[BioC] INstalling devel version
James MacDonald
jmacdon at med.umich.edu
Mon Aug 16 21:18:56 CEST 2004
Mayte,
You will not be able to use annaffy with the devel versions of the
annotation packages. There are often major structural changes made to
these packages that are incompatible with the existing versions of
annaffy (release and devel). Since it takes some time for Colin to 'fix'
annaffy to work with the new versions, each existing version of annaffy
is 'tied' to a given version of annotation package. For instance, the
current versions of annaffy are tied to the 1.5.x annotation packages,
and if you have the devel (1.6.x) versions, annaffy will attempt to
download the compatible versions.
That said, you should have got a much better error message than that.
Will you let me know exactly what the error message was?
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
>>> Mayte Suarez-Farinas <mayte at babel.rockefeller.edu> 08/16/04 02:51PM
>>>
On Mon, 16 Aug 2004 bioconductor-request at stat.math.ethz.ch wrote:
Thank you James and Wolfgang. I succeed with tkl/tk but
I could'nt install annotation package (devel). It succesfully installed
GO
but stop in humann LLMAppings. Below follows the messages..
As a result, I cant not use annaffy because it fails with:
You do not have GO or KEGG installed
or you have incompatible versions.
[1] "Attempting to download humanLLMappings from
http://www.bioconductor.org/data/metaData-devel/"
[1] "Download complete."
[1] "Installing humanLLMappings"
* Installing *source* package 'humanLLMappings' ...
** R
** data
** help
>>> Building/Updating help pages for package 'humanLLMappings'
Formats: text html latex example
humanLLMappings text html latex
humanLLMappingsACCNUM2LL text html latex example
humanLLMappingsGO2LL text html latex example
humanLLMappingsLL2ACCNUM text html latex example
humanLLMappingsLL2GO text html latex example
humanLLMappingsLL2PMID text html latex example
humanLLMappingsLL2UG text html latex example
humanLLMappingsPMID2LL text html latex example
humanLLMappingsQC text html latex
humanLLMappingsUG2LL text html latex example
Execution halted
ERROR: installing package indices failed
** Removing '/usr/lib/R/library/humanLLMappings'
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 humanLLMappings from
http://www.bioconductor.org/data/metaData-devel/"
Warning message:
Installation of package humanLLMappings had non-zero exit status in:
installPkg(fileName, pkg, pkgVer, type, lib, repEntry, versForce)
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> Today's Topics:
>
> 1. Re: Labels in exprs to plot (mcolosim at brandeis.edu)
> 2. R-Tcl/Tk support on RedHat 9 (was Re: [BioC] Re: Bioconductor
> Digest, Vol 18, Issue 10) (James Wettenhall)
> 3. Selecting probe pairs for analysis (Hee Siew Wan)
> 4. RE: Harsh results using limma! (michael watson (IAH-C))
>
>
>
----------------------------------------------------------------------
>
> Message: 1
> Date: Sun, 15 Aug 2004 09:56:19 -0400
> From: mcolosim at brandeis.edu
> Subject: Re: [BioC] Labels in exprs to plot
> To: Adaikalavan Ramasamy <ramasamy at cancer.org.uk>
> Cc: James MacDonald <jmacdon at med.umich.edu>, BioConductor mailing
list
> <bioconductor at stat.math.ethz.ch>
> Message-ID: <1092578179.411f6b83556b9 at webmail.staff.brandeis.edu>
> Content-Type: text/plain; charset=ISO-8859-1
>
> I think this would be great.
>
> Thanks for the function and help.
>
> Marc
>
> Quoting Adaikalavan Ramasamy <ramasamy at cancer.org.uk>:
>
> > I would like to contribute this little function if anyone is
interested.
