[BioC] methyAnalysis - changes to GenoSet
Kasper Daniel Hansen
kasperdanielhansen at gmail.com
Tue Mar 12 14:57:52 CET 2013
Lavina,
I don't really have time to really look into all of this, but note the
function mapToGenome which associates a MethylSet with genomic
coordinates. So you can do
gmSet = mapToGenome(mSet)
and now you have
granges(gmSet) # locations
getMeth(gmSet) # methylation channel
etc. Note that the transformation is non-invertible because a few
CpGs have no associated genomic locations and they are dropped (well,
you can use drop=FALSE).
Kasper
On Mon, Mar 11, 2013 at 5:13 PM, Lavinia Gordon
<lavinia.gordon at mcri.edu.au> wrote:
> Argh!, thanks Martin, sorry about that.
>
> So in that case I think I've worked out a (rather ugly) way to force minfi data into a MethyGenoSet object for use within the package methyAnalysis:
>
> Library(minfi)
> # mset is minfi object, e.g. used in dmpFinder code
> myexprs <- getM(mSet)
> mymeth <- getMeth(mSet)
> myunmeth <- getUnmeth(mSet)
>
> # using Tim Triches helpful code, thanks!
> library(IlluminaHumanMethylation450kprobe)
> data(IlluminaHumanMethylation450kprobe)
> library(GenomicRanges)
> chs = levels(IlluminaHumanMethylation450kprobe$chr)
> names(chs) = paste('chr',chs,sep='')
> CpGs.450k = with(IlluminaHumanMethylation450kprobe,
> GRanges(paste('chr',chr,sep=''),
> IRanges(start=site, width=2, names=Probe_ID),
> strand=strand))
>
> CpGs.450k.df <- as.data.frame(CpGs.450k)
>
> mylocData <- RangedData(ranges=IRanges(start=CpGs.450k.df$start, end=CpGs.450k.df$end, names=row.names(CpGs.450k.df)), space=CpGs.450k.df$seqnames)
>
> mymethy.obj <- new("MethyGenoSet", locData=mylocData, assayData=assayDataNew(exprs=new("matrix"), methylated=new("matrix"), unmethylated=new("matrix"), detection=new("matrix")))
>
> # make sure the CpG ids in mylocData agree with what is in CpGs.450k.df
>
> exprs(mymethy.obj) <- myexprs
> methylated(mymethy.obj) <- mymeth
> unmethylated(mymethy.obj) <- myunmeth
> # had to add this afterwards
>
> methyGenoSet.sm <- smoothMethyData(mymethy.obj, winSize = 250)
>
> I haven't run through all of the MethyAnalysis functions so the MethyGenoSet object may need more information added to it, in other slots, for it to work properly.
>
>
> Lavinia Gordon
> Senior Research Officer
> Quantitative Sciences Core, Bioinformatics
>
> Murdoch Childrens Research Institute
> The Royal Children's Hospital
> Flemington Road Parkville Victoria 3052 Australia
> T 03 8341 6221
> www.mcri.edu.au
>
>
> -----Original Message-----
> From: Martin Morgan [mailto:mtmorgan at fhcrc.org]
> Sent: Saturday, 9 March 2013 1:49 AM
> To: Lavinia Gordon
> Cc: bioconductor at r-project.org; dupan at gmal.com
> Subject: Re: [BioC] methyAnalysis - changes to GenoSet
>
> On 03/07/2013 08:23 PM, Lavinia Gordon wrote:
>> Dear Bioc users,
>> I have just tried the example code supplied with the package methyAnalysis and am getting an error:
>>
>>> ###################################################
>>> ### code chunk number 3: Slide-window smoothing of the DNA
>>> methylation data ###################################################
>>> methyGenoSet.sm <- smoothMethyData(exampleMethyGenoSet, winSize =
>>> 250)
>> Smoothing Chromosome 21 ...
>>
>> Note: Method with signature âGenoSet#character#ANY#ANYâ chosen for
>> function â[â, target signature âMethyGenoSet#character#character#missingâ.
>> "MethyGenoSet#ANY#ANY#ANY" would also be valid Warning message:
>> The ranges method on a GenoSet is depricated. Please use space(locData(x)) or seqnames(locData(x)) as appropriate for RangedData or GRanges.
>>> methyGenoset.sm
>> Error: object 'methyGenoset.sm' not found
>
> typo -- methyGenoSet.sm
>
> Martin
>
>>> sessionInfo()
>> R version 2.15.2 (2012-10-26)
>> Platform: x86_64-redhat-linux-gnu (64-bit)
>>
>> locale:
>> [1] LC_CTYPE=en_US.UTF-8
>> [2] LC_NUMERIC=C
>> [3] LC_TIME=en_US.UTF-8
>> [4] LC_COLLATE=en_US.UTF-8
>> [5] LC_MONETARY=en_US.UTF-8
>> [6] LC_MESSAGES=en_US.UTF-8
>> [7] LC_PAPER=C
>> [8] LC_NAME=C
>> [9] LC_ADDRESS=C
>> [10] LC_TELEPHONE=C
>> [11] LC_MEASUREMENT=en_US.UTF-8
>> [12] LC_IDENTIFICATION=C
>>
>> attached base packages:
>> [1] grid stats graphics grDevices
>> [5] utils datasets methods base
>>
>> other attached packages:
>> [1] methyAnalysis_1.0.0 org.Hs.eg.db_2.8.0
>> [3] RSQLite_0.11.2 DBI_0.2-5
>> [5] AnnotationDbi_1.20.5 Biobase_2.18.0
>> [7] IRanges_1.16.6 BiocGenerics_0.4.0
>>
>> loaded via a namespace (and not attached):
>> [1] affy_1.36.1 affyio_1.26.0
>> [3] annotate_1.36.0 BiocInstaller_1.8.3
>> [5] biomaRt_2.14.0 Biostrings_2.26.3
>> [7] biovizBase_1.6.2 bitops_1.0-5
>> [9] BSgenome_1.26.1 cluster_1.14.3
>> [11] colorspace_1.2-1 dichromat_2.0-0
>> [13] genefilter_1.40.0 GenomicFeatures_1.10.2
>> [15] GenomicRanges_1.10.7 genoset_1.10.1
>> [17] Gviz_1.2.1 Hmisc_3.10-1
>> [19] KernSmooth_2.23-9 labeling_0.1
>> [21] lattice_0.20-13 lumi_2.10.0
>> [23] MASS_7.3-23 Matrix_1.0-11
>> [25] methylumi_2.4.0 mgcv_1.7-22
>> [27] munsell_0.4 nleqslv_2.0
>> [29] nlme_3.1-108 parallel_2.15.2
>> [31] plyr_1.8 preprocessCore_1.20.0
>> [33] RColorBrewer_1.0-5 RCurl_1.95-4.1
>> [35] Rsamtools_1.10.2 rtracklayer_1.18.2
>> [37] scales_0.2.3 splines_2.15.2
>> [39] stats4_2.15.2 stringr_0.6.2
>> [41] survival_2.37-4 tools_2.15.2
>> [43] XML_3.95-0.2 xtable_1.7-1
>> [45] zlibbioc_1.4.0
>>>
>>
>> With regards,
>>
>> Lavinia Gordon
>> Senior Research Officer
>> Quantitative Sciences Core, Bioinformatics
>>
>> Murdoch Childrens Research Institute
>> The Royal Children's Hospital
>> Flemington Road Parkville Victoria 3052 Australia T 03 8341 6221
>> www.mcri.edu.au<http://www.mcri.edu.au/>
>>
>>
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