[BioC] warning messages in gcrma

Lizhe Xu lxu at chnola-research.org
Mon Mar 15 22:22:35 MET 2004


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-----
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Sent: Monday, March 15, 2004 12:43 PM
To: bioconductor at stat.math.ethz.ch
Subject: Bioconductor Digest, Vol 13, Issue 30


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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 at med.umich.edu>
Subject: Re: [BioC] questions about Affy package from new user: one
	more question
To: <lxu at chnola-research.org>, <bioconductor at stat.math.ethz.ch>
Message-ID: <s055735d.015 at 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 at 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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https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor



------------------------------

Message: 2
Date: Mon, 15 Mar 2004 06:17:23 -0800
From: Ben Bolstad <bolstad at stat.berkeley.edu>
Subject: Re: [BioC] MOE430 A and B
To: peter robinson <Peter.Robinson at t-online.de>
Cc: bioconductor at stat.math.ethz.ch
Message-ID: <1079360243.1562.2.camel at 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 at stat.math.ethz.ch
> https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
-- 
Ben Bolstad <bolstad at stat.berkeley.edu>
http://www.stat.berkeley.edu/~bolstad



------------------------------

Message: 3
Date: Mon, 15 Mar 2004 06:22:12 -0800
From: rossini at blindglobe.net (A.J. Rossini)
Subject: Re: [BioC] utils required for EBarrays
To: <Arne.Muller at aventis.com>
Cc: bioconductor at stat.math.ethz.ch
Message-ID: <85wu5mb36z.fsf at 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 at 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 at stat.math.ethz.ch
> https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
>

-- 
rossini at 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 at csc.mrc.ac.uk>
Subject: [BioC] Subsetting Affybatch objects by gene lists.
To: <bioconductor at stat.math.ethz.ch>
Message-ID:
	<A09F613372B70C4CBCE35A9095F434F009FFE9 at 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 at med.umich.edu>
Subject: Re: [BioC] Minimum no. of chips for express/rma/gcrma etc
To: <bioconductor at stat.math.ethz.ch>, <aedin.culhane at ucd.ie>
Message-ID: <s055766d.081 at 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 at 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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Bioconductor at stat.math.ethz.ch 
https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor



------------------------------

Message: 6
Date: Mon, 15 Mar 2004 09:27:49 -0500
From: "James MacDonald" <jmacdon at med.umich.edu>
Subject: Re: [BioC] SAM : warning messages problem
To: <Willy.Wynant at curie.fr>, <bioconductor at stat.math.ethz.ch>
Message-ID: <s0557732.029 at 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 at 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=1.4826,delta.fdr=(1:10)/10,rand=123,vec.lambda.p0=(0:95)/100,ngenes=NA,iteration=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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Bioconductor mailing list
Bioconductor at stat.math.ethz.ch 
https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor



------------------------------

Message: 7
Date: Mon, 15 Mar 2004 10:04:57 -0500
From: Naomi Altman <naomi at stat.psu.edu>
Subject: Re: [BioC] Quantile normalization vs. data distributions
To: "Stan Smiley" <swsmiley at genetics.utah.edu>,	"Bioconductor Mailing
	list" <bioconductor at stat.math.ethz.ch>
Message-ID: <6.0.0.22.2.20040314225049.01d7ffb8 at 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 at genetics.utah.edu
>
>_______________________________________________
>Bioconductor mailing list
>Bioconductor at 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 at umassmed.edu>
Subject: [BioC] question on making affy environment
To: <bioconductor at stat.math.ethz.ch>
Message-ID:
	<1A42F1E1A1E73A4F8C6048789F34A32F2D8AF5 at 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 at cancer.org.uk>
Subject: RE: [BioC] questions about Affy package from new user:
	onemore	question
To: "James MacDonald" <jmacdon at med.umich.edu>,
	<lxu at chnola-research.org>,	<bioconductor at stat.math.ethz.ch>
Message-ID: <ODEPICOHNDBJEHIFCIMPKEEKCBAA.ramasamy at 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 at stat.math.ethz.ch
> [mailto:bioconductor-bounces at stat.math.ethz.ch]On Behalf Of James
> MacDonald
> Sent: 15 March 2004 14:11
> To: lxu at chnola-research.org; bioconductor at 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 at 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 at stat.math.ethz.ch
> https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
>



------------------------------

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