There are two ways to get version numbers. For a single package: packageDescription("thepackage")$Version For all loaded packages: sessionInfo() Best, Jim Hongqing Li wrote: > Hi James, > > The whole story is that my hard drive was broken and I only have backup > files left in csv files and scripts used to generate those files. But I did > not > put down the version number of R.gcrma,limma I used, neither could I > restore the old R working environment, so I have no way to check the > package. > versions. I think it is very important to add a line or two on my datafiles > with > the version numbers of those packages from now on. > > What I did was simple: > > data<-ReadAffy() > eset<-gcrma(data) > exprs2excel(eset,'somefilename') > > When I compare the results from this script and the old one, I can not find > perfect > agreement. I installed bioconductor 2.0 last week, so the package should be > Affy > 1.14.2, gcrma 2.8.1, limma 2.10.5. as I found out in > http://www.bioconductor.org/packages/2.0/bioc/ > > BTW, how do you print out the version number of a package in R? I googled > but > could not find the answer. > > Thanks, > hongqing > > On 8/23/07, James W. MacDonald <jmacdon at="" med.umich.edu=""> wrote: >> Hi Hongqing, >> >> Hongqing Li wrote: >>> Hi, >>> >>> I have been using limma and gcrma for microarray analysis for a while. >>> recently I have to reinstall my system so I update my R from 2.4 to >> 2.5.1 >>> I don't know the version of limma,gcrma packages, but they were >> installed >>> as I install my old R 2.4. However, when I run the same script to >> analyze >>> the same file I did last year, I got different results for both gcrma >> and >>> limma. >>> Although the difference is not dramatic. I plotted the scatter plot for >>> gcrma >>> summarized expression data from old analysis and new analysis, it >> supposed >>> to be a straight line, but on my plot the low expression values form a >>> slightly >>> inflated data cloud around the diagonal line. For limma, only about 10 >>> really >>> large or really small t statistics are strongly different from the old >>> analysis. >>> Is this a known issue for using different version of packages ? >> Well, yes. Probably. Since you don't know what versions you were using, >> nor the versions you are now using, it is impossible to say for sure >> (plus you don't say how you are analyzing the data, nor do you give a >> small reproducible example that we could try ourselves). >> >> However, Both limma and gcrma tend to change (limma perhaps more than >> gcrma) over time, as the maintainers improve both the implementation and >> underlying statistical methodology, so it is not unlikely that you would >> get 'not dramatic' changes in results with different versions. >> >> Best, >> >> Jim >>> Thanks, >>> Hongqing >>> >>> [[alternative HTML version deleted]] >>> >>> _______________________________________________ >>> 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 >> >> -- >> James W. MacDonald, M.S. >> Biostatistician >> Affymetrix and cDNA Microarray Core >> University of Michigan Cancer Center >> 1500 E. Medical Center Drive >> 7410 CCGC >> Ann Arbor MI 48109 >> 734-647-5623 >> > > [[alternative HTML version deleted]] > > _______________________________________________ > 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 -- James W. MacDonald, M.S. Biostatistician Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109 734-647-5623