Thanks James On Wed, Jun 19, 2013 at 10:04 PM, James W. MacDonald <jmacdon@uw.edu> wrote: > Hi Srikanth, > > > On 6/19/2013 12:02 PM, Srinivas Srikanth Manda wrote: > >> Hi James, >> >> Thanks for the response. I am a bit new to this kind of analysis. When I >> use and dmpFinder on three different groups Vs Control and I export the >> results as data.frame, I only get columns corresponding to probeid, >> intercept and F, p and q values but not the group name. I want to have data >> in the form how the plotCpG function works (to see a difference in >> methylation across the three groups). How can I achieve this? >> >> My initial goal: >> Find significantly different probes across three different grades of >> cancer samples. >> > > You should note that dmpFinder() does just what you are asking, finding > probes that are significantly different across one or more of your sample > types. However, it is fitting a particular model that you might not like, > and doing an F-test for ANY difference. You may want to do more directed > analyses. > > To that end, note that dmpFinder() is just a nice wrapper for doing the > analysis, and you don't have to use it. You can just as easily use limma to > do the univariate analyses. I won't get into details here, as the limma > User's Guide has any number of examples you could emulate (and I have > already given you a head start below). > > > > Map the probes to gene regions (like promotes, gene body, UTR, etc) >> > > See ?mapToGenome for mapping the probes to genomic coordinates. You will > then need to map significantly differentially methylated regions to genomic > features and maybe make some nice plots. This isn't super difficult, but it > does require a fair amount of base knowledge of a bunch of packages. > > You should probably peruse the following: > > http://bioconductor.org/help/**course- materials/2013/**SeattleFeb2013/** > IntermediateSequenceAnalysis20**13.pdf<http: bioconductor.org="" help="" course-="" materials="" 2013="" seattlefeb2013="" intermediatesequenceanalysis2013.pdf=""> > > which covers a lot of what you want to do (differential methylation isn't > particularly different in a lot of respects from e.g., RNA-seq or whatever. > You have a genomic position that you think is 'interesting' and you might > want to know if there is any known <something> nearby. The only difference > is why you think it is interesting.). > > There is no substitute for just trying to do something and doing your best > to figure out why things aren't working. Search the list, read the > vignettes, read the help pages. > > Best, > > Jim > > > > Find enriched regions. >> >> Any help for the above tasks is appreciated. >> >> >> Thanks >> Srikanth >> >> >> >> >> >> On Wed, Jun 19, 2013 at 7:06 PM, James W. MacDonald <jmacdon@uw.edu<mailto:>> jmacdon@uw.edu>> wrote: >> >> Hi Srinivas, >> >> >> On 6/19/2013 5:21 AM, Srinivas Srikanth Manda wrote: >> >> Hello Members, >> >> I am using Minfi package to analyze 450k data. I have three >> different >> groups of samples and one common control. I did the >> normalization and other >> steps according to manual, but stuck at the differential >> methylation >> positions. When I use: >> >> M<- getM(MSet.norm, type = "beta", betaThreshold = 0.001) >> dmp1<- dmpFinder(M, pheno=pd$Sample_Group, type="categorical") >> >> I want to get a table with probes and corresponding values in >> each group. >> the data.frame dmp1 does not tell me which group has what >> value? How can I >> do that? >> >> >> It's not clear what you mean by 'probes and corresponding values >> in each group'. I am not sure what a corresponding value is. >> >> If I make the assumption that you want the coefficients from the >> model fit, then you can do >> >> design <- model.matrix(~pd$Sample_Group) >> fit <- lmFit(M, design) >> >> and then fit$coefficients has the coefficients. Or perhaps you >> just want the methylation values? The M-values are in your M >> matrix, and if you prefer betas, you can use getBeta(MSet.norm). >> >> You might also just want the mean of each group. In which case it >> would be easier to do >> >> design <- model.matrix(~0+pd$Sample_**Group) >> fit <- lmFit(M, design) >> >> and then fit$coefficients will contain the mean value for each >> group, by probe. >> >> Best, >> >> Jim >> >> >> >> >> >> sessionInfo() >> R version 2.15.2 (2012-10-26) >> Platform: x86_64-unknown-linux-gnu (64-bit) >> >> locale: >> [1] LC_CTYPE=en_US.utf8 LC_NUMERIC=C >> [3] LC_TIME=en_US.utf8 LC_COLLATE=en_US.utf8 >> [5] LC_MONETARY=en_US.utf8 LC_MESSAGES=en_US.utf8 >> [7] LC_PAPER=C LC_NAME=C >> [9] LC_ADDRESS=C LC_TELEPHONE=C >> [11] LC_MEASUREMENT=en_US.utf8 LC_IDENTIFICATION=C >> >> attached base packages: >> [1] stats graphics grDevices utils datasets methods >> base >> >> other attached packages: >> [1] minfiData_0.3.1 >> [2] IlluminaHumanMethylation450kma**nifest_0.4.0 >> [3] minfi_1.4.0 >> [4] Biostrings_2.26.3 >> [5] GenomicRanges_1.10.7 >> [6] IRanges_1.16.6 >> [7] reshape_0.8.4 >> [8] plyr_1.8 >> [9] lattice_0.20-15 >> [10] Biobase_2.18.0 >> [11] BiocGenerics_0.4.0 >> >> loaded via a namespace (and not attached): >> [1] affyio_1.26.0 annotate_1.36.0 >> AnnotationDbi_1.20.7 >> [4] beanplot_1.1 BiocInstaller_1.8.3 bit_1.1-10 >> [7] codetools_0.2-8 crlmm_1.16.9 DBI_0.2-7 >> [10] ellipse_0.3-8 ff_2.2-11 foreach_1.4.0 >> [13] genefilter_1.40.0 grid_2.15.2 iterators_1.0.6 >> [16] limma_3.14.4 MASS_7.3-23 Matrix_1.0-12 >> [19] matrixStats_0.8.1 mclust_4.1 multtest_2.14.0 >> [22] mvtnorm_0.9-9994 nor1mix_1.1-4 >> oligoClasses_1.20.0 >> [25] parallel_2.15.2 preprocessCore_1.20.0 >> RColorBrewer_1.0-5 >> [28] RcppEigen_0.3.1.2.1 R.methodsS3_1.4.2 RSQLite_0.11.3 >> [31] siggenes_1.32.0 splines_2.15.2 stats4_2.15.2 >> [34] survival_2.37-4 tools_2.15.2 XML_3.96-1.1 >> [37] xtable_1.7-1 zlibbioc_1.4.0 >> >> >> >> Regards, >> Srikanth >> >> >> >> -- James W. MacDonald, M.S. >> Biostatistician >> University of Washington >> Environmental and Occupational Health Sciences >> 4225 Roosevelt Way NE, # 100 >> Seattle WA 98105-6099 >> >> >> >> >> -- >> Srinivas Srikanth Manda >> Ph.D. Student >> Institute of Bioinformatics >> Discoverer, 7th Floor, >> International Technology Park, >> Bangalore, India >> Mob:+919019114878 >> > > -- > James W. MacDonald, M.S. > Biostatistician > University of Washington > Environmental and Occupational Health Sciences > 4225 Roosevelt Way NE, # 100 > Seattle WA 98105-6099 > > -- Srinivas Srikanth Manda Ph.D. Student Institute of Bioinformatics Discoverer, 7th Floor, International Technology Park, Bangalore, India Mob:+919019114878 [[alternative HTML version deleted]]