Hi Pan, Thank you very much for your helpful response. I really appreciate it! --Allegra _________________________________ Allegra A. Petti, Ph.D. The Genome Institute 4444 Forest Park Ave. St. Louis, MO 63108 apetti@genome.wustl.edu allegra.conbrio@post.harvard.edu On Feb 25, 2014, at 5:51 PM, Pan Du <dupan.mail@gmail.com> wrote: > Hi Allegra > > In order to get the MethyLumiM object, you must have the methylated and unmethylated probe intensities in your tab-delimited files. The beta-values were basically calculated from the methylated and unmethylated probe intensities. I recommend using .IDAT files if you have them because it includes all the information. > > As for your question about diffTh parameter, you can think it as the log2-foldChange threshold, which is similar as what expression analysis always do. > > There are couple of reasons why visually check the methylation data (without smoothing) is important. Here is just list a couple of them: > 1. The location of DMRs, because methylation regulation of transcript expression is very much chromosome location dependent. DMRs near TSS and CpG-islands are more likely to be involved in negative regulation of transcript expression. > 2. Check whether there is consistent behavior of nearby probes (if there are) because methylation pattern tends to be locally correlated. > 3. Globally check the methylation difference across samples. This may help the quality assessment of the samples, or identify whether there global-hyper or hypo methylation events. > > > Pan > > > On Tue, Feb 25, 2014 at 9:47 AM, Allegra A. Petti <apetti@genome.wustl.edu> wrote: > Hi Pan and Giovanni, > > I realized I have one more question. I'm currently using methyAnalysis starting from idat files, but I'd also like to be able to start from a tab-delimited matrix of beta values. I couldn't figure out how to work with (and annotate) beta values in any of the R packages that produce MethyLumiM objects. Do you have any procedures for this? > > Thank you again, > Allegra > _________________________________ > Allegra A. Petti, Ph.D. > > The Genome Institute > 4444 Forest Park Ave. > St. Louis, MO 63108 > apetti@genome.wustl.edu > allegra.conbrio@post.harvard.edu > > On Feb 25, 2014, at 11:23 AM, Pan Du <dupan.mail@gmail.com> wrote: > >> Hi Allegra and Giovanni >> >> The parameters of identifySigDMR need to be tuned based on your dataset. The default parameters was designed for the cell lines with clear difference between groups. For tissue samples, these parameters need to be less stringent, especially the diffTh parameter. >> Also, I strongly recommend visually checking your data. You can export the methylation data using export.methyGenoSet, and then visualize it in IGV. >> >> Pan >> >> >> >> On Tue, Feb 25, 2014 at 8:31 AM, Giovanni Calice <giovcalice@gmail.com> wrote: >> Hi Allegra, >> >> I'm having some difficulties in identifying Differentially Methylated Regions on my datasets too, >> both with bumphunter/minfi and with methyAnalysis package. >> >> With regard to bumphunter/minfi there was a clarification also to me >> in the previous massages of the BioC list. >> >> With regard to methyAnalysis I think you performed the smoothMethyData function separately >> It isn't wrong; in fact, if you had not smoothing separately, the next method of the pipeline detectDMR.slideWin >> performs it first and then proceeds with differential analysis. >> >> According to me you could try setting a different p.value.detection.th argument and try to see if you get results. >> >> Instead I get results in identifying Differentially Methylated Regions by methyAnalysis but >> none is significant (p.adjust = 1 or p.adjust > 0.7). >> >> So I'm wondering could the small number of samples (I've 8-12 samples for now) affect >> the significance of results and lead to a p.adjust not so good? >> >> Best, >> Giovanni >> >> >> Laboratory of Preclinical and Translational Research >> IRCCS - CROB Oncology Referral Center of Basilicata >> Rionero in Vulture - Italy >> >> *** Mail from ************************ >> Bioconductor Digest, Vol 132, Issue 26 >> ************************************** >> ************************************** >> Date: Mon, 24 Feb 2014 14:52:58 -0800 (PST) >> From: "Allegra Petti [guest]" <guest@bioconductor.org> >> To: bioconductor@r-project.org, apetti@genome.wustl.edu >> Cc: methyAnalysis Maintainer <dupan.mail@gmail.com> >> Subject: [BioC] methyAnalysis for DMR identification >> Message-ID: <20140224225258.99BC91468A7@mamba.fhcrc.org> >> >> >> >> Hello, >> >> I was wondering if anyone here has tried methyAnalysis for identifying Differentially Methylated Regions in Illumina 450k array data. It seems to have wonderful data-visualization options, but it found no differentially-methylated regions