> >
> > exprSet.sampleNames.cleanup <- function(object){
> > stopifnot( is(object, "exprSet") )
> >
> > cn <- sampleNames(object)
> > cn <- sapply( strsplit(cn, split="\/"), function(x) x[ length(x)
] )
> > cn <- sub( ".cel$|.CEL$", "", cn)
> > sampleNames(object) <- cn
> > return(object)
> > }
> >
> >
> > On a particular dataset I have, I have the following :
> >
> > > sampleNames(obj) # before cleanup
> >
> > [1] "/home/adai/tmp/0029_1209_H95Av2_KF0077.cel"
> > [2] "/home/adai/tmp/0029_1210_H95A2_KF0079.cel"
> > [3] "/home/adai/tmp/0029_1213_H95A2_KF0110.cel"
> > [4] "/home/adai/tmp/0029_1221_H95A2_KF0144.cel"
> > [5] "/home/adai/tmp/0029_1222_H95A2_KF0146.cel"
> > [6] "/home/adai/tmp/0029_1224_HU95A_KF0150.cel"
> > [7] "/home/adai/tmp/0029_1225_H95A2_KF0157.CEL"
> > [8] "/home/adai/tmp/0029_1237_H95A2_KF0125.CEL"
> > [9] "/home/adai/tmp/0029_1238_H95A2_KF0128.CEL"
> > [10] "/home/adai/tmp/0029_1239_H95A2_KF0131.CEL"
> > [11] "/home/adai/tmp/0029_1240_H95A2_KF0133.CEL"
> > [12] "/home/adai/tmp/0029_1377_H95A2_KF-104.CEL"
> >
> > obj <- exprSet.sampleNames.cleanup(obj)
> > > sampleNames(obj) # after cleanup
> >
> > [1] "0029_1209_H95Av2_KF0077" "0029_1210_H95A2_KF0079"
> > [3] "0029_1213_H95A2_KF0110" "0029_1221_H95A2_KF0144"
> > [5] "0029_1222_H95A2_KF0146" "0029_1224_HU95A_KF0150"
> > [7] "0029_1225_H95A2_KF0157" "0029_1237_H95A2_KF0125"
> > [9] "0029_1238_H95A2_KF0128" "0029_1239_H95A2_KF0131"
> > [11] "0029_1240_H95A2_KF0133" "0029_1377_H95A2_KF-104"
> >
> >
> >
> > On Sat, 2004-08-14 at 22:11, James MacDonald wrote:
> > > The easiest way I know to do this is to use list.celfiles() when
you
> > > read in your AffyBatch.
> > >
> > > abatch <- read.affybatch(filenames=list.celfiles())
> > >
> > > If you simply use ReadAffy(), you will get the entire path and
will have
> > > to use sub() to truncate later.
> > >
> > > 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
> > > >>> <mcolosim at brandeis.edu> 08/14/04 10:11 AM >>>
> > > Hi,
> > >
> > > I'm having the hardest time trying to get the correct labels from
my
> > > exprs to
> > > plot correctly. I'm using the affy to read in cels and process
them.
> > > However,
> > > the lables I get are the "fullpath" to the files and not the ones
in
> > > pData.
> > >
> > > Is there a way to get the correct labels minus the .CEL from
pData to be
> > > used
> > > as labels for plot (even exprs2excel is now printing out the full
paths,
> > > which it didn't do before).
> > >
> > > Basically I clustered my arrays and want to view it, but with the
full
> > > path as
> > > labels it is tiny.
> > >
> > > hcRMA <- hclust(....)
> > > plot(hcRMA, labels = ?, main = "Hierarchical clustering
dendrogram"
> > >
> > > Thanks
> > > Marc
> > >
> > > _______________________________________________
> > > 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
> > >
> >
> >
>
>
>
> ------------------------------
>
> Message: 2
> Date: Mon, 16 Aug 2004 11:15:01 +1000 (EST)
> From: James Wettenhall <wettenhall at wehi.edu.au>
> Subject: R-Tcl/Tk support on RedHat 9 (was Re: [BioC] Re:
Bioconductor
> Digest, Vol 18, Issue 10)
> To: Mayte Suarez-Farinas <mayte at babel.rockefeller.edu>
> Cc: bioconductor at stat.math.ethz.ch
> Message-ID:
> <Pine.GSO.4.58.0408161108280.16337 at unix33.alpha.wehi.edu.au>
> Content-Type: TEXT/PLAIN; charset=US-ASCII
>
> Hi Mayte,
>
> On Fri, 13 Aug 2004, Mayte Suarez-Farinas wrote:
> > I had to upgrade to R 2.0.0 to be able to run GO. I made
> > a fresh install from subversion. I am running Redhat 9.0.
