce35a9095f434f009ffe9@icex34.cc.ic.ac.uk>
Content-Type: text/plain; charset="iso-8859-1"
Hi all,
I'm trying to run an analysis on 24 Affymetrix HGu95v2 chips.
I've set up, via merge.AffyBatch, an affybatch object containing all
24 arrays.
A1 <- read.affybatch("A1.cel")
.
.
.
A24 <- read.affybatch("A24.cel")
A <- merge.AffyBatch(A1, A2)
A <- merge.AffyBatch(A, A3)
.
.
.
A<- merge.AffyBatch(A, A24)
I then computed MAS5 type Present/Absent calls for each array using
mas5calls.
A.calls <- mas5calls(A)
p.a.A <- exprs(A.calls)
What I'd like to do now is remove all of those genes without a single
present call across all 24 arrays before normalizing.
I can use the p.a.A file to obtain a list of the gene names/affy id
tags that I want to remove but I can't figure out how to delete the
relavent probe pairs from my affybatch object.
In fact that only things I've been able to find on the mailing list
archive and/or vignettes are how to subset by array or how to remove
chunks from the cdf environment - but this presents me with two
problems, first I'm not sure I can get the pattern matching working
well enough to identify which entry numbers in the cdf file correspond
to the gene list I have, and secondly, people have already commented
that this isn't neccessarily a sensible approach for proper analysis
anyway. So I'm kind of stumped now!
Any help or advice would be most greatfully received,
many thanks,
Stu
------------------------------
Message: 5
Date: Mon, 15 Mar 2004 09:24:44 -0500
From: "James MacDonald" <jmacdon@med.umich.edu>
Subject: Re: [BioC] Minimum no. of chips for express/rma/gcrma etc
To: <bioconductor@stat.math.ethz.ch>, <aedin.culhane@ucd.ie>
Message-ID: <s055766d.081@med-gwia-02a.med.umich.edu>
Content-Type: text/plain; charset=US-ASCII
I think you can argue that the batch methods (rma, gcrma) require more
than a certain number of chips to accurately estimate the parameters
you
are modeling. However, this probably only applies to genes that are
not
changing that much, so you will still see the ones that really change.
I don't think mas5 is affected by the number of chips.
Best,
Jim
James W. MacDonald
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623
>>> Aedin <aedin.culhane@ucd.ie> 03/15/04 07:20AM >>>
Dear BioC
Are rma/gcrma/mas5.0/ etc limited by a minimum number of chip (.cel)
files?
I am calling expression values on a dataset with only 2 chips
(treated/control, pooled RNA from n=5). Is 2 chips too few .cel files
for
these methods?
Thanks for your help
Aedin
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------------------------------
Message: 6
Date: Mon, 15 Mar 2004 09:27:49 -0500
From: "James MacDonald" <jmacdon@med.umich.edu>
Subject: Re: [BioC] SAM : warning messages problem
To: <willy.wynant@curie.fr>, <bioconductor@stat.math.ethz.ch>
Message-ID: <s0557732.029@med-gwia-02a.med.umich.edu>
Content-Type: text/plain; charset=US-ASCII
This is really only a problem for the package maintainer. Your results
are not affected.
Basically there has been a change in R, and siggenes has not been
modified to account for that change. Right now everything is still
working correctly, you just have a bunch of annoying error messages.
Best,
Jim
James W. MacDonald
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623
>>> Willy Wynant <willy.wynant@curie.fr> 03/15/04 07:57AM >>>
Hi,
I am using siggenes package and I am encountering problems with
warning
messages with the function sam.
For example, if I type :
res<-sam(data,3:11,12:88,B=1000,alpha.s0=seq(0,1,0.02),factor.s0=1.482
6,delta.fdr=(1:10)/10,rand=123,vec.lambda.p0=(0:95)/100,ngenes=NA,iter
ation=10,initial.delta=(1:20)/10)
I've got as result the SAM analysis and the following answer:
Warning messages :
1:multi-argument returns are deprecated in :
return(r,s,r.perm,s.perm,Z,mat.samp,var.0.genes,NA.genes)
2:multi-argument returns are deprecated in :
return(alpha.hat,s.zero,cv,cv.zero)
3:multi-argument returns are deprecated in :
return(p0,spline.out,vec.p0)
4:multi-argument returns are deprecated in :
return(tab.fdr,mat.fdr,p0)
5 :multi-argument returns are deprecated in :
return(d,d.sort,s,d.bar,d.perm,mat.samp,s0,FDR,p0,fdr.ngenes,
I installed the R.1.8.1 version.
Could you help me ?
Thank you
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------------------------------
Message: 7
Date: Mon, 15 Mar 2004 10:04:57 -0500
From: Naomi Altman <naomi@stat.psu.edu>
Subject: Re: [BioC] Quantile normalization vs. data distributions
To: "Stan Smiley" <swsmiley@genetics.utah.edu>, "Bioconductor Mailing
list" <bioconductor@stat.math.ethz.ch>
Message-ID: <6.0.0.22.2.20040314225049.01d7ffb8@stat.psu.edu>
Content-Type: text/plain; charset="us-ascii"; format=flowed
This is a very good question that I have also been puzzling over. It
seems
useless to try
tests of equality of the distribution such as Kolmogorov-Smirnov- due
to
the huge sample size you
would almost certainly get a significant result.