in my data (56 450k data sets), which was surprising. I've found very little documentation for this method, and I am wondering if I did something wrong - for example, should I have performed the smoothMethyData function separately, with a different window size? Relevant code snippets and output are provided below. I'd greatly appreciate any thoughts or suggestions. >> >> Thank you very much! >> Allegra >> __________________________________________________________ >> Code: >> >> library(lumi); >> library(methylumi); >> library(wateRmelon); >> library(methyAnalysis); >> library(IlluminaHumanMethylation450k.db); >> library(IlluminaHumanMethylation450kanno.ilmn12.hg19); # Note: had to be installed manually >> barcodes <- readLines("barcodes.txt"); # read barcodes from a text file >> classes <- readLines("IdatClasses.txt"); >> data.s <- methylumIDAT(barcodes); # read in idat files (methylumi function) >> *** additional data processing *** >> data.m <- as(data.s, "MethyLumiM"); # convert to MethyLumiM object >> data.g <- MethyLumiM2GenoSet(data.m, lib = "IlluminaHumanMethylation450k.db"); # convert MethyLumiM to MethyGenoSet >> dmrResult <- detectDMR.slideWin(data.g, sampleType=classes); # smooth data and find DMRs >> allDMRInfo <- identifySigDMR(dmrResult); >> ___________________________________________________________________ >> Standard Output: >> >> 65 probes were removed because of lack of chromosome location information! >> Smoothing Chromosome chr1 ... >> Smoothing Chromosome chr10 ... >> Smoothing Chromosome chr11 ... >> Smoothing Chromosome chr12 ... >> Smoothing Chromosome chr13 ... >> Smoothing Chromosome chr14 ... >> Smoothing Chromosome chr15 ... >> Smoothing Chromosome chr16 ... >> Smoothing Chromosome chr17 ... >> Smoothing Chromosome chr18 ... >> Smoothing Chromosome chr19 ... >> Smoothing Chromosome chr2 ... >> Smoothing Chromosome chr20 ... >> Smoothing Chromosome chr21 ... >> Smoothing Chromosome chr22 ... >> Smoothing Chromosome chr3 ... >> Smoothing Chromosome chr4 ... >> Smoothing Chromosome chr5 ... >> Smoothing Chromosome chr6 ... >> Smoothing Chromosome chr7 ... >> Smoothing Chromosome chr8 ... >> Smoothing Chromosome chr9 ... >> Smoothing Chromosome chrX ... >> Smoothing Chromosome chrY ... >> [1] "Done with detectDMR.slideWin\n" >> No significant CpG-sites were identified based on current criteria! >> >> >> >> -- output of sessionInfo(): >> >> R version 3.0.2 (2013-09-25) >> 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=C >> [5] LC_MONETARY=en_US.utf8 LC_MESSAGES=en_US.utf8 >> [7] LC_PAPER=en_US.utf8 LC_NAME=C >> [9] LC_ADDRESS=C LC_TELEPHONE=C >> [11] LC_MEASUREMENT=en_US.utf8 LC_IDENTIFICATION=C >> >> attached base packages: >> [1] grid parallel methods stats graphics grDevices utils >> [8] datasets base >> >> other attached packages: >> [1] MASS_7.3-29 >> [2] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.2.1 >> [3] minfi_1.9.11 >> [4] bumphunter_1.2.0 >> [5] locfit_1.5-9.1 >> [6] iterators_1.0.6 >> [7] foreach_1.4.1 >> [8] Biostrings_2.30.1 >> [9] lattice_0.20-24 >> [10] methyAnalysis_1.4.2 >> [11] GenomicRanges_1.14.4 >> [12] XVector_0.2.0 >> [13] IRanges_1.20.6 >> [14] wateRmelon_1.2.2 >> [15] ROC_1.38.0 >> [16] IlluminaHumanMethylation450k.db_2.0.7 >> [17] org.Hs.eg.db_2.10.1 >> [18] RSQLite_0.11.4 >> [19] DBI_0.2-7 >> [20] AnnotationDbi_1.24.0 >> [21] limma_3.18.4 >> [22] methylumi_2.8.0 >> [23] matrixStats_0.8.14 >> [24] ggplot2_0.9.3.1 >> [25] reshape2_1.2.2 >> [26] scales_0.2.3 >> [27] lumi_2.14.1 >> [28] Biobase_2.22.0 >> [29] BiocGenerics_0.8.0 >> >> loaded via a namespace (and not attached): >> [1] BSgenome_1.30.0 BiocInstaller_1.12.0 Formula_1.1-1 >> [4] GenomicFeatures_1.14.2 Gviz_1.6.0 Hmisc_3.14-0 >> [7] KernSmooth_2.23-10 Matrix_1.1-2 R.methodsS3_1.6.1 >> [10] RColorBrewer_1.0-5 RCurl_1.95-4.1 Rsamtools_1.14.2 >> [13] XML_3.98-1.1 affy_1.40.0 affyio_1.30.0 >> [16] annotate_1.40.0 beanplot_1.1 biomaRt_2.18.0 >> [19] biovizBase_1.10.7 bitops_1.0-6 cluster_1.14.4 >> [22] codetools_0.2-8 colorspace_1.2-4 dichromat_2.0-0 >> [25] digest_0.6.4 doRNG_1.5.5 genefilter_1.44.0 >> [28] genoset_1.14.0 gtable_0.1.2 illuminaio_0.2.0 >> [31] itertools_0.1-1 labeling_0.2 latticeExtra_0.6-26 >> [34] mclust_4.2 mgcv_1.7-28 multtest_2.18.0 >> [37] munsell_0.4.2 nleqslv_2.1 nlme_3.1-113 >> [40] nor1mix_1.1-4 pkgmaker_0.17.4 plyr_1.8 >> 43] preprocessCore_1.24.0 proto_0.3-10 registry_0.2 >> [46] reshape_0.8.4 rngtools_1.2.3 rtracklayer_1.22.3 >> [49] siggenes_1.36.0 splines_3.0.2 stats4_3.0.2 >> [52] stringr_0.6.2 survival_2.37-7 tools_3.0.2 >> [55] xtable_1.7-1 zlibbioc_1.8.0 >> >> ************************************** >> ************************************** >> > > > ____ This email message is a private communication. 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