> > The tcltk built thus aborts:
> >
> > stop("Tcl/Tk support is not available on this system")
>
> In previous versions of RedHat Linux, all you needed to get
> R-Tcl/Tk support was the tcl and tk RPMs (RedHat packages),
> whereas from RedHat 9.0 onwards (including Fedora), the tcl and
> tk RPMs no longer include the header files tcl.h and tk.h so
> you need to install the tcl-devel and tk-devel RPMs as well.
>
> I don't know much about RPMs, but maybe it would be nice if the
> recent RPMs for R (for RedHat Linux) could test whether
> tcl-devel and tk-devel are missing and give a warning
> if appropriate.
>
> One other change to be aware of is that the Tcl/Tk files (from
> RPM) on RedHat 9 and later are now in /usr/share/ instead of
> /usr/lib/
>
> Too see exactly where they are, just type:
> rpm -ql tcl
> rpm -ql tk
>
> And I suspect that:
> rpm -q tcl-devel
> rpm -q tk-devel
>
> will reveal that you have not yet installed these packages from
> your RedHat 9 CDs.
>
> Hope this helps,
> James
>
>
>
> ------------------------------
>
> Message: 3
> Date: Mon, 16 Aug 2004 14:41:51 +0800
> From: "Hee Siew Wan" <g0203658 at nus.edu.sg>
> Subject: [BioC] Selecting probe pairs for analysis
> To: <bioconductor at stat.math.ethz.ch>
> Message-ID:
> <2181704595AEB44F9446B2558970B05810A27B at MBOX22.stu.nus.edu.sg>
> Content-Type: text/plain; charset="utf-8"
>
> Hi All,
>
> I'm interested in calculating the expression measure using only 8
pairs of probes from each probe set of Arabidopsis genechip (i.e instead
of using the whole 11 pairs of a probe set). After searching through the
archive, I found that I can create a new cdf environment that excludes
the pairs that I'm not interested in. However, when I tried using
makecdfenv to create the new CDF package, I get a Segmentation fault.
I'm using R Version 1.9.0 (2004-04-12) on UNIX platform.
>
> I understand that the error occurs due to the file that I have. I
have a ATH1-121501.CDF of type Channel Definition File (which I didn't
have problem reading) and I modified this file by deleting 1 pair of
probes from 266455_at. I saved it as another .CDF. I opened the file
using EditPadLite Version 5.3.0 and did the modification from there as
well. I'm not very sure where did I make mistake(s). I'd appreciate any
comment on this.
>
> Is there another way of reading certain probe pairs instead of
deleting them in the CDF? I'd appreciate any help. Thanks.
>
> Cheers
> siew wan
>
>
> ------------------------------
>
> Message: 4
> Date: Mon, 16 Aug 2004 09:31:41 +0100
> From: "michael watson (IAH-C)" <michael.watson at bbsrc.ac.uk>
> Subject: RE: [BioC] Harsh results using limma!
> To: "Gordon K Smyth" <smyth at wehi.edu.au>, "David K Pritchard"
> <dpritch at u.washington.edu>
> Cc: Anthony Rossini <rossini at u.washington.edu>,
> bioconductor at stat.math.ethz.ch
> Message-ID:
>
<8975119BCD0AC5419D61A9CF1A923E951746B3 at iahce2knas1.iah.bbsrc.reserved>
>
> Content-Type: text/plain; charset="us-ascii"
>
> Hi Guys
>
> Well this turned into a very interesting discussion, thank you for
your
> inputs. All of the explanations lead to a single conclusion, and
that
> is that I (we?) need to find significant differences which are
present
> in only subsets of the data.
