Question: INstalling devel version
0
14.8 years ago by
Mayte Suarez-Farinas300 wrote:
On Mon, 16 Aug 2004 bioconductor-request@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) > Send Bioconductor mailing list submissions to > bioconductor@stat.math.ethz.ch > > To subscribe or unsubscribe via the World Wide Web, visit > https://stat.ethz.ch/mailman/listinfo/bioconductor > or, via email, send a message with subject or body 'help' to > bioconductor-request@stat.math.ethz.ch > > You can reach the person managing the list at > bioconductor-owner@stat.math.ethz.ch > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of Bioconductor digest..." > > > Today's Topics: > > 1. Re: Labels in exprs to plot (mcolosim@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@brandeis.edu > Subject: Re: [BioC] Labels in exprs to plot > To: Adaikalavan Ramasamy <ramasamy@cancer.org.uk> > Cc: James MacDonald <jmacdon@med.umich.edu>, BioConductor mailing list > <bioconductor@stat.math.ethz.ch> > Message-ID: <1092578179.411f6b83556b9@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@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@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@stat.math.ethz.ch > > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > > > > > _______________________________________________ > > > Bioconductor mailing list > > > Bioconductor@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@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@babel.rockefeller.edu> > Cc: bioconductor@stat.math.ethz.ch > Message-ID: > <pine.gso.4.58.0408161108280.16337@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@nus.edu.sg> > Subject: [BioC] Selecting probe pairs for analysis > To: <bioconductor@stat.math.ethz.ch> > Message-ID: > <2181704595AEB44F9446B2558970B05810A27B@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@bbsrc.ac.uk> > Subject: RE: [BioC] Harsh results using limma! > To: "Gordon K Smyth" <smyth@wehi.edu.au>, "David K Pritchard" > <dpritch@u.washington.edu> > Cc: Anthony Rossini <rossini@u.washington.edu>, > bioconductor@stat.math.ethz.ch > Message-ID: > <8975119BCD0AC5419D61A9CF1A923E951746B3@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@wehi.edu.au] > Sent: 14 August 2004 01:07 > To: David K Pritchard > Cc: Anthony Rossini; bioconductor@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@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > > > > ------------------------------ > > _______________________________________________ > Bioconductor mailing list > Bioconductor@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
modified 14.8 years ago • written 14.8 years ago by Mayte Suarez-Farinas300
0
14.8 years ago by
United States
James W. MacDonald50k wrote:
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@babel.rockefeller.edu> 08/16/04 02:51PM >>> On Mon, 16 Aug 2004 bioconductor-request@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) > Send Bioconductor mailing list submissions to > bioconductor@stat.math.ethz.ch > > To subscribe or unsubscribe via the World Wide Web, visit > https://stat.ethz.ch/mailman/listinfo/bioconductor > or, via email, send a message with subject or body 'help' to > bioconductor-request@stat.math.ethz.ch > > You can reach the person managing the list at > bioconductor-owner@stat.math.ethz.ch > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of Bioconductor digest..." > > > Today's Topics: > > 1. Re: Labels in exprs to plot (mcolosim@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@brandeis.edu > Subject: Re: [BioC] Labels in exprs to plot > To: Adaikalavan Ramasamy <ramasamy@cancer.org.uk> > Cc: James MacDonald <jmacdon@med.umich.edu>, BioConductor mailing list > <bioconductor@stat.math.ethz.ch> > Message-ID: <1092578179.411f6b83556b9@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@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@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@stat.math.ethz.ch > > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > > > > > _______________________________________________ > > > Bioconductor mailing list > > > Bioconductor@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@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@babel.rockefeller.edu> > Cc: bioconductor@stat.math.ethz.ch > Message-ID: > <pine.gso.4.58.0408161108280.16337@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@nus.edu.sg> > Subject: [BioC] Selecting probe pairs for analysis > To: <bioconductor@stat.math.ethz.ch> > Message-ID: > <2181704595AEB44F9446B2558970B05810A27B@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@bbsrc.ac.uk> > Subject: RE: [BioC] Harsh results using limma! > To: "Gordon K Smyth" <smyth@wehi.edu.au>, "David K Pritchard" > <dpritch@u.washington.edu> > Cc: Anthony Rossini <rossini@u.washington.edu>, > bioconductor@stat.math.ethz.ch > Message-ID: > <8975119BCD0AC5419D61A9CF1A923E951746B3@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@wehi.edu.au] > Sent: 14 August 2004 01:07 > To: David K Pritchard > Cc: Anthony Rossini; bioconductor@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@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > > > > ------------------------------ > > _______________________________________________ > Bioconductor mailing list > Bioconductor@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 _______________________________________________ Bioconductor mailing list Bioconductor@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor
0
14.8 years ago by
Mayte Suarez-Farinas300 wrote:
On Mon, 16 Aug 2004, James MacDonald wrote: > 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's true. But if I download the compatible version, then GO package will be installed (release version) and then I can't use GOstats, because it is a devel version. Does that mean that I can not use annaffy and GOstats both with the devel packages, because they need diferent versions of GO? > That said, you should have got a much better error message than that. > Will you let me know exactly what the error message was? You do not have GO or KEGG installed or you have incompatible versions. Looking to see if they are available for download Would you like to install the above annotation packages? 1:Install annotation packages 2:Abort/Don't install anything Selection: Furthermore, Trying to go ahead with GOstats, I got an error related with Rgraphviz (See below). Can somebody give an advice what is going on since I installed R 2. It is posible that some incompatibilities with the Rhat version are causing this problems ? I am a litle bit desperate.... library(Rgraphviz) Loading required package: graph Loading required package: cluster Loading required package: Ruuid Creating a new generic function for "print" in "Ruuid" Loading required package: Biobase Loading required package: tools Welcome to Bioconductor Vignettes contain introductory material. To view, simply type: openVignette() For details on reading vignettes, see the openVignette help page. Error in dyn.load(x, as.logical(local), as.logical(now)) : unable to load shared library "/usr/lib/R/library/Rgraphviz/libs/Rgraphviz.so": libdotneato.so.0: cannot open shared object file: No such file or directory Error in library(Rgraphviz) : .First.lib failed > 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@babel.rockefeller.edu> 08/16/04 02:51PM > >>> > On Mon, 16 Aug 2004 bioconductor-request@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) > > > > > Send Bioconductor mailing list submissions to > > bioconductor@stat.math.ethz.ch > > > > To subscribe or unsubscribe via the World Wide Web, visit > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > or, via email, send a message with subject or body 'help' to > > bioconductor-request@stat.math.ethz.ch > > > > You can reach the person managing the list at > > bioconductor-owner@stat.math.ethz.ch > > > > When replying, please edit your Subject line so it is more specific > > than "Re: Contents of Bioconductor digest..." > > > > > > Today's Topics: > > > > 1. Re: Labels in exprs to plot (mcolosim@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@brandeis.edu > > Subject: Re: [BioC] Labels in exprs to plot > > To: Adaikalavan Ramasamy <ramasamy@cancer.org.uk> > > Cc: James MacDonald <jmacdon@med.umich.edu>, BioConductor mailing > list > > <bioconductor@stat.math.ethz.ch> > > Message-ID: <1092578179.411f6b83556b9@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@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@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@stat.math.ethz.ch > > > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > > > > > > > _______________________________________________ > > > > Bioconductor mailing list > > > > Bioconductor@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@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@babel.rockefeller.edu> > > Cc: bioconductor@stat.math.ethz.ch > > Message-ID: > > <pine.gso.4.58.0408161108280.16337@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@nus.edu.sg> > > Subject: [BioC] Selecting probe pairs for analysis > > To: <bioconductor@stat.math.ethz.ch> > > Message-ID: > > <2181704595AEB44F9446B2558970B05810A27B@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@bbsrc.ac.uk> > > Subject: RE: [BioC] Harsh results using limma! > > To: "Gordon K Smyth" <smyth@wehi.edu.au>, "David K Pritchard" > > <dpritch@u.washington.edu> > > Cc: Anthony Rossini <rossini@u.washington.edu>, > > bioconductor@stat.math.ethz.ch > > Message-ID: > > > <8975119BCD0AC5419D61A9CF1A923E951746B3@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@wehi.edu.au] > > Sent: 14 August 2004 01:07 > > To: David K Pritchard > > Cc: Anthony Rossini; bioconductor@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@stat.math.ethz.ch > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > > > > > > > ------------------------------ > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor@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