Currently, I am using the following graphical method:
1. I compute a kernel density estimate of the combined data of all
probes
on all the arrays.
2. I compute a kernel density estimate of the data for each array.
3. I plot both smooths on the same plot, and decide if they are the
same.
Looking at what I wrote above, I think it would be better in steps 1
and 2
to background correct and
center each array before combining. It might also be between to
reduce the
data to standardized scores before combining, unless
you think that the overall scaling is due to your "treatment effect".
It seems like half of what I do is ad hoc, so I always welcome any
criticisms or suggestions.
--Naomi Altman
At 06:07 PM 3/11/2004, Stan Smiley wrote:
>Greetings,
>
>I have been trying to find a quantitative measure to tell when the
data
>distributions
>between chips are 'seriously' different enough from each other to
violate
>the
>assumptions behind quantile normalization. I've been through the
archives
>and seen some discussion of this matter, but didn't come away with a
>quantitative measure I
>could apply to my data sets to assure me that it would be OK to use
quantile
>normalization.
>
>
>"Quantile normalization uses a single standard for all chips, however
it
>assumes that no serious change in distribution occurs"
>
>Could someone please point me in the right direction on this?
>
>Thanks.
>
>Stan Smiley
>stan.smiley@genetics.utah.edu
>
>_______________________________________________
>Bioconductor mailing list
>Bioconductor@stat.math.ethz.ch
>https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
Naomi S. Altman 814-865-3791 (voice)
Associate Professor
Bioinformatics Consulting Center
Dept. of Statistics 814-863-7114 (fax)
Penn State University 814-865-1348
(Statistics)
University Park, PA 16802-2111
------------------------------
Message: 8
Date: Mon, 15 Mar 2004 10:33:00 -0500
From: "Straubhaar, Juerg" <juerg.straubhaar@umassmed.edu>
Subject: [BioC] question on making affy environment
To: <bioconductor@stat.math.ethz.ch>
Message-ID:
<1A42F1E1A1E73A4F8C6048789F34A32F2D8AF5@edunivmail02.ad.umassmed.edu>
Content-Type: text/plain; charset="Windows-1252"
I would like to make a mixed environment for the mouse U74B (and C
separately) version 1 and 2 chip set using common probesets. I know
Ben Bolstad has built such a mixted environment for the U74 A chip.
How do you do this? There are 5 library files for each chip. Do I have
to create new library files containing only the common probesets and
then using the make.cdf.package?
I would be grateful for suggestions on how to proceed.
Juerg Straubhaar
Umass Med School
------------------------------
Message: 9
Date: Mon, 15 Mar 2004 16:22:42 -0000
From: "Adaikalavan Ramasamy" <ramasamy@cancer.org.uk>
Subject: RE: [BioC] questions about Affy package from new user:
onemore question
To: "James MacDonald" <jmacdon@med.umich.edu>,
<lxu@chnola-research.org>,
<bioconductor@stat.math.ethz.ch>
Message-ID: <odepicohndbjehifcimpkeekcbaa.ramasamy@cancer.org.uk>
Content-Type: text/plain; charset="US-ASCII"
The rownames of HGU-133A and HGU-133B are not unique (there is about
100+
redundancies). You might want to add these codes before rbind() to
avoid any
confusion later.
A <- exprs(exprSetA)
rownames(A) <- paste("A.", rownames(A))
B <- exprs(exprSetB)
rownames(B) <- paste("B.", rownames(B))
Also, doing normalization before summary is better because we have
more
information to utilize at probe level.
> -----Original Message-----
> From: bioconductor-bounces@stat.math.ethz.ch
> [mailto:bioconductor-bounces@stat.math.ethz.ch]On Behalf Of James
> MacDonald
> Sent: 15 March 2004 14:11
> To: lxu@chnola-research.org; bioconductor@stat.math.ethz.ch
> Subject: Re: [BioC] questions about Affy package from new user:
onemore
> question
>
>
> AH. GS==GeneSpring.
>
> If you want to join them before importing to GeneSpring, you should
do
> this after computing expression values. You can do something like:
>
> out <- rbind(exprs(exprSetA), exprs(exprSetB))
> write.table(out, "Combined expression data.txt", sep="\t", quote=F,
> col.names=NA)
>
> HTH,
>
> Jim
>
>
>
> James W. MacDonald
> Affymetrix and cDNA Microarray Core
> University of Michigan Cancer Center
> 1500 E. Medical Center Drive
> 7410 CCGC
> Ann Arbor MI 48109
> 734-647-5623
>
> >>> "Lizhe Xu" <lxu@chnola-research.org> 03/14/04 06:20PM >>>
> Now, I tried to load the exported data from Bioconductor to
GeneSpring
> and found another question. Since I used U133 chip set, I wonder if
I
> can joint the U133A and B directly and import them to GS or I should
do
> probeset level normalization first (if so, which package in
bioconductor
> can do it) before joint them. Thanks.
>
> Lxu
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor@stat.math.ethz.ch
> https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor@stat.math.ethz.ch
> https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
>
------------------------------
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End of Bioconductor Digest, Vol 13, Issue 30
