Determining the logistic model of MeDIP enrichment in respect to the expected DNA methylation level

Description

Probe-level MeDIP weighted enrichment is compared to the expected DNA methytlation level. The former is determined applying MeDIP protocol to a fully methylated DNA. The latter is determined as the count of CpGs for each probe. This is assumed to be the methylation level of each probe in a fully methylated sample.

Usage

MEDME(data, sample, CGcountThr = 1, figName = NULL)

Arguments

Details

The model should be applied on calibration data containing MeDIP enrichment of fully methylated DNA, most likely artificially generated (see references). Nevertheless, in case chromosome or genome-wide human tiling arrays are used a regular sample could be used too. In fact, human genomic DNA is known to be hyper-methylated but in the promoter regions. Of course the performance of the method is expected to be somehow affected by this approximation.

Value

The logistic model as returned from the multdrc function from the drc R library

References

http://genome.cshlp.org/cgi/content/abstract/gr.080721.108v1

See Also

smooth, CGcount

Examples

data(testMEDMEset)
## just an example with the first 1000 probes
testMEDMEset = smooth(data = testMEDMEset[1:1000, ])
library(BSgenome.Hsapiens.UCSC.hg18)
testMEDMEset = CGcount(data = testMEDMEset)
MEDMEmodel = MEDME(data = testMEDMEset, sample = 1, CGcountThr = 1, figName = NULL)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(MEDME)

Attaching package: 'MEDME'

The following object is masked from 'package:stats':

    smooth

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/MEDME/MEDME.Rd_%03d_medium.png", width=480, height=480)
> ### Name: MEDME
> ### Title: Determining the logistic model of MeDIP enrichment in respect to
> ###   the expected DNA methylation level
> ### Aliases: MEDME
> 
> ### ** Examples
> 
> data(testMEDMEset)
> ## just an example with the first 1000 probes
> testMEDMEset = smooth(data = testMEDMEset[1:1000, ])
chrX  
> library(BSgenome.Hsapiens.UCSC.hg18)
Loading required package: BSgenome
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: GenomicRanges

Attaching package: 'GenomicRanges'

The following object is masked from 'package:MEDME':

    pos

Loading required package: Biostrings
Loading required package: XVector
Loading required package: rtracklayer
> testMEDMEset = CGcount(data = testMEDMEset)
chrX  
> MEDMEmodel = MEDME(data = testMEDMEset, sample = 1, CGcountThr = 1, figName = NULL)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>