>
> Let me explain - here I had samples from three animals. Two animals
> showed what looks like highly-repeatable differential expression,
and
> the third did not. If we make the assumption that this is down to
> biological variation (ie two of my animals showed an immune
response,
> the third did not, simply because they are different animals), then
> standard statistical tests are missing an effect which is present in
two
> thirds of my population. If you ask me "are you interested in
finding
> effects which are present in only two thirds of your population?"
then
> the answer is of course I am!
>
> Over the last 5 years the whole issue of pharmacogenomics became
huge,
> the right drug for the right patient etc, and I know I am
speculating
> wildly here, but perhaps what my data is showing me is exactly that
-
> that two-thirds of my population show a particular immune response
but
> the other third does not. And that's very interesting ;-)
>
> Now, to the non-statistician, the "bull in a china shop" approach to
> solving this would appear to be to take all possible subsets of my
data
> and running limma on them, to find significant changes in subsets of
my
> data. Clearly this becomes problematic for large datasets.
Presumably
> there are many more intelligent ways....?
>
> Thanks again
>
> Mick
>
> -----Original Message-----
> From: Gordon K Smyth [mailto:smyth at wehi.edu.au]
> Sent: 14 August 2004 01:07
> To: David K Pritchard
> Cc: Anthony Rossini; bioconductor at stat.math.ethz.ch
> Subject: Re: [BioC] Harsh results using limma!
>
>
> > I think Mick's experiences point out a fundamental problem with
> > current statistical analysis of microarray data. If his data was
.2,
> > .2, .2, (dye flips) -.2, -.2, -.2 then Limma would note this gene
as
> highly differentially expressed. In contrast when he sees 6.29,
5.54,
> 0.2, (dye
> > flips)-5.27,-4.61, -0.2 Limma did not mark it as differentially
> expressed.
>
> Actually it is not true that limma will necessarily rank the first
gene
> higher than the second.
> Obviously t-tests would do so, but limma may well rank the second
gene
> higher depending on the information about variability inferred from
the
> whole data set. Looking at fold change alone ranks the second gene
> higher while t-tests would rank the first higher. Limma is somewhere
in
> between depending on the dataset. A typical microarray dataset
actually
> would lead to the second gene being ranked higher, i.e., would lead
to
> the ranking that you would prefer.
>
> > As a biologist I would argue the case for the genes actually
> > being differentially expressed is much higher in the second case.
Yet
>
> > using modified T-statistic approaches and with the limited number
of
> > repeats common with current array experiments, I see array
> experiments "missing" these very interesting high variance genes all
the
> time.
> > Current analytical techniques put a high premium on consistency
of
>
> > results and a lower premium on strength of differential expression
> > which is the parameter that biologists would argue is the most
> significant.
> > There are a variety of biological reasons why high variance
genes
>
> > should exist and personally I think these genes are likely to be
the
> > biologically interesting ones that we should be looking for on
> microarrays.
> > I understand why Limma does what it is does and it is a
> > fantastically useful program. However, I would suggest to the
> > statisticians reading this message that it would be very useful to
> > start developing analytical techniques which could better detect
high
> > variance genes.
>
> I agree with the overall point. Two strategies currently available
are:
> 1. Use spot quality weights. In the example given above it appears
that
> two of the arrays or spots have failed to register any worthwhile
fold
> change for a gene which is differentially expressed on the other
arrays.
> If this can be identified as being due to low quality spots or
arrays,
> then the values may be down-weighted in an analysis and the gene
will
> revert to being highly significant. 2. If small fold changes are not
of
> biological interest to you, then you can require a minimum magnitude
for
> the fold change as well as looking for evidence of differential
> expression.
>
> Gordon
>
> > David Pritchard
>
> _______________________________________________
> 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
>
>
> End of Bioconductor Digest, Vol 18, Issue 15
> ********************************************
>
--
Mayte Suarez Farinas
The Rockefeller University
1230 York Avenue, Box 212
New York, NY 10021
phone: 1-212-327-8186
fax: 1-212-327-7422
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