Hi Wolfgang, > the quickest way to figure out what's going on may be to download and > install an older version of R (as good as you can remember what it was), > run > biocLite("limma") > and then your script and see exactly at what point your results start to > differ. Good advice - thanks! I tried R 2.6.2 / limma_2.12.0 and was able to nail down the difference to lmFit() which looks like it has undergone a major re-write somewhere between 2.12.0 and 2.14.2. So now I'll spend some time studying the source code - maybe I can figure out what exactly causes the change. If anyone else has experienced a similar effect I'd be interested to hear about it... > Is "targets" the same? Yes, it is. cu Philipp -- Dr. Philipp Pagel Lehrstuhl f?r Genomorientierte Bioinformatik Technische Universit?t M?nchen Wissenschaftszentrum Weihenstephan 85350 Freising, Germany http://mips.gsf.de/staff/pagel

Additional to the points mentioned, you can check the log of changes to limma, e.g. changeLog(n=200) To determine what functions may have changed in the time period. When comparing your analysis from a reproducible research point of view if you have any old objects stored (saved R session or objects dumped to disk) you can using the functions "identical" and "all.equal" to examine where analysis might deviate when trying to reproduce it. Marcus On 24/7/08 10:41 PM, "Wolfgang Huber" <huber at="" ebi.ac.uk=""> wrote: > Hi Philipp > > the quickest way to figure out what's going on may be to download and > install an older version of R (as good as you can remember what it was), > run > biocLite("limma") > and then your script and see exactly at what point your results start to > differ. > > Is "targets" the same? (esp if you imported it from text file via > read.table or the like, there can be surprises e.g. to do with > locales/encoding, special characters...) > > Best wishes > Wolfgang > > ------------------------------------------------------------------ > Wolfgang Huber EBI/EMBL Cambridge UK http://www.ebi.ac.uk/huber > > > 24/07/2008 11:23 Philipp Pagel scripsit >> Dear list, >> >> About 3 months ago I analyzed a simple two-color array experiment and got >> results that looked quite reasonable and biologically sound. For some reason >> I >> wanted to repeat the analysis and add a few plots that I had not included >> before. >> >> When I got VERY different results in my toptable, I assumed I must have >> changed something in my approach so I simply ran my original analysis >> script again and found I was unable to reproduce the original toptable. >> I have spent quite some time trying to debug the problem and have to say >> that I am stuck. I have the original data files and the original >> R-script. The normalization is 100% reproducible - i.e. the normalized >> MALists seem to be identical. Yet when searching for differential >> expression I get totally different results. >> >> The only difference between the two runs lies in updates to R and limma in >> the meantime. Unfortunately, I did not record which version of R, limma etc. >> I >> had used, originally. My current environment is this: >> >>> sessionInfo() >> R version 2.7.1 (2008-06-23) >> x86_64-pc-linux-gnu >> >> locale: >> LC_CTYPE=en_US.utf8;LC_NUMERIC=C;LC_TIME=en_US.utf8;LC_COLLATE=en_U S.utf8;LC_ >> MONETARY=C;LC_MESSAGES=en_US.utf8;LC_PAPER=en_US.utf8;LC_NAME=C;LC_ ADDRESS=C; >> LC_TELEPHONE=C;LC_MEASUREMENT=en_US.utf8;LC_IDENTIFICATION=C >> >> attached base packages: >> [1] splines stats graphics utils datasets grDevices methods >> base >> >> other attached packages: >> [1] statmod_1.3.6 MASS_7.2-42 xtable_1.5-2 limma_2.14.2 >> lattice_0.17-10 >> [6] cairoDevice_2.8 >> >> loaded via a namespace (and not attached): >> [1] grid_2.7.1 tools_2.7.1 >> >> >> My search for differential expression seems pretty standard to me: >> >> MA$design <- modelMatrix(targets, ref="control") >> # flag out controls etc. >> MA$weights[MA$genes$Status != 'miRNA', ] = 0.0 >> # sort spots by ID to put replicates next to each other >> MA2 <- MA[order(MA$genes$ID), ] >> >> dupfit <- duplicateCorrelation(MA2, ndups=4) >> fit <- lmFit(MA2, ndups=4, correlation=dupfit$consensus) >> fit <- eBayes(fit) >> tt <- topTable(fit, number=100) >> >> I have siftet through the changelog of limma hoping to find a hint about >> some changed default or behaviour in lmFit or eBayes but saw nothing >> that seemed to expain my problem. >> >> Any hints apprechiated. >> >> cu >> Philipp >> > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor The contents of this e-mail are privileged and/or confid...{{dropped:5}}